Concluding Remarks on Business Strategy: Insights & Implications from a Textual Analysis ()
1. Introduction
Business strategy plays a role in organizational success (Abdulwase et al., 2020; Babalola & Nwanzu, 2020), influencing how firms compete, adapt, and perform in evolving markets (Climent & Haftor, 2021; Marcão et al., 2026). While extensive research examines strategic frameworks (Dion & Evans, 2024; Fuertes et al., 2020; Kayani et al., 2025), competitive positioning (Adenuga et al., 2025; Onalaja & Otokiti, 2021; Saqib, 2021), and performance outcomes (Bıçakcıoğlu-Peynirci & Tanyeri, 2022; Jukka, 2023; Lestari et al., 2020), less attention has been given to how scholars themselves summarize and frame strategy in the conclusion sections of their published work. The conclusion section is where authors reflect on what their research means (Carter et al., 2020; Gray, 2022; Zhou & Jiang, 2023). Because conclusions represent the most direct expression of a study’s limitations, implications, and future research directions, they offer a unique point of assessment into how those engaged in the field of business strategy have defined its priorities and contributions.
This study used a thematic, textual analysis of the conclusion sections from the 50 most relevant peer-reviewed academic articles identified through a structured database search to examine recurring themes, language patterns, and overall sentiment within business strategy research. By analyzing word frequency, common two-word phrases (i.e., bigrams), and sentiment, the study provided a basis to understand how business strategy literature is framed, which themes emerged in concluding discussions, and how researchers have interpreted competitive advantage, organizational performance, and future strategic direction. The null hypothesis for this study (H1) was that the average mean sentiment score for the conclusion section of academic journal articles covering business strategy is less than or equal to 0. Focusing on the conclusion section of business strategy research is important because that is where a study’s key insights and implications are brough together and most clearly articulated. The language used in those sections not only reflect individual research findings but also signals broader disciplinary norms and evolving strategic priorities. By examining patterns across an author-created corpus articles rather than a single study, this research identified a constellation of words, phrases, and sentiments associated with business strategy research suggesting that it consistently emphasized advancement, competitive positioning, digital transformation, and scholarly contribution as dominant themes.
This study conforms to a standard research paper containing a: a) survey of literature (Section 2), b) method section (Section 3), c) presentation of results (Section 4), and d) conclusion (Section 5). Ultimately, this study contributed to an understanding of how the discipline of business strategy presents itself. Through a mixed-method textual approach that combines qualitative thematic interpretation with quantitative rhetorical analysis, this research revealed not only what scholars focused on business strategy study, but how they have communicated its value. By identifying dominant themes and prevailing semantic tone within conclusion sections, this research offers a clearer picture of how modern business strategy research has framed its contributions to both academic audiences and business experts.
2. Survey of Literature
Business strategy is a central component of organizational success, guiding how firms create competitive advantage, allocate resources, and respond to evolving market conditions (Agustian et al., 2023; Collins, 2022; Farida & Setiawan, 2022). The study of business strategy has evolved beyond traditional frameworks to incorporate new analytical approaches, including the growing use of textual analysis to understand better strategic communication and decision-making (Banker et al., 2024; Dvořák et al., 2022; Jackson & Heath, 2023a). A significant body of research now examines not only strategic outcomes, but also the language and narratives used to frame strategic thinking in academic and professional contexts (Georgakalou et al., 2023; Shaik & Dhir, 2020). Textual analysis has emerged as a useful tool for identifying patterns, themes, and sentiment within strategic dialogue, offering additional insight into how strategy is communicated and formulated (Clarkson et al., 2020; Clapham et al., 2023). This paper positions itself at the intersection of business strategy, textual analysis, and the application of textual methods to strategic research. The following survey of literature reviews existing scholarship across these the areas of business strategy (Section 2.1), textual analysis (Section 2.2), and the application of textual analysis to business (Section 2.3). The reviewed literature highlights that textual analysis has been used to examine business strategy and that those approaches have contributed to a broader understanding of strategy within organizational contexts.
2.1. Research on Business Strategy
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 Chandler (1962) established that organizational structure follows strategic decisions, while Ansoff (1965) 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 (Hoskisson et al., 1999). 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. Porter (1980) highlighted that firms benefit from analyzing industry structure and competitive forces to position themselves more effectively, while subsequently Porter (1996) argued that strategy involves choosing a unique and valuable position that differentiates a firm from its competitors. Barney (1991) 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. Mintzberg (1987) 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 Teece et al. (1997) 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. Rumelt et al. (1994) 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. Mintzberg and Waters (1985) 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. Hoskisson et al. (1999) 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.
2.2. Research on Textual Analysis
Textual analysis has become an increasingly important methodological approach across a wide range of academic disciplines, including sociology (Baden et al., 2022; Nelson, 2020), organizational studies (Jackson, 2025; Jackson et al., 2022), political science (Bestvater & Monroe, 2023; Jackson & Heath, 2023b), and philosophy (Blau, 2024; Jackson & Heath, 2024). 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 (Grimmer & Stewart, 2013; Gentzkow et. al., 2019).
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. Michel et al (2011) 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, Evans and Aceves (2016) 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. Jurafsky and Martine (2023) 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 (Diermeier et al., 2012). 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. Pang and Lee (2008) 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 (Haselmayer, 2017). 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 (Loughran & McDonald, 2011). 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 (Prakash & Aloysius, 2021; Thangavel & Lourdusamy, 2023). 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. Liu (2012) 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 (Bird et al., 2009; Fox et al., 2023). 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 (Evans & Aceves, 2016). In political science, scholars use textual data from speeches, debates, and social media to analyze political messaging public opinion, and electoral dynamic (Grimmer & Stewart, 2013). 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 (Loughran & McDonald, 2011). Textual analysis techniques have been applied extensively in the field of communication as well (Ayanwale, 2021; Sabrina et al., 2023; Utari & Pramana, 2025).
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.
2.3. Applications of Textual Analysis to Business Strategy
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 (Jackson & Heath, 2023a; Loughran & McDonald, 2011; Li, 2010).
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. Loughran and McDonald (2011) developed domain-specific dictionaries to improve accuracy of sentiment analysis in financial texts, highlighting that traditional sentiment measures may misclassify business terminology. Similarly, Li (2010) 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 (Chin & Fan, 2023; Feng et al., 2023). Research shows the tone and linguistic patterns used by executives during earnings calls can influence investor perceptions and market reactions. For example, Davis et al. (2012) investigated the tones of earnings press releases and found positive or negative language can impact significantly stock returns. In addition, Huang et al. (2014) 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, Kaplan and Vakili (2015) used text analysis to show technological innovation and firm strategy, demonstrating language reflects shifts in organizational focus and industry trends. In addition, McKenny et al., (2018) 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 (Pollock et al., 2012). 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 McKenny et al. (2018) 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).
3. Method
Effective business strategy is an essential part of organizational success (Al Najjar & Quandeel, 2025; Alvi et al., 2020; Edwards, 2021). The research method used in a study will shape and constrain the types of insights one is able to achieve (Chasokela, 2025; Glibert, 2024). As such, selection of the research method is consequential. This study used a mixed-methods approach (Dawadi et al., 2021; Kajamaa et al., 2020; Shan, 2022), with a primary focus on a qualitative, thematic textual analysis method (Berger et al., 2020; Roberts, 2020; Van Atteveldt et al., 2021) to examine how academic researchers have summarized and interpreted business strategy in scholarly articles. It specifically analyzed the conclusion sections of selected peer-reviewed, academic articles referencing business strategy. A thematic textual analysis approach (Dawadi, 2020; Peel, 2020; Squires, 2023) was selected because it allows for an in-depth interpretation of meaning and language across multiple sources. This approach is appropriate for examining the conclusion sections of published, academic research papers because this is where authors tend to have their most direct statements about recommendations, limitations, implications, and future research directions (Goulart, 2024; Liu & Ziao, 2022; Zhou & Jiang, 2023). The goal of this research design was to identify repeated themes, words, patterns, sentiments, and dominant narratives in the literature behind business strategy and to understand how strategy has been framed in academic discussions with an intent to identify insights that might prove useful to business practitioners responsible for crafting and enacting business strategy.
The dataset for this study consisted of the top 50 most relevant peer-reviewed academic articles associated with the search phrase business strategy. Articles were collected from Academic Search Complete, on 21 January 2026, using a structured filtered process. Filters for peer reviewed, pdf format, and written in English were applied. The keyword phrase business strategy was entered. The results were sorted by relevance to identify the top 50 most relevant academic articles dealing with business strategy. Within Academic Search Complete, relevance is prioritized based on a scoring algorithm that considers publication date, publication type, peer review status, and document length, in which newly published records are prioritized over older records and peer-reviewed records are prioritized over non-peer-reviewed ones (EBSCO Connect, 2025). Whereas the selection to focus on 50 articles is ultimately arbitrary, it is consistent with other published research (Troyer et al., 2024) and exceeds the minimum of 30 required to achieve approximate normality via the central limit theorem (Kwak & Kim, 2017), and is therefore considered to be a useful starting point for the application of this approach to this area of inquiry. The top 50 articles that followed the adopted filters were then selected for this analysis, and formed the basis for the author-created corpus. This selection process utilized is illustrated in Figure 1.
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Figure 1. Data filtering process for article identification.
As indicated in Figure 1, a funnel-style selection process was used to identify the final collection of 50 academic articles used to form the author-created corpus. The selection process started with logging into Academic Search Complete. Following database selection, the search protocol was narrowed by selecting peer-reviewed articles, with PDF articles, written in the English language. The results were sorted by topical relevance, and the top 50 articles were selected and compiled to form the author-created corpus. A list of the 50 articles identified and selected for study are included in Appendix A. Based on the selection protocol, the resulting corpus included 50 academic journal articles, each containing an identifiable conclusion section. These articles represent a range of a variety of academic perspectives, but share a common connection around business strategy as a main topic.
The conclusions were extracted from the PDF articles and organized into an author-created Excel dataset to ensure consistency. The dataset included the last names of the author (s) of each article (i.e., first column) and the full conclusion text for each selected study (i.e., second column). To maintain a uniform qualitative dataset focused strictly on textual interpretation, any supplemental information included in the conclusion (e.g., data tables, charts, graphs, figures, or quantitative results) were removed from the text analyzed in this study. As such, only the written narrative portion of each conclusion was analyzed. This approach resulted in an Excel file with 51 rows, in which the first row consisted of the column headings and two columns. The generated Excel file served as the primary data source for the subsequent textual analyses. The Excel file was read into RStudio for processing and analysis. The code used for this study is provided in Appendix B. The following packages were used to analyze the articles’ introductions; dplyr (Wickham et al., 2026a), ggplot2 (Wickham et al., 2016), textdata (Hvitfeldt, 2024), tidyr (Wickham et al., 2026b), tidytext (Silge & Robinson, 2016), tidyverse (Wickham et al., 2019), readxl (Wickham & Bryan, 2025), stringr (Wickham, 2025), tm (Feinerer & Hornik 2025), and xlsx (Dragulescu & Arendt, 2022).
Prior to analysis, common words (e.g., a, the, this, that, etc.) were excluded from the study, as the focus was on the content of conclusions related to business strategy, not the grammatical form in which that content was expressed. The analysis was focused on the following three areas: a) word frequency, b) bigram frequence, and c) sentiment analysis. The top 25 words and bigrams of merit (i.e., determined to be conceptually linked to business strategy) were determined and listed in order of frequency. If either the top 25 words or bigrams of merit end at a point at which there are words (or bigrams) that are equally as frequent as the 25th term (or bigram), the additional terms (or bigrams) were included until that frequency of occurrence was exhausted. Words and bigrams of merit is a phrase used to convey the words that are retained after the exclusion of common stop words (Troyer et al., 2024). Once the words and bigrams of merit were produced, an inductive, thematic analysis was conducted by the authors for each respective list of terms or phrases, as there were no predefined themes used, but the themes were generated organically from the observations (Braun & Clarke, 2006). The collected information was placed in tabular form. The AFINN sentiment lexicon (Nielsen, 2011; Silge & Robinson, 2016) was used to determine the average (i.e., mean) sentiment score by article. AFINN sentiment ranges from −5 (i.e., extremely negative sentiment) to +5 (i.e., extremely positive sentiment), with a value of 0 indicating a neutral sentiment, and is useful for ascertaining the emotional valence of a text (Burch et al., 2026). A boxplot (, 2020) for those data was generated to convey the distribution of sentiment found when concluding academic research on business strategy. A one-tailed, single sample t test (Black) was used to test hypothesis 1 (H1). A one-tailed test was considered most appropriate because results from prior research suggest that business strategies tend to be positive (Malmendier & Tate, 2008).
As indicated, this study utilized a mixed-method, qualitative, thematic textual analysis approach to analyze the top 25 key words and bigrams of merit of an author-created corpus consisting of the conclusion section from top 50 most relevant academic articles identified when the search phrase business strategy was used. Additionally, the average AFINN sentiment score per article was determined. Collectively, these three focus areas provide insight into the content and tone associated with business strategy. With the research method established, it is possible to turn attention to the actual results of this research (Section 4).
4. Results
As indicated in the method (Section 3), this study is a mixed-method, thematic, textual analysis. This section presents the findings from the analysis conducted on author-created corpus which compiled the conclusion sections of the top 50 most relevant academic journal articles identified using the search phrase business strategy. The purpose of this analysis was to identify the most common themes, patterns, and structures present in the conclusions of articles focused on business strategy. The extracted conclusion texts were analyzed using word frequency analysis, bigram frequency analysis, and sentiment analysis. The results of this analysis are presented in four major sections: frequency analysis of words of merit (Section 4.1), frequency analysis of bigrams of merit (Section 4.2), descriptive statistics associated with average sentiment scores across top 50 articles (Section 4.3), and the integrated findings across analyses (Section 4.4). These findings provide insight into how academic researchers commonly frame implications, limitations, and future directions when concluding analyses of research related to business strategy.
4.1. Words of Merit Frequency
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.
Obs. |
Word |
Frequency (n) |
1 |
Business |
274 |
2 |
Strategy |
187 |
3 |
Research |
131 |
4 |
Study |
109 |
5 |
Strategies |
106 |
6 |
Digital |
85 |
7 |
Companies |
75 |
8 |
Performance |
70 |
9 |
Market |
69 |
10 |
Competitive |
63 |
11 |
Innovation |
61 |
12 |
Model |
58 |
13 |
Results |
58 |
14 |
Based |
56 |
15 |
Future |
53 |
16 |
Management |
52 |
17 |
Firms |
51 |
18 |
Managers |
51 |
19 |
Advantage |
47 |
20 |
AI |
45 |
21 |
Knowledge |
45 |
22 |
Process |
43 |
23 |
Activities |
42 |
24 |
Company |
42 |
25 |
Studies |
40 |
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.
4.2. Bigrams of Merit Frequency
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 25th 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), Ahmad 2010 (n = 8), and theoretical framework (n = 7). The reference to both the ban model (n = 13) and Ahmad 2010 (n = 8) are from the source Abu Adi et al. (2021). 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.
Obs. |
Bigram |
Frequency (n) |
1 |
Business strategy |
97 |
2 |
Business strategies |
47 |
3 |
Competitive advantage |
36 |
4 |
Future research |
21 |
5 |
Digital business |
15 |
6 |
Business model |
14 |
7 |
Digital transformation |
14 |
8 |
Ban model |
13 |
9 |
Business models |
12 |
10 |
Digital strategies |
12 |
11 |
Tournament incentives |
12 |
12 |
Future studies |
11 |
13 |
Contracting companies |
10 |
14 |
Innovative activities |
9 |
15 |
Ahmad 2010 |
8 |
16 |
Daily news |
8 |
17 |
Maturity mismatched |
8 |
18 |
Mismatched investment |
8 |
19 |
British business |
7 |
20 |
Differentiation strategy |
7 |
21 |
Executive compensation |
7 |
22 |
Financial performance |
7 |
23 |
Firm performance |
7 |
24 |
Institutional distance |
7 |
25 |
Media outlets |
7 |
26 |
Social media |
7 |
27 |
Strategy types |
7 |
28 |
Tax policy |
7 |
29 |
Theoretical framework |
7 |
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 management and 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), british business (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, Wang et al. (2021). 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.
4.3. Sentiment Analysis
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) Bederna et al. (2021) with an average sentiment score of −0.1, and b) Zarenczansky et al. (2024) 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.
4.4. Integrated Findings across 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 model indicates 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).
5. Conclusion
This study examined how business strategy has been conceptualized in the conclusion sections of academic journal articles through a structured thematic textual analysis of 50 peer-reviewed articles. The findings of this study revealed a consistent narrative structure across the corpus. Word frequency and bigram analyses demonstrated business strategy conclusions repeatedly emphasized competitive advantage, organizational performance, business models, and digital transformation. These patterns suggest scholars reinforce the foundational objectives of strategy research: a) improving firm competitiveness and b) navigating evolving market environments. At the same time, the frequent appearance of forward-looking language, with phrases like future research and digital transformation, indicated strategy has been typically framed as an evolving field of inquiry. Sentiment analysis further revealed that business strategy conclusions were generally written in a positive tone, emphasizing contribution, opportunity, and managerial relevance. This suggested strategy scholars have tended to conclude their research by reinforcing optimism about the applicability and future impact of their findings, even while acknowledging its limitations. Collectively, these results indicate that business strategy conclusions follow a structured academic pattern: a) summarizing empirical findings, b) reinforcing strategic value, c) emphasizing competitive implications, and d) encouraging continued scholarly inquiry.
By combining thematic textual analysis with strategic management research, this study showed how language reflects disciplinary priorities and evolving business concerns. As digital and technological transformations continue to reshape markets, the way scholars frame strategy in their conclusions may provide signals about the future direction of the field. Ultimately, this research demonstrated that the way scholars have summarized and concluded their studies plays an insightful role in shaping how business strategy is understood and how the field continues to evolve. Whereas these insights are useful, this study is not without its limitations.
This study was based on a relatively small sample of 50 academic articles, written in a single language, and from a single database. Individually and collectively, this limits the generalizability of the results of this study. First, based on these initial results, the study could be beneficially expanded to include more than 50 articles. Such expansion would broaden its aperture and add insight into the consistency of results. Second, the articles selected were constrained to academic, peer-reviewed articles. This provides a limited understanding of business strategy. Conducting a subsequent study of the conclusion sections of trade or practitioner publications would provide a basis for a robust comparative analysis of the differences in treatment of the topic. Third, all of the articles were published in English. The selection of language could reasonably shape and constrain how business strategy is understood. Expanding the study to examine articles published in languages other than English would be constructive to understanding the degree to which this occurs. Relatedly, English words and phrases like future research, study, and results suggest strong genre conventions which may reflect academic writing norms as much as substantive priorities in business research. Lastly, this study used a single academic search engine. Conducting a comparative analysis among various free and subscription sources would provide insight into the degree to which understanding is potentially constrained by access.
Beyond identifying the identified recurring themes, this analysis also highlighted the broader communicative function of conclusion sections. Conclusions do more than restate results, they actively position research within ongoing academic conversations and alert one to what is considered relevant, valuable, and impactful within the discipline. By emphasizing competitive advantage, innovation, and forward-looking development, strategy research appears to frame itself as both analytically rigorous and practically applicable. This reinforces the idea that business strategy scholarship is not only concerned with explaining firm behavior, but also with guiding managerial decision-making and addressing organizational challenges.
These findings contribute to a deeper level of understanding within the strategy field. Rather than focusing primarily on what firms should do, this study clarifies how scholars communicate strategic knowledge and how rhetorical patterns shape understandings of the discipline. As such, it opens the door for future research to explore how academic framings evolve over time, and how shifts in language can reflect broader economic, organizational, and technological transformations at play within our society.
Appendix A
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https://doi.org/10.1080/1051712x.2024.2395570
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Appendix B
library(dplyr)
library(ggplot2)
library(textdata)
library(tidyr)
library(tidytext)
library(tidyverse)
library(readxl)
library(stringr)
library(tm)
library(xlsx)
#File Check: Returns list of Excel Files in Folder
filelist <- list.files(pattern="*.xlsx")
#Corpus construction: Each Row is a News Article
posts <- read_excel("Data.xlsx")
#Break Corpus Down into words
tidy_posts <- posts %>%
unnest_tokens(word, Conclusion)
#Remove Stop Words
data(stop_words)
tidy_posts <- tidy_posts %>%
anti_join(stop_words)
#Count Words of Merit (WOM)
tidy_posts %>%
count(word, sort = TRUE) %>%
top_n(100)
#Bigram Analysis:
strat_bigrams <- posts %>%
unnest_tokens(bigram, Conclusion, token = "ngrams", n = 2)
strat_bigrams %>%
count(bigram, sort = TRUE)
#taking out common words
strat_bigrams_seperated <- news_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
strat_bigrams_filtered <- strat_bigrams_seperated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
strat_bigrams_filtered_counts <- strat_bigrams_filtered %>%
count(word1, word2, sort = TRUE)
strat_bigrams_united <- strat_bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")
#Sentiment Analysis
get_sentiments("afinn")
tidy_posts_sentiment <- tidy_posts %>%
inner_join(get_sentiments("afinn"))
intro_source_sentiment <- tidy_posts_sentiment %>%
group_by(Author) %>%
summarise(sentiment = mean(value))