Only one in five organizations succeed in realizing the true value of digital transformation (Morakanyane et al., 2020). The challenge is not merely technical—change is only complete when it is fully adopted and sustainable (Pacolli, 2022). These places change management at the heart of digital transformation strategy. This study examines how DTS influences CM effectiveness at Elesely Group, a leading Egyptian international trade and logistics company.

Four critical research gaps were identified from the literature review (Table 2.1).

The central problem is the high failure rate of digital transformation initiatives, stemming from overemphasizing technology while neglecting the human element.

The main objective is to evaluate the overall impact of DTS on CM effectiveness.

The main hypothesis (H1) states: Digital Transformation Success has a statistically significant positive impact on Change Management.

Three sub-hypotheses:

Theoretically, this study integrates TOE and RBV in an underexplored developing-economy logistics context. Practically, it provides a validated diagnostic instrument and actionable strategies for Egyptian organizations. For future research, it establishes an empirical baseline for comparative and longitudinal studies in the Egyptian logistics and international trade sector.

The population (N=1,100) comprises all eligible full-time employees at Elesely's Damietta branch directly involved in or affected by digital transformation. Using the Krejcie & Morgan (1970) formula (95% confidence, ±5% margin), the required sample was n=285; 312 valid responses were ultimately collected. Simple random sampling was chosen for representativeness, bias reduction, and statistical robustness. Data were collected via an Arabic-language Google Forms questionnaire during 2026. The 35-item instrument covered: (1) Demographic Profile; (2) DTS - 15 items (5 per dimension) on a 5-point Likert scale; and (3) CM - 20 items (4 per dimension) on a 5-point Likert scale, all adapted from Ekal et al. (2024).

SPSS analysis followed five sequential steps: (1) Descriptive statistics; (2) Kolmogorov-Smirnov normality tests; (3) Pearson correlation validity tests; (4) Cronbach's Alpha and Split-Half reliability tests; (5) Multiple regression for H1 and all sub-hypotheses. Ethical principles—voluntary participation, anonymity, confidentiality, academic purpose only, and risk minimization—were strictly observed throughout.

Digital Transformation Success (DTS) is achieved when an organization effectively leverages digital technologies to transform its processes, enhance employee performance, and realize business goals while managing associated human and cultural changes. Table 1.1 synthesizes definitions from six prominent sources.

Change Management (CM) is a systematic approach for guiding organizations from current to desired future states through planning, communication, and engagement strategies that minimize resistance and ensure successful implementation (Ekal et al., 2024). The five ADKAR dimensions are operationalized as shown in Table 1.4.

Research reveals a strong positive relationship when human, strategic, and technological elements are integrated. Ekal et al. (2024) and AL-Hinaai (2023) demonstrate that robust digital capabilities substantially influence CM outcomes. Sour (2023) and Pacolli (2022) confirm that structured programs and leadership correlate with higher adoption. However, Kakungulu (2024) and Pacolli (2022) warn that ignoring the human dimension leads to resistance and failure. The relationship is positive when all three elements align, and negative when organizations focus narrowly on technology.

This study integrates two theories: the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990) and the Resource-Based View (RBV) (Barney, 1991). The TOE framework explains macro-level enabling conditions for DTS adoption—technological readiness, organizational readiness, and external pressures. The RBV explains how VRIN resources (skilled employees, flexible IT, data analytics) create lasting competitive advantages and sustained CM capabilities.

The theoretical integration operates through a two-stage mechanism: the TOE framework explains the macro-level enabling conditions that determine whether digital transformation adoption is possible (Stage 1: Launch Conditions), while the RBV explains how the resulting digital capabilities generate lasting organizational value and change management effectiveness (Stage 2: Value Creation). Together, they form a complete explanatory architecture—TOE explains the "how it begins," RBV explains the "why it sustains." Neither theory alone is sufficient: TOE without RBV cannot explain why some digitally enabled organizations still fail to sustain change; RBV without TOE cannot explain why some resource-rich organizations fail to initiate transformation. For CM, the Organizational Change Capability Theory (Judge & Douglas, 2009) captures enterprise-level change leadership, governance, and competencies.

The research model proposes that Digital Transformation Success—comprising Digital Transformation Strategy (H1.2), Employee Digital Skills (H1.1), and Digital Technology Implementation (H1.3)—exerts a direct positive influence on Change Management (H1) across all five ADKAR dimensions. The model was developed from literature review, identified research gaps, and the TOE-RBV theoretical foundation.

Both variables show moderately positive perceptions (means = 3.55-3.58) and low variability (SD = 0.37), with approximately symmetric distributions. KS significance values < 0.05 indicate non-normal distribution; however, given N=312 and the central limit theorem, parametric regression was justified (Field, 2018).

All item-to-dimension correlations exceeded the 0.30 threshold (Field, 2018), confirming construct validity. Regarding reliability, the Cronbach's α for Employee Digital Skills (EDS) was 0.690—marginally below the conventional 0.70 threshold but within the 0.65–0.70 range deemed acceptable in exploratory and cross-cultural research contexts (Nunnally, 1978; Hair et al., 2019). This value is further supported by a Split-Half (Spearman-Brown) coefficient of 0.779 for the overall DTS scale, indicating adequate internal consistency. The EDS items measure a multidimensional construct (general digital literacy, specialized roles, and team composition), and slight inter-item heterogeneity is theoretically expected and does not compromise construct validity. Split-Half reliability (Spearman-Brown): DTS = 0.779; CM = 0.851, confirming acceptable-to-good internal consistency across all constructs.

Main regression equation: CM=2.991+0.133(DTS strategy)+0.146(EDS)+0.167(DT). All three predictors are statistically significant (p<0.001), with Digital Technology Implementation yielding the strongest effect (B=0.167), followed by Employee Digital Skills (B=0.146) and Digital Transformation Strategy (B=0.133).

H1 was accepted (R=0.434, R2=0.189, F=23.879, p<0.001). The 18.9% explained variance confirms that DTS meaningfully drives CM effectiveness—consistent with Ekal et al. (2024), AL-Hinaai (2023), and Sour (2023). The TOE framework accounts for enabling conditions (technology, organization, environment), while the RBV explains how resulting VRIN resources translate into lasting CM capabilities. The R² value of 18.9% warrants contextual interpretation: in behavioral and organizational research involving complex social phenomena such as digital transformation adoption, R² values in the range of 15–25% are widely considered meaningful and theoretically acceptable (Hair et al., 2019; Cohen, 1992). Comparable studies in developing-economy digital transformation contexts report similar R² values: Ekal et al. (2024) reported R2=0.21, and Errida & Lotfi (2021) reported R2=0.17, confirming that the remaining 81.1% of variance is attributable to contextual factors intentionally outside this study's scope—including organizational culture, digital maturity level, leadership style, and individual change readiness—which are identified as priority variables for future research.

H1.1 accepted (R=0.279, B=0.194, F=26.260, p<0.001; R2=7.8%). A well-defined strategy provides vision and resource allocation clarity, directly strengthening CM ability. The moderate correlation confirms strategy is necessary but insufficient alone—it requires complementary technology and skills to generate CM momentum. This supports Arasuraja (2024) and Abdulquadri et al. (2021).

H1.2 accepted (R=0.303, B=0.214, F=31.425, p<0.001; R2=9.2%). This confirms the human element is nearly as influential as technology itself. Consistent with Social Cognitive Theory (Bandura, 1986) and Ekal et al. (2024), employees who develop self-efficacy through peer observation and positive outcomes are more likely to adopt and reinforce change. This also aligns with the Organizational Change Capability Theory's emphasis on change competencies (Judge & Douglas, 2009).

H1.3 accepted (R=0.313, B=0.225, F=33.689, p<0.001; R2=9.8%)—the strongest individual predictor. Actual deployment of AI, IoT, and cloud creates visible change in daily work, naturally generating ADKAR Awareness and Desire. This aligns with AL-Hinaai (2023) and Pacolli (2022). The ranking DT > EDS > DTS mirrors TOE-RBV logic and warrants deeper explanation. Digital Technology Implementation (B=0.225) emerges as the strongest predictor because actual technological deployment generates immediate, tangible visibility of change in daily operations—naturally activating the first two ADKAR stages (Awareness and Desire) without requiring explicit management intervention (AL-Hinaai, 2023; Pacolli, 2022). Employee Digital Skills (B=0.214) rank second, reflecting the RBV proposition that workforce digital competencies constitute VRIN resources that translate technological capability into sustained organizational performance. Digital Transformation Strategy (B=0.133), while the weakest individual predictor, remains statistically significant and theoretically essential as the governance mechanism ensuring coherence between technology deployment and skill development. This differential pattern is consistent with Liu et al. (2023) and Pacolli (2022), who similarly found technology deployment effects to exceed strategic planning effects in short-to-medium term change management outcomes.

This study provides robust empirical support for a positive relationship between DTS and CM. All four hypotheses were accepted. Digital Technology Implementation is the strongest driver (B=0.225), followed by Employee Digital Skills (B=0.214) and Digital Transformation Strategy (B=0.133). The integrated TOE-RBV framework proved valid and applicable for developing-economy logistics organizations. Collectively, the three dimensions explain 18.9% of CM variance—practically meaningful and theoretically grounded.

Limitations include: (1) moderating variables (culture, leadership, size) excluded from scope; (2) findings specific to Elesely's Damietta branch only; (3) cross-sectional design captures associative and predictive relationships but cannot establish definitive causal direction—all hypothesis language should be interpreted as predictive rather than causal, and longitudinal replication is necessary to establish temporal ordering; (4) self-reported data subject to social desirability bias. Future research should: (1) examine moderating and mediating variables; (2) adopt longitudinal designs; (3) replicate across all 17 Elesely branches and other sectors; (4) combine quantitative and qualitative methods for richer mechanism insights.