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<h2>Introduction</h2><p>The rapid advancement of digital technologies has fundamentally altered the competitive landscape across industries. Organizations are increasingly adopting digital transformation (DT) initiatives—such as cloud computing, big data analytics, and the Internet of Things (IoT)—to improve operational efficiency, enhance customer experiences, and create new business models (Vial, 2019). However, the ultimate goal of DT is not merely technological adoption but the development of organizational agility, defined as the capability to sense and respond swiftly to environmental changes (Teece, Peteraf, & Leih, 2016). Agility enables firms to reconfigure resources and processes in response to market shifts, thereby sustaining competitive advantage.</p><p>While the positive correlation between DT and agility has been suggested in prior literature (Warner & Wäger, 2019), the underlying mechanisms remain ambiguous. Specifically, how does DT translate into agility? One potential pathway is through enhanced knowledge management (KM) capabilities—the ability to acquire, share, and apply knowledge effectively (Gold, Malhotra, & Segars, 2001). DT facilitates the collection and dissemination of vast amounts of data, which, when properly managed, can improve decision-making and responsiveness. Yet, empirical evidence on this mediating role is scarce.</p><p>Moreover, the effectiveness of DT in fostering agility may depend on the external environment. In highly dynamic markets, characterized by rapid technological changes and shifting customer preferences, the need for agility is acute, and DT may be more critical. Conversely, in stable environments, the impact might be less pronounced. This suggests a moderating role of environmental dynamism (Jansen, Van Den Bosch, & Volberda, 2006).</p><p>This study addresses these gaps by examining the following research questions: (1) To what extent does digital transformation influence organizational agility in SMEs? (2) Does knowledge management capability mediate this relationship? (3) Does environmental dynamism moderate the direct effect of DT on agility? By focusing on SMEs in the manufacturing sector, we respond to calls for more research on DT in smaller firms, which often face resource constraints but are vital to economic growth (Moeuf et al., 2018).</p><p>The remainder of this paper is structured as follows: Section 2 reviews relevant literature and develops hypotheses. Section 3 describes the methodology. Section 4 presents the results. Section 5 discusses the findings, and Section 6 concludes with implications and limitations.</p><h2>Methods</h2><h3>Research Design</h3><p>This study employed a sequential explanatory mixed-methods design. The quantitative phase involved a cross-sectional survey to test the hypothesized relationships, followed by a qualitative phase to provide deeper insights into the quantitative findings. This approach allows for triangulation and a richer understanding of the phenomena (Creswell & Plano Clark, 2017).</p><h3>Sample and Data Collection</h3><p>The target population consisted of manufacturing SMEs (10-250 employees) in the United States, identified through the Dun & Bradstreet database. A stratified random sampling method was used to ensure representation across sub-sectors (e.g., electronics, machinery, textiles). An online questionnaire was distributed to senior managers (e.g., CEOs, operations managers) who had knowledge of their firm's digital initiatives and strategic orientation. After two follow-up reminders, 312 usable responses were obtained, yielding a response rate of 31.2%. The sample had an average firm size of 87 employees (SD = 54) and an average firm age of 22 years (SD = 12).</p><p>For the qualitative phase, 20 semi-structured interviews were conducted with managers from a purposive subset of survey respondents, selected to represent varying levels of DT adoption and agility. Interviews lasted 45-60 minutes and were audio-recorded and transcribed verbatim.</p><h3>Measures</h3><p>All constructs were measured using validated scales from prior literature, adapted to the context. Responses were on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree).</p><p><strong>Digital transformation</strong> was assessed using a 6-item scale developed by Verhoef et al. (2021), capturing the extent of adoption of digital technologies in operations, customer interface, and business models. A sample item is "Our firm extensively uses digital technologies to automate processes."</p><p><strong>Organizational agility</strong> was measured using a 5-item scale from Lu and Ramamurthy (2011), reflecting both market-sensing and operational adjustment capabilities. A sample item is "Our firm can quickly respond to changes in customer demands."</p><p><strong>Knowledge management capabilities</strong> were measured using a 7-item scale from Gold et al. (2001), covering knowledge acquisition, conversion, and application. A sample item is "Our firm has processes for acquiring knowledge about our customers."</p><p><strong>Environmental dynamism</strong> was measured using a 4-item scale from Jansen et al. (2006), assessing the rate of change in the external environment. A sample item is "The environment in our industry changes rapidly."</p><p>Control variables included firm size (number of employees), firm age, and industry type (dummy coded).</p><h3>Data Analysis</h3><p>Quantitative data were analyzed using SPSS 26 and AMOS 26. First, confirmatory factor analysis (CFA) was conducted to assess the measurement model's validity and reliability. Then, structural equation modeling (SEM) was used to test the hypothesized paths, including the mediation and moderation effects. The bootstrap method (5,000 resamples) was used to test indirect effects. For moderation, we created an interaction term (DT × dynamism) and included it in the model.</p><p>Qualitative data were analyzed using thematic analysis (Braun & Clarke, 2006). Transcripts were coded inductively, and themes were identified that related to the mechanisms of DT's impact on agility and the role of the environment.</p><h2>Results</h2><h3>Measurement Model</h3><p>The CFA results indicated a good fit for the four-factor model (χ²/df = 1.82, CFI = 0.96, TLI = 0.95, RMSEA = 0.05). All factor loadings were significant and above 0.60. Composite reliabilities (CR) ranged from 0.82 to 0.91, and average variance extracted (AVE) ranged from 0.55 to 0.68, supporting convergent validity. Discriminant validity was confirmed as the square root of AVE for each construct exceeded its correlations with other constructs (Fornell & Larcker, 1981).</p><h3>Descriptive Statistics and Correlations</h3><p>Table 1 presents means, standard deviations, and correlations. Digital transformation was positively correlated with organizational agility (r = 0.52, p < 0.01) and knowledge management capabilities (r = 0.48, p < 0.01). Knowledge management capabilities were also positively correlated with agility (r = 0.61, p < 0.01).</p><p><em>Table 1: Descriptive Statistics and Correlations</em></p><table border="1" cellpadding="5"><tbody><tr><th>Variable</th><th>Mean</th><th>SD</th><th>1</th><th>2</th><th>3</th><th>4</th></tr><tr><td>1. Digital Transformation</td><td>4.82</td><td>1.23</td><td>1</td><td></td><td></td><td></td></tr><tr><td>2. Knowledge Management</td><td>5.01</td><td>1.10</td><td>0.48**</td><td>1</td><td></td><td></td></tr><tr><td>3. Organizational Agility</td><td>5.14</td><td>1.05</td><td>0.52**</td><td>0.61**</td><td>1</td><td></td></tr><tr><td>4. Environmental Dynamism</td><td>4.56</td><td>1.31</td><td>0.21**</td><td>0.18**</td><td>0.25**</td><td>1</td></tr><tr><td>Note: ** p < 0.01</td></tr></tbody></table><h3>Hypothesis Testing</h3><p>The structural model demonstrated good fit (χ²/df = 2.01, CFI = 0.95, TLI = 0.94, RMSEA = 0.06). The results are summarized in Table 2.</p><p><em>Table 2: Structural Model Results</em></p><table border="1" cellpadding="5"><tbody><tr><th>Path</th><th>Standardized β</th><th>t-value</th><th>p-value</th></tr><tr><td>DT → Agility (direct)</td><td>0.28</td><td>4.12</td><td><0.001</td></tr><tr><td>DT → KM</td><td>0.48</td><td>7.56</td><td><0.001</td></tr><tr><td>KM → Agility</td><td>0.42</td><td>6.23</td><td><0.001</td></tr><tr><td>DT × Dynamism → Agility</td><td>0.15</td><td>2.89</td><td>0.004</td></tr></tbody></table><p>The direct effect of DT on agility was significant (β = 0.28, p < 0.001), supporting Hypothesis 1. The indirect effect via KM was also significant (indirect effect = 0.20, 95% CI [0.12, 0.29]), indicating partial mediation, thus supporting Hypothesis 2. The interaction term was significant (β = 0.15, p = 0.004), suggesting that environmental dynamism moderates the DT-agility relationship. Simple slope analysis revealed that the effect of DT on agility was stronger for firms in high dynamism environments (β = 0.43, p < 0.001) compared to low dynamism (β = 0.13, p = 0.08). Thus, Hypothesis 3 was supported.</p><h3>Qualitative Findings</h3><p>The thematic analysis revealed three main themes. First, <em>digital transformation as an enabler of real-time information</em>: managers emphasized that DT tools like ERP systems and IoT sensors provided real-time data, enabling faster decision-making. One manager stated, "With our new cloud-based system, we can see inventory levels instantly and adjust production schedules on the fly." Second, <em>knowledge management as a bridge</em>: participants noted that DT alone was insufficient; they needed to codify and share knowledge to leverage data effectively. A manager commented, "We invested in a knowledge-sharing platform, and that's when we saw the real benefits of our digital tools." Third, <em>environmental dynamism as a catalyst</em>: in volatile markets, DT was seen as essential for survival, whereas in stable markets, its impact was less noticeable. One respondent said, "In our industry, customer preferences change every few months, so we have to be agile; digital tools help us keep up."</p><h2>Discussion</h2><p>This study set out to examine the relationship between digital transformation and organizational agility in manufacturing SMEs, with a focus on the mediating role of knowledge management capabilities and the moderating role of environmental dynamism. The findings confirm that DT positively influences agility, aligning with prior research (Warner & Wäger, 2019). However, our study extends this by revealing that KM capabilities partially mediate this relationship. This suggests that DT's impact on agility is not automatic; firms must actively manage knowledge to translate digital investments into agility. This is consistent with the knowledge-based view of the firm (Grant, 1996), which posits that knowledge is a key resource for competitive advantage.</p><p>The moderation effect of environmental dynamism is particularly noteworthy. The stronger effect of DT on agility in dynamic environments underscores the strategic importance of DT in turbulent markets. This finding resonates with dynamic capabilities theory (Teece et al., 2016), which argues that firms need to reconfigure resources to address rapidly changing environments. In stable environments, the marginal benefit of DT for agility may be limited, suggesting that firms should calibrate their DT investments based on their environmental context.</p><p>The qualitative insights enrich our understanding by highlighting the practical mechanisms. Managers emphasized the importance of leadership support and employee digital literacy, which were not explicitly measured in the quantitative phase. This suggests that future research should incorporate these organizational factors as potential moderators or antecedents.</p><h3>Theoretical Implications</h3><p>This research contributes to the literature in several ways. First, it provides empirical evidence of the mediating mechanism through which DT affects agility, addressing a gap in the literature. Second, it identifies environmental dynamism as a boundary condition, offering a more nuanced view of when DT is most beneficial. Third, by focusing on SMEs, it extends the applicability of DT research beyond large corporations, which have been the primary focus of prior studies (Moeuf et al., 2018).</p><h3>Practical Implications</h3><p>For managers of SMEs, our findings suggest that investing in digital technologies alone is insufficient. To enhance agility, firms should also develop robust knowledge management practices, such as creating cross-functional teams, implementing knowledge-sharing platforms, and fostering a culture of learning. Additionally, managers should assess the dynamism of their environment to prioritize DT initiatives. In highly dynamic markets, DT should be a top strategic priority, whereas in stable markets, a more cautious approach may be warranted.</p><h3>Limitations and Future Research</h3><p>This study has several limitations. First, the cross-sectional design precludes causal inferences. Future research could employ longitudinal designs to establish causality. Second, the sample was limited to manufacturing SMEs in the United States, which may limit generalizability to other sectors or countries. Third, self-reported measures may be subject to social desirability bias. Future studies could use objective performance data. Fourth, we focused on KM capabilities as a mediator; other mechanisms, such as organizational learning or innovation capability, could also be explored. Finally, the moderating role of other environmental factors, such as competitive intensity or technological turbulence, could be examined.</p><h2>Conclusion</h2><p>This study provides robust evidence that digital transformation enhances organizational agility in manufacturing SMEs, and that this relationship is partially mediated by knowledge management capabilities. Moreover, the effect is stronger in dynamic environments, highlighting the contextual importance of DT. These findings have significant implications for both theory and practice, emphasizing the need for a holistic approach to digital transformation that integrates technology with knowledge management and considers the external environment. 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