INTRODUCTION

Technological changes, evolving consumer needs, and market dynamics are external factors that significantly influence organizational performance (^{Bayighomog et al., 2020}). The literature on strategic management and resources has explained the factors contributing to attaining superior results in sustainable competitive advantage (^{Andersén, 2021}). Furthermore, the Dynamic Capabilities (DC) perspective delves deeper into the analysis, elucidating how competitive advantage and superior performance are achieved (^{Kaur, 2019}). Therefore, dynamic capabilities have recently earned considerable attention as organizations endeavor to adapt to unpredictable business environments.

Numerous contributions to the DC perspectives have emerged since its introduction, particularly in literature reviews and theoretical stances, compared to the empirical evidence generated (^{Reimann et al., 2021}). Considering the heterogeneity of the research subjects, the abstraction of the conceptual framework of the research perspective, and the nomological network, the possibility of contributing to the development of the perspective remains valid (^{Leemann & Kanbach, 2022}; ^{Schriber & Löwstedt, 2020}; ^{Schilke et al., 2018}).

Therefore, the study aims to analyze the business adaptability of firms and superior performance, utilizing the Triad Model of DC (^{Pitelis et al., 2023}), a set of distinctive capabilities that allow the organization to adapt and innovate. The variables sense, seize, and transform have been scrutinized based on the available state-of-the-art literature and collected a priori to ensure consistency with systematic progress (^{Pitelis et al., 2023}). These variables are selected because there are active studies in which the company is involved, through Sense or detect activities, can interpret the information to develop changes to adapt to customers, suppliers, market, or competitors (^{Khan et al., 2020}) and, at the same time, develop capabilities that help to adapt to these changes. By seizing or integrating knowledge into the different changes (^{Correia et al., 2021}), and with the above, the company will be able to transform and reallocate resources without compromising the company's performance (^{Prester, 2023}).

A non-experimental, exploratory, and cross-sectional study was designed for this research. Key informants included middle and senior managers of companies that hosted students from the Internship Program of the University Center between November 2021 and April 2022. The company's age was a control variable when calculating a stratified sample. Two hundred twenty-seven companies met the inclusion criteria, with a margin of error of 5% and a confidence level of 95%. A self-administered electronic survey was constructed using perceptual measures and distributed through emails from the University Center (^{CUCEA, 2021}; ²⁰²²). Descriptive statistics and exploratory factor analysis were utilized to analyze business adaptability and superior performance, and the nonparametric test Kuskal-Wallis was chosen to test hypotheses.

DYNAMIC CAPABILITIES TAXONOMY

The literature review unveils extensive research on Dynamic Capabilities (DC) frameworks. ^{Schilke et al. (2018)}, ^{Arend and Bromiley (2009)}, and ^{Teece (2007)} highlighted criticisms of its construct in the decades after the perspective's introduction. The primary controversy concerns whether DC should be perceived as a process or capability enabling firms to uphold their competitive advantage (^{Wilhelm et al., 2022}). The contributions of ^{Eisenhardt & Martín (2000)} and Teece and Pisano (1994) have influenced numerous researchers and essays, presenting divergent narratives that hindered the perspective's consolidation (^{Kurtmollaiev, 2020}; ^{Waleczek et al., 2019}; ^{Pavlou & El Sawi, 2011}; ^{Helfat, 2007}). The discussion also addresses the implications of dynamic capabilities for organizational strategy and the challenges linked to their development and effective utilization.

The hierarchical treatment has been vital to reconciling positions and reducing the concepts' abstraction level (^{Chen, 2005}). To date, sufficient evidence has emerged affirming that employing concept hierarchization makes it possible to structure the perspective taxonomy, concluding that the schools of Teece and Eisenhardt are complementary and convergent.

Their integration provides valuable elements for advancing the perspective toward consolidation (^{Wenzel et al., 2021}).

There is consensus that the taxonomy includes first- and second-level capabilities (^{Teece, 2018}, p. 363). The first level of the taxonomy, also called high-order capabilities, pertains to observable behaviors across organizations' cultures that represent competitive advantage (^{Santoro et al., 2019}; ^{Bogers et al., 2019}). It is inferred that companies with these capabilities are flexible, collaborative, and prone to adaptation to sustain superior performance (^{Kahn et al., 2020}). These capabilities are observed in the study to elucidate how companies attain organizational adaptability through the implementation of the triad model, which integrates the capabilities of Sense, seize, and transform (^{Leemann & Kanbach, 2022}; ^{Leemann et al., 2021}; ^{Furnival et al., 2019}).

First-level capabilities, (1) sense, are capabilities through which organizations identify emerging opportunities and environmental threats. (2) Seize and (3) Transform are capabilities by which the organization captures and reconfigures its resources to capitalize on the insights (^{Popadiuk et al., 2018}; ^{Zahra et al., 2006}).

While operational processes are considered second-order or micro-foundational capabilities, they are now learning is generated and used to develop improvements, thereby accumulating knowledge, improving processes, and ultimately aligning with first-order capabilities (^{Gaviria-Marin et al., 2021}; ^{Wenzal et al., 2021}; ^{Helfat, 2007}). Processes allow the organization to cultivate first-order DC, thus elucidating how business adaptability is achieved. Firms respond to the dynamism of the environment by employing different configurations of resources and capabilities that are influenced by the firm's characteristics and its context (^{Bayighomog et al., 2020}; ^{Chen et al., 2021}; ^{Fainshmidt et al., 2016}; ^{Foss & Lindenberg, 2013}; ^{Peteraf et al., 2013}; ^{Foss, 2011}). Therefore, studying firm heterogeneity reveals different ways of sensing, seizing, and transforming firms and contributes to the strategic management of firms (^{Foss, 2011}, p. 1414).

^{Popadiuk et al. (2018)} and ^{Leemann and Kanbach (2022)} observed the alignment between second-order processes and the three critical high-order capabilities or competencies in the dynamic capabilities' taxonomy: Sense, Seize and Transform (Yoshikun, described initially by ^{Teece and Pisano (1994)} and supplemented by ^{Furnival et al. (2019)} and ^{Leemann et al. (2021)}.

With the organization of the taxonomy, the contributions of dynamic capabilities (DC) are strengthened. This perspective provides a theory of the mechanisms used by organizations to maintain a position of competitive advantage over time, complementing the theory of resources and strategic management, which is particularly relevant in constantly changing environments (^{Teece et al., 1997}; ^{Eisenhardt et al., 2000}; ^{Doz, 2020}).

Therefore, to measure business adaptability, the study adheres to the hierarchical treatment of the taxonomy and focuses its observations on the high-level capabilities presented by ^{Teece and Pisano (1994}). Three key capabilities, Sense, Seize, and Transform, permeate the organization (^{Kurtmollalev, 2020}; ^{Schilke et al., 2018}).

Incorporating empirical evidence to validate the Triad Model of DC remains ongoing; therefore, adding empirical support is crucial. ^{Araújo et al. (2018)}, ^{Kump et al. (2018)}, ^{Pavlou and El Sawy (2011)}, and ^{Thanh Nhon et al. (2020)} have made contributions toward achieving this objective. The study is designed to contribute using the perspective's first-level taxonomy. It addresses the tautological relationships between the concepts, the triad model, and the superior performance measures (^{Arndt et al., 2022}). Therefore, we propose the following research hypothesis:

H: There is a statistically significant difference between business adaptability and superior performance.

Dynamic capability and superior performance

Theoretically, given the evolutionary logic of changing the resource base to maintain or gain competitive advantage, a natural outcome is a relationship between the triad model of DC and superior performance. Therefore, it is necessary to define how superior performance is measured. The literature review focuses on the critical importance of studying the various dimensions of performance, enabling the identification of areas for improvement within the organization (^{Cristofaro & Lovallo, 2022}; ^{Rengkung, 2022}; ^{Ringov, 2017}). We divide superior performance indicators into qualitative and quantitative categories for practical purposes, defining them below.

First, the quantitative measures commonly used in academic studies are related to financial performance. The concept pertains to the economic health of companies over a specified period. Some metrics are associated with return on assets, equity, and earnings per share (^{Gjoni et al., 2022}; ^{Sánchez-Gutiérrez et al., 2019}). Other studies analyze Key Performance Indicators (KPIs), such as absenteeism, employee turnover, churn cost, health cost, outputs, performance, profitability, and time worked. These also represent quantitative measures (^{Bortoluzzi et al., 2021}).

Second, qualitative measures are essential for understanding aspects that quantitative data alone cannot capture. These measures often include customer satisfaction assessments, leadership effectiveness, and organizational culture (^{Bortoluzzi et al., 2021}). Although qualitative indicators are more subjective and difficult to quantify, they provide a comprehensive view of a company's performance. Frameworks such as SERVQUAL demonstrate the potential of qualitative measures (^{Sarathy, 2006}). In this line of thinking, perceptual measures can be valid and accurate if they capture the experiences and attitudes of those most closely associated with the organization, as demonstrated by the study of ^{Ross and Grace (2012)}.

According to ^{VU (2020)}, researchers employ qualitative and quantitative superior performance measures. Therefore, this study implemented five indicators for the variable superior performance using a comprehensive measure adapted from ^{Thanh Nhon et al. (2020)} work. The adaptation of the measures addresses tautological associations between the variables.

^{Gaviria-Marin et al. (2021)} used the company's age as a control variable, which is essential because it is often associated with accumulating knowledge and experiences. Therefore, older firms typically possess established routines, deeper customer relationships, and more robust brand recognition. Thus, the company's ability to address rapidly changing environments can be observed through its longevity. The interplay between the age of the organization and dynamic capability influences the ability to strategize for sustainable success, ultimately correlating with superior performance (^{Chen et al., 2021}). Some studies supporting the aforementioned include ^{Lin et al. (2020)}, ^{Mikalef et al. (2020)}, ^{Zahra et al. (2006)}, and ^{Eisenhardt and Martin (2000)}. The study's reflective model has three independent variables measuring business adaptability: Sense, seize, and transform; five items for superior performance as the dependent variable; and finally, the control variable, age firm (Figure 1).

Source: Own elaboration.

Figure 1 Research model. Business adaptability and Superior performance 

METHODS

The research is non-experimental, exploratory, and cross-sectional. It was designed to explain business adaptability and its correlation with superior performance. The study involved middle and senior managers who supervised students during internships between November 2021 and April 2022. A self-administered electronic survey distributed through emails from the University Center (^{CUCEA, 2021}; ²⁰²²) served as the data collection tool. The information collection lasted six months, from November 2021 to April 2022. The Centro Universitario de Ciencias Económico Administrativo (CUCEA) facilitated the survey through its Professional Internship Program in 2021 and 2022. The survey was constructed using perceptual measures (^{Leví-Magnin and Varela-Malloy, 2003}). A lower score indicated more robust disagreement with the statement, while a higher score represented significant agreement. The survey included 12 items to measure business adaptability, with five dedicated to assessing superior performance. Lastly, the age of the firm was used as a control variable.

Normality was assessed using the histogram technique in the Statistical Package for Social Sciences ver. 21.1 (SPSS). The data exhibited asymmetry, with a mean of 2.43 and a standard deviation of 0.75 (nh= 227). Companies with eight or more years in the market were proportionally the most represented stratum (59%). Content validity was estimated using the Lawshe method, yielding a content validity index of 94%. The instrument's reliability was assessed with Cronbach's alpha coefficient using SPSS, yielding a value of 94.3%.

The study used descriptive statistics, where the standard error of the mean, mean standard deviation, and lower and upper limits. An exploratory factor analysis (EFA) used the principal component method to elucidate the data behavior and interdependence among the Triad Model of DC components. The EFA techniques employed were (1) correlation test, (2) commonalities, (3) component matrix, (4) KMO and Bartlett, and (5) proportion of variance explained. No underlying factors were identified. Consequently, we grouped the measures according to the operational concept of the Triad model of DC and conducted a second exploratory factor analysis. Rotation tests and sedimentation plots were deemed unnecessary for this analysis (^{Fornell & Larcker, 1981}).

The data's nature and research objectives justify using the Kruskal-Wallis test for hypothesis testing. First, the data asymmetry violates the normality assumption required for parametric techniques such as ANOVA. According to ^{Gibbons and Chakraborti (2011)}, the Kruskal- Wallis test is a natural extension of the two-sample Wilcoxon test (p.357), making it suitable for non-normally distributed data. Given the asymmetry of the data, alternatives such as the median test extension and Kruskal-Wallis, Terry, and Van der Waerden tests are all appropriate. However, the researchers chose the nonparametric Kruskal-Wallis test because of its simplicity, power, availability of exact distribution tables, and reasonably accurate chi- squared approximation (^{Gibbons et al.,2011}, p. 378). Researchers have found the technique to be 95% effective compared to other parametric methods.

In addition to considering the nature of the data and the test's effectiveness, have also been considered software compatibility. The Statistical Package for Social Sciences (SPSS) version 21.1. was used for the analysis, and the Kruskal-Wallis test was quickly implemented and widely used within this software package. Therefore, the test emerged as the preferred choice for hypothesis testing, providing a robust and accessible method for examining differences in medians across multiple groups.

Finally, 555 (N) companies met the criteria for inclusion in the sample by substituting the literals of the formula for calculating the sample in a finite population; a sample of 227 (nh) was obtained, stratified by the control variable of the firm's age. The maximum allowable error was 5%, and the confidence level was 95%. (Table 1)

RESULTS AND DISCUSSION

CONCLUSION AND LIMITATIONS

The dynamic capabilities perspective arises from resource-based theory and strategic management. The Triad Model of DC examines these capabilities through intra- organizational behaviors that constitute business adaptability and, consequently, a competitive advantage (^{Cristofaro & Lovallo, 2022}; ^{Markovich et al., 2022}; ^{Doz, 2020}; ^{Zhang et al., 2018}). While maintaining taxonomy and avoiding tautological relationships, the use of perceptual measures is a valuable technique for observing the presence of DC (^{Min & Kim, 2022}; ^{Thanh Nhon et al., 2020}; ^{Ross & Grace, 2012}). According to the literature, these characteristics represent competitive advantages and define a flexible, ambidextrous, and adaptable organization that produces results perceived as superior to competitors. (^{Al-Matari et al., 2022}; ^{Almaraz, 2022}; ^{Gaviria-Marin et al., 2021}).

The literature review revealed evidence of the integration of the contributions of ^{Teece and Pisano (1994)}, ^{Eisenhardt and Martin (2000)}, and the Ambidexterity theory (^{Frogeri et al., 2022}). These frameworks complement each other and, when integrated, enhance the taxonomy of perspective capabilities, delineating first and second-level capabilities (^{Leemann & Kanbach, 2022}). It addresses the observation that the taxonomy of the dynamic capability’s perspective lacks order (^{Kurtmollaiev, 2020}; ^{Schilke et al., 2018}). The Sense, seize, and transform triad model of DC reinforces and advances the dynamic capabilities perspective on consolidation (^{Leemann & Kanbach, 2022}; ^{Leemann et al., 2021}; ^{Thanh Nhon et al., 2020}; ^{Kump et al., 2018}; ^{Teece, 2007}).

The descriptive statistics results, considering a stratified sample of 227, a confidence level of 95%, and an estimated error of 5%, indicate an SEM of 0.6 to 0.8, suggesting a relatively accurate estimate of the population mean in the specific context of a stratified sample. Therefore, the distribution of the indicators of interest across the population is understood (^{Berndt, 2020}). Conversely, the Exploratory Factor Analysis (EFA) results for the variable Business Adaptability have captured a significant part of the variability present in the data, with an AVE of 78% for the variables of the first-order triad model of DC. It indicates that the extracted factors can explain much of the variation observed in the variables analyzed. The commonalities of the factor loadings are within an adequate range, with values between 0.69 and 0.83, indicating the strength of the relationship between the factors in the model, with transformation being the most representative (h value of 0.83, r = .78, and λ = .91). The correlations between the variables of the model are robust, with coefficients ranging from 0.61 to 0.78, indicating significant relationships between them. Similarly, the values of the matrix components are consistently high, oscillating between 0.81 and 0.91, indicating a strong association between the observed variables. Furthermore, the significance value is less than 0.05, and the Kaiser-Meyer_Olkin measure of 0.70 indicates that the data sample is suitable for factor analysis, supporting the validity of the results (^{Mavrou, 2015}).

The literature review indicates that Sense represents the firm's ability to systematically search for information outside the firm to identify opportunities and threats to formulate action plans (Kump et al., 2019; ^{Pavlou & El Sawi, 2011}). According to Mostafiz, companies with a high ability to sense are "Able to identify opportunities arising from changes in customers, technology, and their competitors, increasing their ability to adapt" (2020, p.18). The results of the Exploratory Factor Analysis (EFA) suggest that companies associated with Centro Universitario de Ciencias Económico Administrativas (CUCEA) through the internship program commonly engage in increasing knowledge and integrating it into decision-making processes (^{Al-Matari et al., 2022}; ^{Barros-Contreras et al., 2021}).

The H statistic for the grouped variable Sense and the indicator of superior performance has a lower bound of 44.37 and an upper bound of 61.89, corresponding to product and service innovation and more significant revenue growth, respectively. The more incredible revenue growth indicates superior performance, exhibiting the highest disparity within the sense variable. The data's behavior is asymptotic, and the significance level is <0.5. The second most important factor is higher profitability and greater customer satisfaction. These results are consistent with firms' interest in gathering, sharing, analyzing, and utilizing market intelligence (^{Bayighomog et al., 2020}) and decision-making through big data analytics (^{Akter et al., 2020}). The mediating role of the sense variable on the variables is to seize and transform (^{Markovich et al., 2022}).

The seize variable examines whether firms utilize internal collaboration mechanisms to gain competitive advantage. The H statistics results indicate that a more substantial presence of the integrated indicators corresponds to a more significant presence of superior performance, which aligns with the importance of synchronized work sharing and sharing individual knowledge to the group (^{Pavlou & El Sawy, 2011}), and the consistency of the company in the implementation of planned changes (^{Kump et al., 2018}). The H statistics ranged between ranks 19.12 and 32.05. Compared with innovation in product and service, the seized variable had the lowest H value, and the highest quality product or service had the most representative H value.

The data's behavior is asymptotic, and the significance level is <0.5. This observation is consistent with ^{Peng et al. (2022}) assertion regarding the importance of alignment, organizational structure design, and organizational processes in adapting firms to environmental and technological change. The results indicate that the companies associated with the University Center possess this ability, although to a lesser extent than the Sense and transform variables. Future efforts should complement the scale with items aligned to the triad DC model. However, considering that seize has the highest level of abstraction, it is inferred that this tends to make it less noticeable to be observed (^{Le & Lei, 2019}; ^{Gregory et al., 2017}; ^{Friedman et al., 2016}). Researchers suggest conducting studies at the second level 96 or micro-foundations to specify the mechanisms by which top management creates or incentivizes collaboration and knowledge absorption among the different levels of the organizational structure.

We utilized five indicators to analyze the Transform variable, including the ability to communicate operational needs, among other measures. This indicator emerged as the most representative, exhibiting the highest factor loadings and correlation values. Decision-makers ensure that flexibility does not compromise operational efficiency and that improvements in production, administration, maintenance processes, or the integration of new technologies are effectively implemented. They must also ensure that the organization's learning curve is short, the level of uncertainty is low, and the organization has clarity about the expected results (^{Hernández-Linares et al., 2020}; ^{Lin et al., 2020}).

Transforming exhibits the highest correlation value, commonality, and factor loadings. Hypothesis testing was bound between 54.68, the lowest value for H, and 64.77, the highest, representing higher product and service quality and profitability, respectively. The data's behavior was asymptotic, and the significance level was <0.5. The result suggests that the greater the ability to transform, the more comprehensive the range of superior performance consistently. This finding aligns with ^{Schepers et al. (2022)}, who referenced the study of Jong and Den-Hartong, highlighting the critical role of capturing innovativeness signals. Employees' intentional effort to positively influence work outcomes by introducing change and innovation to processes, products, services, and customer solutions is crucial (^{Schepers et al., 2022}, p.3).

The EFA results for the dependent variable (superior performance) indicate correlations ranging from 0.60 to 0.75, suggesting a substantial and coherent relationship between them. Additionally, the commonality values ranging from 0.69 to 0.85 indicate they are well represented. The values on the component matrix are high, between 0.83 and 0.92. The AVE is 76%, meaning that the extracted factors can explain a significant amount of the total variability of the data. A KMO of 0.88 indicates that the data is suitable for factor analysis (^{Mavrou, 2015}). The superior performance measures are in line with the study of ^{Min and Kim (2022)}, ^{Peng et al. (2022)}, and ^{Mostafiz (2020)}. The results show that when it comes to customer satisfaction and profitability, followed by products and services of higher quality or the introduction of innovations, the companies associated with the University Center have better results than their competitors.

Finally, hypothesis testing showed significant differences between business adaptability and superior performance. The relationship demonstrates an asymptotic pattern. The high H- statistics suggest a statistically significant difference. Therefore, hypotheses 1, H1a, H1b, and H1c of the study have been accepted. The operationalization of the concepts addressed the tautological relationships between the variables. Thus, empirical evidence was generated under conditions of systematic advancement, following the recommendations of ^{Arndt and Pierce (2018)(}. These findings emphasize the critical role of dynamic capabilities in driving superior performance. The ability to adapt through sensing, seizing, and transforming becomes a determinant of success. Therefore, investing in nurturing their dynamic capabilities in organizations is advisable.

According to the Demografía de los negocios (^{INEGI, 2022}), observers consider them companies capable of "consciously creating, expanding, and modifying their organizational operations for adaptive purposes" (^{Gaviria-Marin et al., 2021}, p. 417). The items measuring customer satisfaction and profitability (compared to the competition) were the most representative. Because of the COVID-19 global health emergency, the years 2021 and 2022 were financially complicated for the Mexican economy.

In Jalisco, approximately 90 days of inactivity were implemented to control the spread of the disease, and government regulations and readjustments in the global commercial chain were external factors that affected the organizations. Adaptability favored the companies associated with the University Center in maintaining their performance even during economic, political, and social pressure periods. Thus, due to the cross-sectional nature of the phenomenon, more data is needed to prove that firm business adaptability changes over time. A longitudinal study is recommended.