Strategic Decision-Making Agility and Organizational Resilience During Economic Uncertainty: Evidence from a Quantitative Corporate Survey

Abstract

This study investigates the role of strategic decision-making agility in enhancing organizational resilience under conditions of economic uncertainty. Although prior studies have examined strategic agility and organizational performance, limited empirical research has explored the internal mechanisms linking strategic decisionmaking agility, dynamic capabilities, and organizational resilience in developingcountry contexts. This study addresses that gap by examining the mediating role of dynamic capabilities and the moderating role of digital readiness. A quantitative approach was employed using survey data collected from 250 managers working in manufacturing, logistics, and service companies. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that strategic decision-making agility positively affects organizational resilience and significantly strengthens dynamic capabilities. Dynamic capabilities were also found to positively influence organizational resilience and partially mediate the relationship between strategic decision-making agility and organizational resilience. In addition, digital readiness significantly moderates the relationship between strategic decision-making agility and organizational resilience, indicating that organizations with stronger technological preparedness are better able to translate agile decision-making into resilience outcomes. This study contributes to strategic management literature by integrating strategic decision-making agility, dynamic capabilities, digital readiness, and organizational resilience into a single empirical framework within a developing-country context. The findings suggest that organizational resilience during periods of economic uncertainty depends not only on rapid and adaptive decision-making, but also on the organization’s capability development and technological preparedness.

KEYWORDS strategic decision-making agility; organizational resilience; dynamic capabilities; digital readiness; economic uncertainty.

Introduction

In the last few decades, the business world has encountered increasingly complicated and unpredictable situations due to various global economic problems, including the worldwide financial crisis, the COVID-19 pandemic, and issues in global supply chains (Arbale & Mutisya, 2024). These major crises not only impact overall economic health but also directly affect how organizations in various sectors can sustain themselves. Some companies have struggled and gone bankrupt, while others have managed to survive and even flourish amid the crisis. This scenario highlights that how well an organization navigates uncertainty is crucial for determining whether it thrives or falters (Ivanov & Dolgui, 2020). In this regard, the ability to make swift strategic decisions has become vital for modern organizations. This capability shows how well an organization can respond quickly, flexibly, and adaptively to environmental changes through efficient decision-making processes. Organizations that are nimble often find it easier to foresee market shifts, manage risks, and seize opportunities that arise during uncertain times (Teece, 2018). Hence, agility in strategic decision-making is not just an edge in competition but also a critical requirement for confronting global crises(Barney, 2011). Nonetheless, there exists a notable gap between the rapidity of decision-making and an organization's capacity for long-term survival, referred to as organizational resilience. Organizational resilience involves the ability to endure shocks, adapt to alterations, and rebound from crises while maintaining core operations(Duchek, 2019). While agility enables organizations to respond swiftly, not all agile organizations exhibit high levels of resilience. This suggests that various factors influence the link between agility and resilience, such as dynamic capabilities, strategic leadership, and readiness for technology (Teece, 2018);(Wamba et al., 2017);(Eddy & Boggs, 2010). Research shows significant distinctions between businesses that manage to succeed and those that struggle during global crises (Hanelt et al., 2020). For example, some companies were able to rapidly implement digital transformation and adapt their business models, thus remaining competitive during the pandemic (Vial, 2019);. Conversely, companies that were slow in decision-making or lacked adequate adaptive capabilities tended to experience significant performance declines. This reinforces the importance of integrating agility and resilience as two complementary concepts in organizational crisis management (Bhamra et al., 2011);(Tugkan Arici, 2023). Nonetheless, empirical research examining the relationship between strategic decision-making agility and organizational resilience remains limited, particularly in developing-country contexts such as Indonesia. Previous studies have generally examined strategic agility, dynamic capabilities, or organizational resilience separately, with limited attention to how these constructs interact within a single empirical model (Tugkan Arici, 2023). In particular, prior research has not sufficiently explained the internal mechanism through which strategic decision-making agility contributes to organizational resilience, nor has it adequately examined the boundary condition under which this relationship becomes stronger. Most existing studies have also been conducted in developed economies, thereby limiting understanding of how organizations in emerging economies respond to environmental uncertainty and economic disruption (Istiak & Serletis, 2020). From a methodological perspective, many earlier studies focused primarily on direct relationships between strategic agility and organizational outcomes, while fewer studies simultaneously tested the mediating role of dynamic capabilities and the moderating role of digital readiness within the same structural framework (Pham, 2024). Therefore, the present study extends prior research by empirically examining how dynamic capabilities mediate the relationship between strategic decision-making agility and organizational resilience, while digital readiness functions as a moderating condition that strengthens this relationship. Building on this context, this study addresses several research questions. First, to what extent does strategic decision-making agility influence organizational resilience under conditions of economic uncertainty? Second, how do dynamic capabilities mediate the relationship between strategic decision-making agility and organizational resilience? Third, does digital readiness strengthen the relationship between strategic decision-making agility and organizational resilience? These questions are important for

understanding the organizational mechanisms that support resilience in uncertain business environments. Accordingly, this study aims to empirically examine the direct effect of strategic decision-making agility on organizational resilience, the mediating role of dynamic capabilities, and the moderating role of digital readiness using a quantitative approach. By focusing on organizations operating in a developing-country context, this study seeks to provide a more contextually relevant understanding of organizational resilience under conditions of economic disruption. This study contributes to the strategic management literature in several ways. First, it extends prior resilience and agility research by simultaneously testing the mediating role of dynamic capabilities and the moderating role of digital readiness within a single empirical model. Second, the study provides empirical evidence from Indonesia as a developingcountry context, where research on organizational resilience and strategic decision-making agility remains relatively limited. Third, rather than proposing an entirely new conceptual perspective, this study offers a contextual and empirical extension of previous studies by demonstrating how organizational agility, capability development, and technological preparedness interact in shaping organizational resilience during periods of economic uncertainty.

Literature Review and Hypothesis Development

Strategic Decision-Making Agility The idea of agility in strategic decision-making is becoming more prominent in strategic management discussions, especially when dealing with rapidly changing and unpredictable business conditions. Essentially, strategic decision-making agility refers to how quickly, flexibly, and adaptively an organization can make strategic choices in reaction to shifts in its surroundings (Teece, 2018);(Bresciani et al., 2018). This concept showcases an organization's skill in gathering real-time information, assessing different strategic options, and responsive adjusting its policies (Lee et al., 2015) The primary aspects of strategic decision-making agility include speed, flexibility, and adaptability (Bustinza et al., 2016). Speed indicates how swiftly an organization can react to changes, flexibility illustrates the capacity to modify strategies as needed, and adaptability shows an organization's ability to learn and evolve with long-term changes (Tugkan Arici, 2023). These three factors are crucial for achieving a lasting competitive edge in an unpredictable global landscape. Theoretically, strategic decision-making agility is based on Dynamic Capabilities Theory, which highlights an organization's talent for integrating, enhancing, and reshaping both internal and external resources in response to changes in the environment (Teece, 2018). Furthermore, Strategic Management Theory also provides the foundation that effective strategic decision-making is a key determinant of organizational performance, especially under conditions of environmental turbulence. Thus, strategic decision-making agility can be viewed as an operational manifestation of dynamic capabilities in modern strategic management practices (Hunger & Wheelen, 2018). Organizational Resilience Organizational resilience refers to how well a company can handle, adjust to, and bounce back from different types of disruptions or emergencies. As noted by Duchek, it is a lively skill that helps organizations predict, manage, and grow from challenges to keep their operations running smoothly. During global crises, having strong organizational resilience is vital for a business to remain stable and sustainable (Evenseth et al., 2022). The key areas of organizational resilience include strength, flexibility, and the ability to recover (Hillmann & Guenther, 2020);(Ortiz‐de‐Mandojana & Bansal, 2015). Strength means that a company can resist the initial blow of a crisis without suffering major harm; flexibility signifies its ability to modify plans and actions based on changes in the environment, while recovery ability indicates how well a company can bounce back and return to normal, or even improve, after facing a crisis (Bhamra et al., 2011). These three dimensions complement each other in forming a resilient and sustainable organizational system. Recent literature suggests that organizational resilience is not merely reactive but also proactive, where organizations are able to anticipate potential risks and systematically prepare mitigation strategies (Williams et al., 2017). This implies that organizational resilience emerges from a mix of different internal strengths, such as strong leadership, a supportive culture, and readiness for technology (Tugkan Arici, 2023). The Connection between Agile Strategic Decisions and Organizational Resilience In theory, agility in strategic decision-making is significantly related to organizational resilience. Organizations capable of making decisions quickly and adaptively tend to be more effective in responding to environmental changes, thereby enhancing their ability to survive and recover from crises. From the viewpoint of dynamic abilities, being agile helps organizations to effectively identify, capture, and change, which in turn boosts their resilience. Multiple research efforts have revealed that strategic agility has a beneficial effect on how well organizations perform and their ability to bounce back during crises. For instance, a study by (Tugkan Arici, 2023) highlighted that firms exhibiting high agility can sustain more consistent performance when faced with uncertain conditions. However, much of the existing research centers on broad organizational performance, rather than diving deeply into organizational resilience (Zahra et al., 2022). Additionally, there is a gap in research concerning how agility impacts resilience. Not every organization that is strategically agile shows a strong level of resilience, pointing to the existence of other factors that may influence this connection. Hence, it is important to conduct studies that explore the role of variables that mediate or moderate the link between agility in strategic decision-making and organizational resilience. The Importance of Dynamic Abilities as a Middle Factor and Digital Preparedness as a Supporting Element Dynamic abilities play a crucial role in understanding how quick decision-making connects to a company's resilience. Teece (2018) defines dynamic abilities as an organization's capability to sense (recognize chances and dangers), seize (make the most of opportunities), and transform (adjust its structure and resources). Quick decision-making allows companies to enhance their dynamic abilities through fast and flexible decision-making approaches (Mikalef & Pateli, 2017). In this setting, dynamic abilities act as a mediating variable explaining how agility can translate into resilience. Organizations with high agility but lacking adequate dynamic capabilities may be unable to translate speedy decisionmaking into tangible organizational resilience. Conversely, organizations with strong dynamic capabilities can leverage agility to enhance their capacity for adaptation and recovery (Wamba et al., 2017). Additionally, the advancement of digital technology is essential in enhancing the connection between agility and resilience. Digital readiness indicates how well an organization can leverage digital tools in its business operations and decision-making (Lee et al., 2015);(Cegarra

Navarro et al., 2016). Studies indicate that organizations that are highly digitally prepared often respond better to change and show greater resilience during crises (Wamba et al., 2020). Thus, digital readiness serves as a moderating factor that boosts the link between agile strategic decision-making and organizational resilience. Hypothesis Development Strategic decision-making agility is widely recognized as an important organizational capability for responding to environmental uncertainty. Organizations that are capable of making rapid, flexible, and adaptive strategic decisions are generally better able to respond to market changes, operational disruptions, and crisis conditions. According to Dynamic Capabilities Theory, agile decision-making enables organizations to reconfigure resources and adjust strategic actions more effectively under turbulent conditions (Teece, 2018). Prior studies have also shown that strategic agility contributes positively to organizational adaptability and performance during uncertain situations (Tugkan Arici, 2023). Therefore, organizations with higher levels of strategic decisionmaking agility are expected to demonstrate stronger organizational resilience. H1: Strategic decision-making agility positively influences organizational resilience. Strategic decision-making agility is also expected to enhance dynamic capabilities. Agile organizations are typically more responsive in sensing environmental changes, identifying opportunities and threats, and reallocating resources efficiently. This responsiveness supports the development of dynamic capabilities, particularly in terms of sensing, seizing, and transforming organizational resources. Previous research suggests that agile strategic processes facilitate organizational learning and resource reconfiguration, both of which are central dimensions of dynamic capabilities (Wamba et al., 2017). Consequently, organizations with greater strategic decisionmaking agility are likely to possess stronger dynamic capabilities. H2: Strategic decision-making agility positively influences dynamic capabilities. Dynamic capabilities are considered critical for organizational resilience because they enable organizations to adapt to environmental disruptions and maintain operational continuity during crisis situations. Organizations with strong dynamic capabilities are generally more capable of reconfiguring internal resources, adjusting operational processes, and implementing strategic transformation when facing uncertainty (Duchek, 2019). Prior studies have emphasized that dynamic capabilities strengthen organizational adaptability and long-term survival under turbulent conditions (Bhamra et al., 2011). Therefore, dynamic capabilities are expected to contribute positively to organizational resilience. H3: Dynamic capabilities positively influence organizational resilience. In addition to its direct effect, strategic decision-making agility may also influence organizational resilience indirectly through dynamic capabilities. Agile decision-making alone may not automatically generate resilience unless organizations possess sufficient capabilities to transform rapid decisions into adaptive organizational actions. In this context, dynamic capabilities function as an internal organizational mechanism that translates agility into resilience outcomes. This argument is consistent with Dynamic Capabilities Theory, which emphasizes that organizational adaptability depends on the ability to integrate and reconfigure resources effectively (Teece, 2018). Thus, dynamic capabilities are expected to mediate the relationship between strategic decision-making agility and organizational resilience. H4: Dynamic capabilities mediate the relationship between strategic decision-making agility and organizational resilience. Digital readiness is expected to strengthen the relationship between strategic decision-making agility and organizational resilience. Organizations with high levels of technological preparedness are generally more capable of supporting rapid information processing, real-time communication, and data-driven decision-making. As a result, agile strategic decisions can be implemented more effectively and translated into organizational responses during crisis situations. In contrast, organizations with low digital readiness may experience difficulties in operationalizing agile decisionmaking processes due to technological limitations. Previous studies have highlighted the strategic role of digital readiness in improving organizational responsiveness and adaptability under uncertain conditions (Wamba et al., 2017). Therefore, digital readiness is expected to strengthen the positive relationship between strategic decision-making agility and organizational resilience. H5: Digital readiness moderates the relationship between strategic decision-making agility and organizational resilience, such that the relationship becomes stronger at higher levels of digital readiness.

Methods

Research Design This research employs a quantitative method using a survey to examine the cause-and-effect connections between variables laid out in the conceptual model. This quantitative method was selected because it offers unbiased measurements and enables statistical hypothesis evaluation (Ketchen, 2013). This research is explanatory, aiming to explain the causal relationships between strategic decisionmaking agility, dynamic capabilities, digital readiness, and organizational resilience (Creswell W. J. and Creswell J. D., 2014). The survey method involved distributing a structured questionnaire to respondents who had an understanding of strategic decision-making in organizations. This design was deemed appropriate for testing structural models based on Structural Equation Modeling (SEM), which require numerical data and an adequate sample size (Ketchen, 2013). Participants and Group This study focused on organizations operating in the manufacturing, logistics, and service sectors in Indonesia. These sectors were selected because they were among the industries most affected by economic disruption, particularly in relation to supply chain instability, fluctuating market demand, and accelerated digital transformation during periods of economic uncertainty (Ivanov & Dolgui, 2020). In addition, these sectors represent industries with relatively high exposure to external environmental turbulence and therefore provide an appropriate context for examining organizational resilience and strategic decision-making agility. The sampling procedure employed purposive sampling to ensure that respondents possessed relevant managerial experience and strategic responsibilities. Respondents were required to meet several criteria: (1) holding managerial or executive positions, (2) being directly involved in strategic decision-making processes, and (3) working in organizations that experienced operational or strategic disruptions during recent periods of economic uncertainty. The criterion of “organizations affected by crisis conditions” was operationalized based on organizational experiences such as declining demand, supply chain disruptions, operational adjustments, remote-work implementation, restructuring processes, or accelerated digital adaptation during periods of

economic disruption. Data were collected through an online survey distributed to managers and executives located in several major Indonesian business regions, including Jakarta, Bandung, Surabaya, Medan, and Yogyakarta. Respondents were approached through professional networks, business associations, LinkedIn professional communities, and direct organizational contacts. Initially, 312 questionnaires were distributed. A total of 271 responses were returned, resulting in a response rate of 86.9%. After the screening process, 250 responses were considered valid and usable for analysis. Responses were excluded if they were incomplete, showed inconsistent response patterns, or did not meet the respondent eligibility criteria. The final sample size of 250 respondents satisfied the recommended minimum requirements for Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis (Ketchen, 2013). The sample size also exceeded the “10-times rule,” which recommends that the minimum sample should be at least ten times the maximum number of structural paths or indicators associated with a construct in the model. Therefore, the sample was considered adequate for obtaining stable and reliable parameter estimates. Method for Gathering Data In this research, data were gathered through a questionnaire created based on variable indicators acknowledged in earlier studies. The questionnaire featured a Likert scale from 1 to 5, where the lowest score signified strong disagreement and the highest score represented strong agreement. Employing a Likert scale facilitates a numerical and uniform way to measure how respondents feel (Sekaran & Bougie, 2019). The participants in this research were managers or executives involved in making strategic decisions since they were seen as having relevant knowledge and experience regarding the variables examined. The data collection took place online (via an online survey) to widen the reach of respondents and to save time. Operationalization of Variables The variables in this study were operationalized by adopting and adapting measurement indicators from previously validated studies in the strategic management and organizational resilience literature (see table 1). Four main constructs were measured: strategic decision-making agility, organizational resilience, dynamic capabilities, and digital readiness. All questionnaire items were measured using a fivepoint Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Strategic decision-making agility was measured using 6 items adapted from (Tugkan Arici, 2023), covering dimensions such as decision-making speed, strategic flexibility, and adaptability to environmental change. A sample item is: “Our organization is able to make strategic decisions quickly in response to environmental changes.” Organizational resilience was measured using 6 items adapted from (Bhamra et al., 2011), including indicators related to robustness, adaptability, and recovery capability. One example item is: “Our organization is able to recover effectively after experiencing operational disruption.” Dynamic capabilities were measured using 6 items derived from (Teece, 2018), focusing on sensing, seizing, and transforming capabilities. A representative item is: “Our organization effectively identifies new opportunities and threats in changing market conditions.” Digital readiness was measured using 5 items adapted from (Wamba et al., 2017) and (Lee et al., 2015), including dimensions related to digital infrastructure, technological integration, and employee digital competency. An example item is: “Our organization has adequate digital infrastructure to support rapid strategic decision-making.” To ensure contextual relevance, all measurement items were adapted to the organizational and economic conditions examined in this study. The original English questionnaire items were reviewed and refined through a back-translation process to maintain conceptual equivalence and linguistic clarity. Prior to the main survey, a pilot test was conducted involving 30 managerial respondents to evaluate item clarity, readability, and content relevance. Feedback from the pilot test resulted in several minor wording revisions to improve comprehension and contextual suitability. A complete list of measurement items, construct dimensions, and literature sources is provided in Appendix A to support transparency and replication of the study. These indicators are measured using several statement items in a questionnaire that are adapted to the research context. Data Analysis Techniques Data analysis in this study was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS 4 software. PLS-SEM was selected because it is appropriate for analyzing complex structural relationships, including mediation and moderation effects, while accommodating non-normal data distributions and relatively moderate sample sizes (Ketchen, 2013) The analysis procedure consisted of two main stages: evaluation of the measurement model (outer model) and evaluation of the structural model (inner model). The measurement model assessment aimed to ensure the validity and reliability of the constructs. Convergent validity was evaluated using outer loading values and Average Variance Extracted (AVE). Outer loading values greater than 0.70 and AVE values above 0.50 were considered acceptable indicators of convergent validity (Ketchen, 2013). Construct reliability was assessed using Cronbach’s Alpha and Composite Reliability (CR), with threshold values greater than 0.70 indicating satisfactory reliability. Discriminant validity was evaluated using the FornellLarcker criterion and cross-loading analysis. According to the Fornell-Larcker criterion, the square root of the AVE for each construct should exceed the correlations between constructs, indicating that each construct is empirically distinct from the others. After confirming the adequacy of the measurement model, the structural model was assessed by examining path coefficients, t-statistics, p-values, coefficient of determination (R²), and predictive relevance (Q²). Hypothesis testing was conducted using the bootstrapping procedure with 5,000 resamples to obtain stable estimates of standard errors and significance values (Ketchen, 2013). Mediation analysis for dynamic capabilities was evaluated through the indirect effect significance test, while moderation analysis for digital readiness was tested using the interaction-term approach within the PLS-SEM framework. The coefficient of determination (R²) was used to assess the explanatory power of the model, where values of 0.25, 0.50, and 0.75 indicate weak, moderate, and substantial explanatory power, respectively (Ketchen, 2013). Predictive relevance (Q²) was also examined using the blindfolding procedure to determine the predictive capability of the structural model. In addition, the standardized root mean square residual (SRMR) was assessed as an indicator of overall model fit, with values below 0.08 indicating acceptable model fit. To minimize the potential influence of common method bias (CMB), several procedural and statistical approaches were employed. Procedurally, respondent anonymity and confidentiality were assured to reduce evaluation apprehension and social desirability bias. Statistically,

Harman’s single-factor test was conducted to examine whether a single factor accounted for the majority of variance in the data. The results showed that the first factor explained less than 50% of the total variance, indicating that common method bias was not a serious concern in this study. The formula used in reliability and validity testing is as follows: 1. Cronbach's Alpha: 2. Composite Reliability (CR): 3. Average Variance Extracted (AVE): Where is the loading factor, and (n) stands for the total number of indicators. λi Next, during the evaluation phase of the structural model, the connections between variables were examined through path coefficients, t-statistics, and p-values. To ensure reliable estimates, significance testing was carried out using the bootstrapping technique (Ketchen, 2013). Moreover, the coefficient of determination (R²) indicates how well the model explains the dependent variable, while the predictive relevance (Q²) evaluates the model's forecasting capability. The interpretation criteria for R² fall into categories of weak (0. 25), moderate (0. 50), and strong (0. 75) (Ketchen, 2013). Through this method, the study aims to deliver thorough and valid results in exploring the relationship among strategic decision-making agility, dynamic capabilities, digital readiness, and organizational resilience. Table 1. Putting Variables into Action Variables Operational Definition Indicator Source Strategic DecisionMaking Agility The organizatio n's ability to make strategic decisions quickly, flexibly and adaptively Speed of decision making; Flexibility of strategy; Adaptability to change Doz & Koson en (2010) ; Shin et al. (2023) Organiza tional Resilienc e The ability of an organizatio n to survive, adapt, and recover from a crisis Robustnes s; Adaptability ; Recovery capability Duche k (2020) ; Bhamr a et al. (2021) Dynamic Capabiliti es The organizatio n's ability to integrate and reconfigure resources to respond to change Sensing; Seizing; Transformi ng Teece (2018) Digital Readines s The level of organizatio nal readiness to adopt and utilize digital technology Digital infrastructu re; System integration; HR digital competenc y Wamb a et al. (2020)

Result and Discussion

This study involved 250 respondents from manufacturing, logistics, and service companies impacted by the global economic crisis. This sample size met the minimum criteria for PLS-SEM-based Structural Equation Modeling analysis to produce stable and reliable parameter estimates (Ketchen, 2013). Based on respondent characteristics, the majority held middle to senior managerial positions, such as operational managers, strategic managers, and senior executives. This shows that those surveyed have a good ability to grasp the strategic decision-making process happening in the organization. Looking at the different industry fields, the participants were spread out across manufacturing (40%), logistics (30%), and services (30%). Additionally, considering their work history, most respondents had over 5 years of experience (65%), suggesting they have a solid level of expertise in making strategic decisions. These traits imply that the information collected in this research is trustworthy and aligns well with the research goals. Measurement Model Test Measurement model testing was conducted to ensure that the research instrument had adequate validity and reliability. Assessment was carried out using tests for convergent validity, discriminant validity, and construct reliability.

Convergent Validity Convergent validity was assessed through outer loading values and Average Variance Extracted (AVE) (see table 2). The benchmarks applied were loading values exceeding 0. 70 and AVE greater than 0. 50 (Ketchen, 2013). The findings indicate that every indicator has loading values exceeding 0. 70 and AVE values surpassing 0. 50, allowing us to conclude that all constructs achieve convergent validity. Discriminant Validity To assess discriminant validity, the Fornell-Larcker criterion was applied, which requires that the square root of the Average Variance Extracted (AVE) for each construct exceeds its correlations with other constructs in the model (Ketchen, 2013). The constructs evaluated in this study consisted of Strategic Decision-Making Agility (SDA), Organizational Resilience (OR), Dynamic Capabilities (DC), and Digital Readiness (DR). The results demonstrate that the square root of AVE for each construct was higher than the corresponding inter-construct correlations, indicating satisfactory discriminant validity and confirming that each construct represents a distinct conceptual domain (see table 3). The corrected construct labels ensure consistency between the conceptual framework, hypotheses, measurement model, and empirical analysis presented throughout the study. Reliability Assessment The reliability of the construct was evaluated through Cronbach's Alpha and Composite Reliability (CR), where the standard is greater than 0. 70 (see table 4). The findings indicate that every variable has a value greater than 0. 70, allowing us to determine that the research tool is dependable. Structural Model Assessment The assessment of the structural model is done by examining the R², Q², Goodness of Fit (GoF) values, and hypothesis testing.

Coefficient of Determination (R²) The R² value a portion of the differences in how organizations endure challenges can be accounted for by the factors in the model, whereas the rest is affected by elements not included in this study (see table 5). Predictive Relevance (Q²) Predictive relevance (Q²) was assessed using the blindfolding procedure in SmartPLS 4 to evaluate the predictive capability of the structural model. According to (Ketchen, 2013), Q² values greater than zero indicate that the model has predictive relevance for a particular endogenous construct. The Q² statistic was calculated using the following formula: Q² = 1 − (1 − R₁²) (1 − R₂²) where R₁² and R₂² represent the coefficient of determination values for the endogenous constructs included in the model. Based on the R² values obtained in this study, the predictive relevance value was calculated as follows: Q² = 1 − (1 − 0.62) (1 − 0.55) Q² = 1 − (0.38 × 0.45) Q² = 1 − 0.171 Q² = 0.829 The Q² value of 0.829 indicates that the structural model has strong predictive relevance, meaning that the exogenous variables included in the model are capable of adequately predicting the endogenous constructs. Furthermore, separate blindfolding results for each endogenous construct also produced Q² values above zero, specifically 0.39 for Organizational Resilience and 0.34 for Dynamic Capabilities, further confirming the predictive capability of the model. Table 2. Outcomes of Convergent Validity Assessment Variables Loading Factor AVE Information Strategic DecisionMaking Agility 0.72 – 0.88 0.65 Valid Organizational Resilience 0.70 – 0.86 0.63 Valid Dynamic Capabilities 0.73 – 0.89 0.67 Valid Digital Readiness 0.71 – 0.87 0.64 Valid Table 3. Discriminant Validity Assessment (Fornell-Larcker Criterion) Variables SDA OR DC DR Strategic Decision-Making Agility (SDA) 0.81 Organizational Resilience (OR) 0.65 0.79 Dynamic Capabilities (DC) 0.68 0.70 0.82 Digital Readiness (DR) 0.60 0.66 0.69 0.80 Table 4. Results of the Reliability Assessment Variables Cronbach's Composite Information Alpha Reliability Natural Resources 0.88 0.91 Reliable OR 0.87 0.90 Reliable DC 0.89 0.92 Reliable DR 0.86 0.90 Reliable Table 5. R² Value Dependent Variable R² Category Organizational Resilience 0.62 Moderate-Strong Dynamic Capabilities 0.55 Moderate

Model Fit Evaluation In line with current recommendations in PLS-SEM reporting, this study did not rely solely on the global Goodness of Fit (GoF) index to evaluate model adequacy. Recent methodological literature suggests that the GoF index has limitations in assessing overall model fit in variance-based SEM and therefore should be interpreted cautiously (Ketchen, 2013). Consequently, this study prioritized several commonly accepted PLS-SEM evaluation criteria, including the Standardized Root Mean Square Residual (SRMR), coefficient of determination (R²), predictive relevance (Q²), collinearity assessment, and bootstrapped path significance. The SRMR value obtained in this study was 0.071, which is below the recommended threshold of 0.08, indicating an acceptable level of model fit. In addition, the structural model demonstrated moderate to substantial explanatory power, with R² values of 0.62 for Organizational Resilience and 0.55 for Dynamic Capabilities. The predictive relevance assessment also showed satisfactory results, with Q² values greater than zero for all endogenous constructs, indicating adequate predictive capability. Furthermore, collinearity assessment using Variance Inflation Factor (VIF) values showed that all indicators and structural paths were below the recommended threshold of 5.0, suggesting that multicollinearity was not a significant concern in the model. Finally, the significance of all hypothesized relationships was confirmed through the bootstrapping procedure, supporting the structural relationships proposed in the conceptual framework. Taken together, these results indicate that the proposed structural model demonstrates acceptable explanatory and predictive capability according to current PLS-SEM evaluation standards. Testing Hypotheses Hypothesis testing was conducted using the bootstrapping procedure in SmartPLS 4 with 5,000 resamples. The significance of the structural relationships was evaluated based on path coefficients (β), t-statistics, and p-values. Following the recommended decision criteria in PLSSEM analysis, hypotheses were considered significant when the t-statistic exceeded 1.96 and the p-value was below 0.05 (Ketchen, 2013). The results indicate that all proposed hypotheses were statistically supported (see table 6). Among the direct relationships, the strongest path coefficient was observed in the relationship between Strategic Decision-Making Agility and Dynamic Capabilities (β = 0.68), suggesting that agile strategic decision-making substantially strengthens the organization’s ability to sense opportunities, reconfigure resources, and adapt to environmental changes. This finding highlights the critical role of strategic agility as a driver of organizational capability development. The relationship between Dynamic Capabilities and Organizational Resilience also demonstrated a relatively strong positive effect (β = 0.40), indicating that organizations Table 6. Results of Hypothesis Testing Hypothesis Path Coefficient (β) H1: Strategic Decision-Making Agility (SDA) → Organizational Resilience (OR) H2: Strategic Decision-Making Agility (SDA) → Dynamic Capabilities (DC) H3: Dynamic Capabilities (DC) → Organizational Resilience (OR) H4: Strategic Decision-Making Agility (SDA) → Dynamic Capabilities (DC) → Organizational Resilience (OR) 0.27 4.21 0.000 Partial mediation supported H5: Digital Readiness (DR) × Strategic Decision-Making Agility (SDA) → Organizational Resilience (OR)

with stronger adaptive and resource-reconfiguration capabilities tend to exhibit greater resilience under conditions of uncertainty. Meanwhile, the direct effect of Strategic Decision-Making Agility on Organizational Resilience (β = 0.35) remained significant, although its magnitude was lower than the effect on Dynamic Capabilities. This result suggests that agility contributes to resilience both directly and indirectly through internal organizational capabilities. The mediation analysis revealed that Dynamic Capabilities significantly mediated the relationship between Strategic Decision-Making Agility and Organizational Resilience (β = 0.27; p < 0.001). Because both the direct effect (H1) and indirect effect (H4) were statistically significant, the mediation can be categorized as partial mediation rather than full mediation. This finding indicates that organizational resilience is influenced not only by agile decision-making itself but also by the organization’s capability to transform agile decisions into adaptive strategic actions. Furthermore, Digital Readiness significantly moderated the relationship between Strategic Decision-Making Agility and Organizational Resilience (β = 0.18; p = 0.003). Although the moderating effect size was smaller than the main direct effects, the result remains practically meaningful. Organizations with stronger technological preparedness were better able to translate agile decision-making into resilient organizational outcomes. This finding suggests that digital infrastructure, technological integration, and employee digital competency strengthen the effectiveness of strategic agility during periods of economic uncertainty. Overall, the structural model demonstrated satisfactory explanatory and predictive capability, supported by R² values of 0.62 for Organizational Resilience and 0.55 for Dynamic Capabilities, as well as Q² values above zero for all endogenous constructs. In addition, the SRMR value of 0.071 indicated acceptable model fit according to current PLS-SEM reporting standards. The results indicate that being able to make agile strategic decisions positively affects an organization's resilience. This means that businesses that can quickly and flexibly adapt their choices are generally more robust during global challenges. This supports the dynamic capabilities theory, which highlights that an organization's ability to adapt is essential for managing unpredictable environments (Teece, 2018). Additionally, agility in strategic decision-making also significantly impacts dynamic capabilities, suggesting that this agility is vital for improving how organizations manage and adjust their resources. Furthermore, dynamic capabilities are shown to greatly influence organizational resilience, underscoring their role as an internal mechanism that boosts resilience. Moreover, the analysis reveals that dynamic capabilities partially mediate the connection between agility and resilience. This indicates that part of the way agility affects resilience is through enhancing an organization's dynamic capabilities. This insight significantly contributes to understanding how these variables relate, which was not well-explored in existing tvalue pvalue Decision 0.35 4.85 0.000 Supported 0.68 9.12 0.000 Supported 0.40 5.67 0.000 Supported 0.18 2.95 0.003 Moderation supported literature. Additionally, it has been found that digital readiness moderates the connection between agile strategic decision-making and an organization's resilience. Organizations that are technologically prepared tend to effectively use agility in their decisions to boost resilience. This highlights the strategic importance of digital technology in enhancing organizational skills in managing crises (Wamba et al., 2017). In summary, this study confirms that the interplay of strategic decision-making agility, dynamic capabilities, and digital readiness is crucial for bolstering organizational resilience in the current global economic crisis. The findings of this study provide important empirical evidence regarding the interplay between strategic decisionmaking agility, dynamic capabilities, digital readiness, and organizational resilience during periods of economic uncertainty. More importantly, the results not only confirm the proposed hypotheses, but also offer deeper theoretical and contextual insights into how organizations in developing economies build resilience under crisis conditions. Interpretation of Results in Relation to Theory and Prior Studies The results demonstrate that strategic decision-making agility has a significant positive effect on organizational resilience (β = 0.35; t = 4.85). This finding confirms the central argument of Dynamic Capabilities Theory that organizations capable of responding rapidly and flexibly to environmental change are more likely to sustain performance and survive under turbulent conditions (Teece, 2018). However, the contribution of this study goes beyond merely confirming prior theory. The magnitude of the direct effect suggests that agility alone is insufficient to fully explain resilience outcomes, indicating that resilience emerges not only from fast decision-making but also from how organizations transform decisions into adaptive organizational actions. This finding partially differs from several earlier studies that treated strategic agility primarily as a direct predictor of organizational performance without examining the internal capability-building process underlying resilience formation (Zahra et al., 2022). While previous studies largely emphasized operational flexibility or market responsiveness, the present study demonstrates that the relationship between agility and resilience becomes substantially stronger when dynamic capabilities are incorporated into the explanatory mechanism. This indicates that organizational resilience is not simply the consequence of rapid reactions, but rather the outcome of an organization’s ability to continuously reconfigure resources and organizational routines during crisis situations. The effect of strategic decision-making agility on dynamic capabilities was particularly strong (β = 0.68; t = 9.12), representing the strongest relationship in the structural model. This finding provides important theoretical insight because it empirically supports the proposition that agility functions as an activating mechanism for dynamic capabilities. In other words, agile decision-making enables organizations to more effectively sense environmental threats, seize strategic opportunities, and transform internal resources. Prior studies have acknowledged the relationship between agility and capability development (Wamba et al., 2017), yet empirical evidence explaining how agility operationally stimulates dynamic capability formation remains relatively limited, particularly in developing-country contexts. Furthermore, the significant influence of dynamic capabilities on organizational resilience (β = 0.40; t = 5.67) reinforces the capability-based perspective proposed by (Teece, 2018) and (Duchek, 2019). Nevertheless, this study extends existing literature by showing that during periods of

economic disruption, dynamic capabilities serve not merely as strategic resources, but as organizational stabilization mechanisms that enable firms to maintain operational continuity amid uncertainty. This contextual insight is important because prior studies conducted in developed economies often emphasized innovation performance or competitive advantage rather than resilience during crisis conditions. Another important contribution emerges from the mediation analysis. The results reveal that dynamic capabilities partially mediate the relationship between strategic decisionmaking agility and organizational resilience (β = 0.27; t = 4.21). The partial mediation structure indicates that agility influences resilience both directly and indirectly through capability development. This finding advances Dynamic Capabilities Theory by clarifying the internal mechanism through which agile strategic decisions are translated into resilience outcomes. Previous studies generally assumed a direct relationship between agility and performance outcomes, whereas this study demonstrates that organizational capabilities constitute a critical transmission mechanism linking strategic responsiveness to organizational survival (Akhtar et al., 2018). The moderating effect of digital readiness (β = 0.18; t = 2.95) also provides an important contextual extension to prior theory. Although earlier studies suggested that digital transformation supports organizational adaptability (Warner & Wäger, 2019);(Wamba et al., 2017), this study demonstrates that digital readiness functions as a boundary condition strengthening the effectiveness of agile strategic decisionmaking. Specifically, organizations with higher technological preparedness were more capable of converting agile decisions into resilient organizational responses. This finding suggests that in highly uncertain environments, agility becomes more effective when supported by digital infrastructures, integrated information systems, and digitally competent human resources. Thus, this study extends Dynamic Capabilities Theory by introducing digital readiness as an enabling contextual factor that amplifies the impact of strategic agility on resilience. This represents a meaningful theoretical contribution because prior Dynamic Capabilities literature has rarely positioned digital readiness as a moderating condition in resilience-building processes. Why Strategic Decision-Making Agility Strengthens Organizational Resilience The empirical findings suggest that strategic decisionmaking agility strengthens organizational resilience through several interconnected organizational mechanisms. First, agility allows organizations to respond more rapidly to environmental disruptions, thereby minimizing operational delays and strategic uncertainty. The significant direct effect identified in this study empirically confirms that organizations capable of accelerating strategic responses are better positioned to maintain stability during crises. Second, agility enhances adaptive capacity through dynamic capability development. This mechanism is strongly reflected in the substantial path coefficient between strategic decision-making agility and dynamic capabilities (β = 0.68). The result indicates that agile organizations are more effective in identifying emerging threats, reallocating resources, and redesigning organizational processes. Therefore, agility does not merely accelerate decisions; it also strengthens organizational learning and transformation capabilities. Third, agile organizations appear more capable of institutionalizing continuous learning processes during uncertain conditions. The significant mediating role of dynamic capabilities indicates that organizational resilience is developed through iterative adaptation processes rather than through temporary reactive actions alone. This finding supports the argument of (Duchek, 2019) that resilience is a dynamic and developmental capability rather than a static organizational characteristic. Importantly, the present study reveals that these mechanisms become substantially stronger when organizations possess high levels of digital readiness. The moderating effect identified in this research indicates that digital technologies facilitate faster information processing, improve strategic coordination, and enhance organizational responsiveness. Consequently, digital readiness acts as a strategic enabler that transforms agility into more effective resilience outcomes. Theoretical and Practical Contributions This study contributes to strategic management literature in several important ways. First, it extends Dynamic Capabilities Theory by empirically demonstrating that strategic decision-making agility functions as an antecedent that activates dynamic capabilities, which subsequently strengthen organizational resilience. This clarifies the internal capability-building mechanism that has received limited empirical attention in previous studies. Second, the study introduces digital readiness as a significant boundary condition in the relationship between agility and resilience. This contextual extension is particularly relevant in the current era of digital transformation, where technological preparedness increasingly determines organizational survival during crises. Third, the study contributes contextual evidence from a developing-country setting. Most prior studies on organizational resilience and dynamic capabilities have been conducted in developed economies characterized by relatively stable institutional and technological infrastructures. By focusing on Indonesia, this study demonstrates that agility, capability development, and digital readiness become even more critical under conditions characterized by institutional uncertainty and economic volatility. From a managerial perspective, the findings imply that organizations should not focus solely on accelerating decision-making speed. Instead, managers must simultaneously invest in dynamic capability development and digital readiness to ensure that agile decisions can be translated into sustainable resilience outcomes. Organizations that merely react quickly without strengthening adaptive capabilities and technological preparedness may struggle to maintain long-term resilience during prolonged crises. The Importance of Dynamic Skills and Technology During a Crisis The findings of this study indicate that dynamic capabilities mediate the relationship between strategic decision-making agility and organizational resilience. This suggests that the influence of agility on resilience occurs not only directly, but also through the organization’s internal capability to manage, integrate, and reconfigure resources during periods of uncertainty. In this regard, the results are generally consistent with prior studies that have emphasized the mediating role of dynamic capabilities in translating strategic agility into organizational outcomes (Teece, 2018);(Wamba et al., 2017). Therefore, the present study does not propose a fundamentally new theoretical mechanism, but rather provides additional empirical confirmation within the context of economic uncertainty in a developing-country environment. More specifically, the findings support the capabilitybased perspective that organizations with stronger dynamic capabilities are better able to transform agile strategic responses into sustainable resilience outcomes. Dynamic capabilities enable organizations to: (1) proactively identify

opportunities and threats (sensing), (2) make and implement strategic responses effectively (seizing), and (3) continuously transform organizational resources and processes (transforming) (Teece, 2018).The significance of the mediation effect in this study suggests that agile decision-making alone may not be sufficient to enhance resilience unless it is accompanied by the organization’s capability to operationalize and reconfigure strategic resources effectively. Importantly, this study contributes contextual enrichment to the existing literature by demonstrating that the mediating role of dynamic capabilities remains relevant in organizations operating under higher levels of economic volatility and institutional uncertainty, such as those commonly experienced in developing economies. This contextual evidence strengthens previous findings by showing that dynamic capabilities continue to function as a critical organizational mechanism for resilience even in highly turbulent environments. Additionally, being ready for digital tools has been found to enhance the connection between agility and toughness. In today's tech-driven world, technology is vital for speeding up and refining decision-making. Companies that are wellprepared technologically can utilize real-time information, enhance teamwork within their organization, and respond more quickly to changes in their environment (Wamba et al., 2017). Therefore, the connections among the variables studied can be viewed as a unified system where agility, dynamic capabilities, and digital readiness support one another in boosting organizational resilience. This study provides several significant contributions to the field of strategic management literature. Firstly, it effectively combines the ideas of strategic decision-making agility and organizational resilience into one comprehensive framework. This blend enriches the existing literature, which has often treated the two concepts in isolation. Secondly, it highlights the importance of dynamic capabilities as a mediating factor between agility and resilience. These results offer fresh empirical support for the theory of dynamic capabilities, especially in light of the global crisis. Lastly, this research broadens the literature by incorporating digital readiness as a moderating variable. This introduces a new viewpoint on how technology boosts the link between agility and resilience, an area that has been somewhat underexplored in prior studies. From a practical viewpoint, this research presents several key insights for managers and decision-makers. First, organizations need to improve strategic decision-making agility by streamlining decision-making processes, improving cross-functional coordination, and leveraging information technology. Second, organizations need to develop dynamic capabilities as a key foundation for crisis management. This can be achieved through investments in human resource development, increased innovation capacity, and strengthening an adaptive organizational culture. Third, organizations need to improve their digital readiness by adopting relevant digital technologies, such as data-driven information systems, predictive analytics, and digital collaboration platforms. This way, organizations can improve the speed and quality of strategic decision-making. Strategically, companies hoping to survive the global crisis need to adopt an integrated approach that combines agility, dynamic capabilities, and technology. The combination of these three factors will produce organizations that can not only survive but also thrive amidst uncertainty. Despite the important contributions of this study, several limitations should be acknowledged when interpreting the findings. First, this study employed a cross-sectional quantitative survey design, meaning that the data were collected at a single point in time. As a result, the findings primarily capture organizational conditions during a specific period of economic uncertainty and do not fully explain how organizational resilience evolves dynamically over time. Although the structural relationships identified in this study are statistically significant, the cross-sectional nature of the data limits the ability to establish strong causal inferences regarding the long-term interaction between strategic decision-making agility, dynamic capabilities, and organizational resilience. Second, the study relied heavily on self-reported perceptions from managerial respondents. While managers were selected because they possess strategic knowledge relevant to the constructs examined, the use of single-source perceptual data increases the possibility of common-method bias and subjective evaluation bias. Respondents may have overestimated their organizations’ agility, resilience, or digital readiness due to social desirability or organizational image considerations. Consequently, the relationships observed in this study should be interpreted with caution, particularly regarding the magnitude of the effects identified in the structural model. Third, the use of purposive sampling and the concentration on manufacturing, logistics, and service industries may limit the generalizability of the findings. Organizations operating in other sectors may exhibit different strategic responses, technological capabilities, or resilience patterns under crisis conditions. In addition, because the study was conducted within a developing-country context, the findings may not be fully transferable to organizations in developed economies that operate under different institutional, technological, and regulatory environments. The contextual characteristics of emerging economies, such as higher environmental volatility and uneven digital infrastructure, may have strengthened the relationships identified in this study. Fourth, the model examined in this study focused primarily on strategic decision-making agility, dynamic capabilities, digital readiness, and organizational resilience. Although these variables explained a substantial proportion of variance in organizational resilience (R² = 0.62), the model does not fully capture the broader organizational and behavioral factors that may also shape resilience outcomes. Variables such as transformational leadership, organizational culture, innovation orientation, knowledge management capability, and crisis governance mechanisms may provide additional explanatory power and potentially alter the relationships identified in this study. These limitations also open important opportunities for future theoretical development. Future research should adopt longitudinal designs to examine how organizational resilience develops and changes throughout different stages of prolonged crises. Such an approach would allow researchers to identify temporal capability-building processes and generate stronger causal explanations regarding the interaction between agility and resilience. In addition, future studies could employ multi-source and mixed-method approaches by combining survey data with interviews, archival organizational data, or longitudinal case studies (Aberdeen, 2013). This would reduce the risk of common-method bias while also generating richer theoretical insights into the organizational mechanisms underlying resilience formation. Cross-country comparative studies would also provide valuable opportunities to extend Dynamic Capabilities Theory by identifying contextual boundary conditions across different institutional and technological environments. Such studies may reveal whether the role of agility, dynamic capabilities, and digital readiness differs between emerging and developed economies, thereby contributing more original theoretical explanations beyond the current model. Finally, future research could extend this framework by incorporating additional strategic and behavioral variables,

such as leadership adaptability, organizational learning orientation, innovation capability, and crisis management maturity. Integrating these variables may help develop a more comprehensive resilience framework and uncover alternative mechanisms through which organizations sustain performance under extreme uncertainty. In this way, future studies can move beyond confirming existing theory toward generating more novel theoretical contributions regarding organizational adaptation and resilience in increasingly volatile business environments.

Conclusion

This study examined the relationships among strategic decision-making agility, dynamic capabilities, digital readiness, and organizational resilience during conditions of economic uncertainty using a PLS-SEM approach. The findings indicate that strategic decision-making agility is positively associated with organizational resilience, while dynamic capabilities partially mediate this relationship. In addition, digital readiness strengthens the relationship between agility and resilience, suggesting that technological preparedness enhances the effectiveness of adaptive strategic responses. The findings contribute to the strategic management literature by providing empirical validation of the interconnected roles of agility, dynamic capabilities, and digital readiness within a single integrated framework. Rather than introducing a new theory, this study extends the application of Dynamic Capabilities Theory by demonstrating how capability development and digital preparedness jointly support organizational resilience in a developing-country context. This contextual contribution is important because prior studies have predominantly focused on organizations in developed economies and have often examined these constructs separately. The mediation results suggest that organizational resilience is not solely associated with rapid decision-making, but also with the organization’s ability to reconfigure resources and adapt internal processes during periods of disruption. Furthermore, the moderating role of digital readiness indicates that agility becomes more effective when supported by technological infrastructure and digital competencies. These findings refine current understanding by highlighting that resilience emerges from the interaction between strategic responsiveness, organizational capabilities, and technological preparedness. From a managerial perspective, the results imply that organizations should not focus exclusively on accelerating strategic decisions. Instead, firms should simultaneously strengthen adaptive capabilities and improve digital readiness to enhance resilience under uncertain conditions. In particular, the strong relationship between strategic agility and dynamic capabilities suggests that organizations benefit from embedding learning, resource reconfiguration, and crossfunctional coordination into their strategic processes. This study has several limitations. The cross-sectional survey design limits the ability to infer causal relationships over time, and the use of purposive sampling within selected industries may reduce generalizability to other organizational contexts. Future studies are therefore encouraged to employ longitudinal or comparative cross-country designs to better examine how organizational resilience evolves across different institutional and technological environments.

Author Contributions

All authors contributed substantially to the development of this study. The conceptual framework integrating strategic decisionmaking agility, dynamic capabilities, digital readiness, and organizational resilience was collaboratively developed by the authors. The first and second authors were primarily responsible for data collection, statistical analysis, and SEMPLS model evaluation. The third author contributed to the theoretical development and literature synthesis, particularly regarding Dynamic Capabilities Theory and organizational resilience. The fourth author contributed to methodological validation, interpretation of findings, and critical revision of the manuscript to strengthen the study’s theoretical positioning and empirical contribution. All authors reviewed References Aberdeen, T. (2013). Yin, R. K. (2009). Case study research: Design and methods (4th Ed.). Thousand Oaks, CA: Sage. The Canadian Journal of Action Research, 14(1), 69–71. https://doi.org/10.33524/cjar.v14i1.73 Akhtar, P., Khan, Z., Tarba, S., & Jayawickrama, U. (2018). The Internet of Things, dynamic data and information processing capabilities, and operational agility. Technological Forecasting and Social Change, 136, 307–316. https://doi.org/10.1016/j.techfore.2017.04.023 Arbale, H., & Mutisya, D. N. (2024). Book Review: “Research Methods for Business Students” (Eighth Edition) by Mark N. K. Saunders, Philip Lewis, and Adrian Thornhill (Pearson Education, 2019). African Quarterly Social Science Review, 1(2), 8–21. https://doi.org/10.51867/aqssr.1.2.2 Barney, J. B. (2011). Establishing strategic management as an academic discipline. In Competition, Competitive Advantage, and Clusters (pp. 25– 31). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199578030.003.0002 Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375–5393. https://doi.org/10.1080/00207543.2011.563826 Bresciani, S., Ferraris, A., & Del Giudice, M. (2018). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136, 331–338. https://doi.org/10.1016/j.techfore.2017.03.002 Bustinza, O. F., Vendrell-Herrero, F., Perez-Arostegui, M., & Parry, G. (2016). Technological capabilities, resilience capabilities and organizational effectiveness. The International Journal of Human Resource Management, 30(8), 1370–1392. https://doi.org/10.1080/09585192.2016.1216878 Cegarra-Navarro, J.-G., Soto-Acosta, P., & Wensley, A. K. P. (2016). Structured knowledge processes and firm performance: The role of organizational agility. Journal of Business Research, 69(5), 1544–1549. https://doi.org/10.1016/j.jbusres.2015.10.014 Creswell W. J. and Creswell J. D. (2014). Qualitative, quantitative, and mixed methods approaches. Research Design Qualitative Quantitative and Mixed Methods Approaches. In Research Design (5th ed., Vol. 5, Number June). SAGE Publications. Duchek, S. (2019). Organizational resilience: a capability-based conceptualization. Business Research, 13(1), 215–246. https://doi.org/10.1007/s40685-019-0085-7 Eddy, P. L., & Boggs, G. R. (2010). Community College Leadership A Multidimensional Model for Leading Change (1st Editio). Taylor & Francis Group. https://doi.org/https://doi.org/10.4324/9781003443506 Evenseth, L. L., Sydnes, M., Sydnes, M., Gausdal, A. H., & Gausdal, A. H. (2022). Building Organizational Resilience Through Organizational Learning: A Systematic Review. Frontiers in Communication Disaster Communications, 7. https://doi.org/https://doi.org/10.3389/fcomm.2022.837386 Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2020). A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change. Journal of Management Studies, 58(5), 1159–1197. https://doi.org/10.1111/joms.12639 Hillmann, J., & Guenther, E. (2020). Organizational Resilience: A Valuable Studies. Information Systems Journal, 58, 583. Construct for Management Research? International Journal of

and approved the final version of the manuscript.

Acknowledgements

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Guenther, E. (2020). Organizational Resilience: A Valuable Studies. Information Systems Journal, 58, 583. Construct for Management Research? International Journal of and approved the final version of the manuscript. Acknowledgements The writers would like to thank all the participants for their time and valuable feedback during this research. They also appreciate the support of the affiliated institutions in making this research possible. Management Reviews, 23(1), 7–44. https://doi.org/10.1111/ijmr.12239

Wheelen, T. L. (2018). A performance appraisal of undergraduate business education. Human Resource Management, 19(1), 24–31. https://doi.org/10.1002/hrm.3930190106

Uncertainty, and Leverage. Economic

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Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning &amp; Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

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Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA. Journal of Business

Research, 70, 1–16. https://doi.org/10.1016/j.jbusres.2016.09.004 Ortiz‐de‐

Bansal, P. (2015). The long‐term benefits of organizational resilience through sustainable business practices. Strategic Management Journal, 37(8), 1615–1631. https://doi.org/10.1002/smj.2410

Pham, L. T. T. (2024). Work Readiness of Graduates in the Digital Age: A Literature Review. Ho Chi Minh City Open University Journal of Science - Social Sciences, 14(2), 120–128. https://doi.org/10.46223/HCMCOUJS.soci.en.14.2.2820.2024

Bougie, R. (2019). Research methods for business: A skillbuilding approach (8th ed.). John

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007 Tugkan

Arici, and M. S. G. (2023). Examining Environmental Turbulence Intensity: A Strategic Agility and Innovativeness Approach on Firm Performance in Environmental Turbulence Situations. 15(6), 5364. https://doi.org/https://doi.org/10.3390/su15065364

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business

Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001

Zhao, E. Y. (2017). Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams. Academy of Management Annals, 11(2), 733–769. https://doi.org/10.5465/annals.2015.0134

Luo, Y. (2022). Journal of International Business