Digital transformation extends beyond digitising individual records or automating isolated tasks. In supply chains, it involves redesigning information flows, planning processes, decision rights and inter-firm interfaces through interconnected digital technologies. Real-time monitoring, integrated enterprise systems, analytics, digital partner interfaces and automated workflows can improve the speed and quality of operational decisions ([4]; [14]; [22]). Internet-of-things and sensor technologies are especially relevant because they create near-real-time operational data that can improve monitoring, traceability and coordination ([3]; [19]). Big-data analytics likewise contributes to performance when analytical resources are aligned with business strategy and embedded in organisational capabilities ([1]; [23]).

Although digital transformation can be positively related to efficiency, the study does not assume that a direct relationship will necessarily remain once capability mechanisms are modelled. The direct digital-transformation coefficient is therefore retained in the full model as an empirical benchmark rather than as the central theoretical claim.

Organisational Information Processing Theory proposes that organisations facing uncertainty and interdependence must develop sufficient capacity to acquire, interpret and distribute information ([7]). Manufacturing supply chains face demand variation, lead-time uncertainty, production disruptions, inventory imbalances and logistics exceptions. When operational information is late, incomplete or fragmented, planning quality deteriorates and coordination costs rise. Digital transformation can increase information-processing capacity through real-time data, shared dashboards, integrated databases and analytical tools ([14]; [20]).

Supply-chain visibility is the ability to access timely, accurate and usable information about orders, inventory, demand, disruptions and logistics flows. Connected systems should strengthen visibility because they reduce informational delays and make exceptions more observable. Accordingly, H1: Digital transformation is positively associated with supply-chain visibility.

Visibility does not itself ensure coordinated action. Integration concerns the alignment of planning, information exchange and decision-making across procurement, production, logistics and external partners. A consistent view of orders, capacity, demand and disruption should reduce duplicated work and contradictory decisions. Accordingly, H2: Supply-chain visibility is positively associated with supply-chain integration.

The Dynamic Capabilities View holds that performance differences arise not only from resources but from the ability to sense, seize and transform in changing environments ([21]). Digital systems can reveal bottlenecks and changing demand, but they cannot by themselves redesign production sequences, revise inventory rules or redeploy resources. Reconfiguration capability captures the firm’s ability to alter processes, routines, decisions and resource allocations in response to operational conditions. Studies of digital transformation and big-data analytics increasingly show that digital resources create value through dynamic capabilities rather than technological possession alone ([23]; [22]; [25]).

Integration should support reconfiguration because coordinated functions and partners can implement process changes more consistently. Reconfiguration, in turn, should improve efficiency by enabling firms to remove bottlenecks, reallocate capacity, revise fulfilment priorities and adjust supplier or logistics arrangements. The following hypotheses are therefore proposed: H3: Supply-chain integration is positively associated with supply-chain efficiency. H4: Supply-chain integration is positively associated with reconfiguration capability. H5: Reconfiguration capability is positively associated with supply-chain efficiency.

The central mediation proposition concerns the complete capability sequence rather than an unmodelled direct digital transformation-to-reconfiguration path. H6: Digital transformation has a positive serial indirect association with supply-chain efficiency through supply-chain visibility, supply-chain integration and reconfiguration capability.

Environmental uncertainty may increase the operational value of digital transformation because volatile demand, supply conditions and operating constraints raise the need for rapid information processing and adaptive response. Digital systems may therefore have stronger efficiency associations when uncertainty is high, although severe uncertainty may also overwhelm firms whose routines cannot absorb additional information ([11]; [20]; [26]; [27]). H7: Environmental uncertainty positively moderates the association between digital transformation and supply-chain efficiency.

Internal digital capability refers to employees’ digital skills, system competence and ability to apply digital tools. Firms with stronger capability may be better positioned to integrate technologies into operational routines. Enterprise digital capability has been linked with supply-chain digitalisation and cooperation capability ([5]), while digital-transformation research emphasises managerial and organisational capability in converting technologies into value ([22]; [25]). H8: Internal digital capability positively moderates the association between digital transformation and supply-chain efficiency.

Figure 1 distinguishes the direct structural paths from H6, which is defined as the full serial indirect pathway DT → VIS → INT → REC → EFF. Environmental uncertainty and internal digital capability are modelled as moderators of the digital transformation-efficiency association.

Figure 1. Conceptual framework.

The study employed a cross-sectional explanatory survey design at the firm level. The target population comprised medium and large manufacturing firms with identifiable procurement, production, inventory, logistics or distribution activities and at least three years of operation. The unit of analysis was the firm. Because the purpose was to test theory-led associations among organisational constructs rather than establish temporal causation, all effects are interpreted as contemporaneous associations.

A working sampling frame was assembled from publicly accessible manufacturing-company listings and industry-association directories and classified using the industry and firm-size categories commonly used in Ghana’s establishment statistics. Entries were de-duplicated and screened by telephone or company website to confirm manufacturing activity, firm size and the existence of formal supply-chain functions. This process produced a working frame of 536 eligible firms. A stratified recruitment plan targeted 200 usable cases across six industries, with allocations designed to preserve representation of the principal manufacturing categories while ensuring sufficient cases in smaller sectors. Within each sector, firms were approached systematically from the verified list until the target was reached.

A total of 320 firms were contacted. Two hundred and forty-six indicated willingness to participate, 214 returned sufficiently complete questionnaires, and 200 met all eligibility and data-quality requirements, yielding a usable response rate of 62.5% relative to firms contacted. The achieved distribution closely approximated the targeted industry allocation and the target split of 150 medium and 50 large firms. The industry-specific sampling frame, recruitment targets and achieved usable responses are summarised in Table 1.

Table 1. Sampling frame, recruitment targets and achieved usable responses.

One knowledgeable informant was selected per firm to minimise ambiguity in the unit of analysis. Initial contact was made with the head of operations, supply chain, procurement, logistics, production planning or information systems. The preferred respondent had direct knowledge of the firm’s digital systems and supply-chain routines and at least two years of organisational tenure. Where the initial contact did not meet these criteria, the firm was asked to nominate the manager or senior specialist with the broadest cross-functional knowledge of digital and supply-chain processes. No firm contributed more than one questionnaire to the analytical dataset.

Respondents represented supply-chain/procurement management, logistics and distribution, production and planning, operations/process improvement, and information-systems or digital-transformation functions. Mean organisational tenure was 7.1 years (SD = 4.3; median = 6), and 86.0% of respondents had at least three years’ tenure, supporting their use as key informants for firm-level practices. The key-informant profile is summarised in Table 2.

Table 2.Key-informant profile.

All focal constructs were measured on seven-point agreement scales ranging from 1 (strongly disagree) to 7 (strongly agree). Item wording was informed by established digital-transformation, information-processing, supply-chain visibility, integration, dynamic-capability and performance literatures and adapted to a manufacturing supply-chain context. Digital transformation was represented by five items; internal digital capability, visibility, integration, reconfiguration capability and environmental uncertainty by four items each; and supply-chain efficiency by five items. The full item wording, source domains and scoring rules are reported in Appendix A.

Before the main survey, the questionnaire was piloted with 12 managers drawn from eight manufacturing firms that were not included in the final sample. Pilot participants represented procurement, production, logistics and information-systems roles. The pilot led to four types of refinement: technical terms were simplified; double-barrelled statements were separated or removed; the distinction between digital-system deployment and employee digital capability was sharpened; and several items were anchored more explicitly to supply-chain operations. Predictor and outcome blocks were also separated in the questionnaire to reduce consistency-motif responding. The construct operationalisations and source domains are summarised in Table 3.

Table 3.Construct operationalisation and source domains.

The raw dataset was preserved unchanged and all derived variables were generated reproducibly. Coding ranges, duplicate identifiers, missingness, descriptive distributions and consistency between item-level responses and stored composite scores were checked. There were 115 missing item responses among 6000 potential item values (1.92%). For the primary analysis, a construct score was retained when at least half of its items were completed: at least three of five items for five-item scales and at least two of four items for four-item scales. All 200 firms met this rule. Construct scores were calculated as the arithmetic mean of available items, preserving the original one-to-seven metric.

Measurement quality was assessed using Cronbach’s alpha, indicator loadings, composite reliability and average variance extracted (AVE). Discriminant validity was evaluated using the heterotrait-monotrait ratio (HTMT), with values below 0.85 treated as strong evidence of empirical distinction ([10]). Because digital transformation and internal digital capability correlated at 0.702, their HTMT value and variance inflation factors (VIFs) were examined explicitly. Collinearity was assessed in the full moderation model after standardisation of the continuous predictors and construction of the product terms.

Structural relationships were estimated with standardised composite scores using ordinary least-squares path models and HC3 heteroskedasticity-consistent standard errors. Industry, ownership, employee size, firm age, and upstream and downstream concentration were included as controls. Moderation was assessed with product terms created from standardised variables. The serial indirect association was evaluated with 5000 non-parametric bootstrap resamples. Because the design is cross-sectional, indirect effects are interpreted as statistical indirect associations rather than temporal causal mediation.

Two missing-data sensitivity analyses were conducted. First, a stricter scoring rule required at least 75% of items for each scale (at least four of five items and at least three of four items), retaining 195 firms. Second, the structural models were re-estimated using only the 112 firms with complete responses on all 30 focal items. The purpose was to assess whether conclusions depended on available-item means.

As shown in Table 4, the 200 firms represented six manufacturing industries. Food and beverages formed the largest category (25.5%), followed by consumer goods (19.5%), light industrial products (17.5%), plastics (16.5%), construction materials (12.5%) and chemicals (8.5%). Most firms were locally owned (78.0%) and medium-sized (74.5%). Mean employment was 215.06 workers (SD = 175.19), and mean firm age was 22.45 years (SD = 11.23).

Table 4.Sample profile and firm characteristics.

As shown in Table 5, digital transformation and internal digital capability were moderately below the scale midpoint, whereas environmental uncertainty had the highest mean. The DT-IDC correlation of 0.702 warranted formal discriminant-validity and collinearity assessment rather than reliance on the correlation threshold alone.

Table 5. Descriptive statistics and construct correlations. DT = digital transformation; IDC = internal digital capability; VIS = supply-chain visibility; INT = supply-chain integration; REC = reconfiguration capability; EFF = supply-chain efficiency; EU = environmental uncertainty.

As shown in Table 6, all constructs displayed strong internal consistency and convergent validity. Cronbach’s alpha ranged from 0.891 to 0.957, composite reliability from 0.926 to 0.968, and AVE from 0.759 to 0.857. Indicator loadings were uniformly high. The first unrotated factor across all items explained 38.71% of variance; this diagnostic was treated as descriptive rather than as proof that common-method bias was absent.

Table 6.Reliability and convergent validity.

As shown in Table 7, the highest HTMT value was 0.747 for digital transformation and internal digital capability, below the conservative 0.85 criterion. The result supports empirical distinction between the constructs despite their relatively high zero-order correlation.

Table 7. HTMT discriminant-validity ratios. Values are based on item-level pairwise correlations; all are below 0.85.

As shown in Table 8, the largest focal-predictor VIF was 3.557 for digital transformation. Although higher than the VIFs of the remaining constructs, it was below conventional levels indicating severe multicollinearity. Internal digital capability had a VIF of 2.373 and the DT × IDC interaction had a VIF of 1.204. Accordingly, the null moderation result is unlikely to be explained solely by excessive predictor collinearity.

Table 8.Focal-predictor VIFs in the full moderation specification.

As shown in Table 9, digital transformation was strongly associated with visibility (β = 0.614, p < 0.001), supporting H1. Visibility was positively associated with integration (β = 0.350, p < 0.001), supporting H2, and integration was positively associated with reconfiguration capability (β = 0.362, p < 0.001), supporting H4. In the full efficiency model, reconfiguration capability (β = 0.254, p < 0.001) and visibility (β = 0.268, p < 0.001) remained significant. Integration did not retain an independent relationship with efficiency (β = 0.059, p = 0.438), so H3 was not supported, while H5 was supported.

Table 9.Structural paths, serial indirect association and moderation.

Consistent with H6, the estimated serial indirect association from digital transformation through visibility, integration and reconfiguration to efficiency was positive (effect = 0.030, 95% CI [0.014, 0.055]). This result supports the complete capability sequence rather than a direct DT → REC mediation claim. The direct digital-transformation coefficient in the full model was small and non-significant (β = 0.074, p = 0.456), indicating that the capability variables accounted for much of the observed association.

Environmental uncertainty produced a positive interaction that did not reach conventional statistical significance (β = 0.119, p = 0.051); H7 was therefore not supported. The interaction involving internal digital capability was not significant (β = 0.043, p = 0.382), and H8 was not supported. The full efficiency model explained 47.4% of the variance. Figure 2 summarises the estimated structural relationships, while Figure 3 displays the conditional digital transformation-efficiency slopes across environmental uncertainty.

Figure 2. Estimated structural relationships.

Figure 3.Conditional digital transformation-efficiency slopes across environmental uncertainty.

As shown in Table 10, the substantive conclusions were stable. Across the stricter 75% item-completion sample and the complete-item sample, the DT → VIS, VIS → INT, INT → REC, VIS → EFF and REC → EFF coefficients remained positive, whereas the independent INT → EFF relationship remained weak. The serial product was also positive in both sensitivity samples: approximately 0.0218 (bootstrap 95% CI [0.0069, 0.0475]) under the 75% rule and 0.0156 (95% CI [0.0018, 0.0470]) among complete-item cases. These checks indicate that available-item means were not responsible for the central findings.

Table 10.Missing-data sensitivity analysis. Coefficients were re-estimated using the same standardisation and control structure within each sensitivity sample.

The study examined how digital transformation is translated into supply-chain efficiency. The results provide a clearer answer to the “how” question than to the “when” question. Digital transformation was strongly associated with visibility; visibility was associated with integration; and integration was associated with reconfiguration capability. Yet only visibility and reconfiguration retained independent relationships with efficiency in the full model. The direct digital-transformation association weakened and became statistically non-significant once these capabilities were considered.

This pattern challenges a technology-centred interpretation of digital transformation. Firms may install integrated systems, dashboards or analytics without obtaining commensurate efficiency gains if the information does not improve operational visibility or enable reconfiguration of workflows and resources. The results are consistent with studies showing that visibility, information exchange, responsiveness, agility, survivability and resilience help explain digitalisation-performance relationships ([2]; [26]; [30]; [31]). The present study adds specificity by isolating operational efficiency and by modelling informational visibility, coordinative integration and adaptive reconfiguration as sequentially distinct mechanisms.

Integration’s unsupported direct relationship with efficiency is theoretically informative. Coordination may be necessary for reconfiguration but insufficient for efficiency when it remains procedural rather than action-oriented. Integration can align information and plans, but efficiency improvement may require managers to change production sequences, inventory rules, supplier arrangements or resource allocations. The positive integration-reconfiguration relationship and the serial indirect association are consistent with this interpretation.

First, the findings refine Organisational Information Processing Theory by locating visibility as the primary informational return to digital transformation. Connected systems appear principally to increase the organisation’s capacity to observe demand, inventory, order and disruption conditions. Visibility then supports integration, consistent with the premise that usable information is a precondition for coordinated decisions ([7]; [20]).

Second, the study develops the Dynamic Capabilities View by showing that reconfiguration is a concrete operational conversion mechanism. Digital information becomes valuable when firms alter routines and redeploy resources. Reconfiguration remained a strong correlate of efficiency after digital transformation, visibility, integration and contextual factors were considered, adding operational specificity to the broader sensing-seizing-transforming logic ([21]; [25]).

Third, H6 clarifies capability sequencing. The empirical proposition is not that reconfiguration alone mediates digital transformation. Rather, the tested serial association runs from digital transformation to visibility, from visibility to integration, from integration to reconfiguration, and from reconfiguration to efficiency. This sequencing aligns the hypothesis, conceptual model and statistical estimand.

Finally, the formal discriminant-validity evidence strengthens interpretation of the non-significant internal-digital-capability moderation. DT and IDC were correlated, but their HTMT ratio (0.747) and VIFs did not suggest empirical redundancy. The null interaction is therefore more plausibly interpreted as evidence that digital capability may operate earlier in the transformation process—as an antecedent of transformation or visibility—rather than amplifying the final DT-efficiency association.

Managers should evaluate digital transformation through the operational capabilities it creates. The first priority is visibility: systems should provide reliable, timely and accessible information about inventory, demand, fulfilment, transport and disruptions. Technology projects that produce additional data without improving decision visibility are unlikely to generate substantial efficiency gains.

Second, digital information should be linked to explicit coordination routines. Shared planning cycles, exception-management protocols and cross-functional decision rights can prevent visibility from remaining passive. Integration, however, should not be the endpoint. Managers need routines for process redesign, resource redeployment and rapid rescheduling so that coordinated information leads to operational change.

Third, firms should measure digital value with operational metrics such as order-cycle time, stockouts, inventory turnover, logistics cost, schedule adherence and process waste. For policy institutions and industry associations, support programmes should combine technology access with process redesign, data governance, interoperability and managerial capability building, particularly for medium-sized and locally owned manufacturers.

The study remains cross-sectional and therefore does not establish temporal ordering or causal mediation. Reverse causality is possible because efficient firms may have greater resources to invest in digital systems. Longitudinal studies should examine whether changes in digital transformation precede changes in visibility, reconfiguration and objective efficiency outcomes.

The focal variables were measured perceptually and largely within one survey. Procedural remedies, the single-factor diagnostic, discriminant-validity checks and collinearity analyses reduce specific concerns but cannot eliminate common-method bias ([17]). Future research should combine manager surveys with system-generated data and objective indicators such as fulfilment time, inventory turnover, logistics cost and production downtime. Multi-informant and supplier-customer dyadic designs would improve construct separation and reduce same-source dependence.

The key-informant strategy is defensible for firm-level organisational practices but does not guarantee perfect organisational representation. Future studies should obtain paired responses from digital/IT and supply-chain managers and quantify inter-rater agreement. Finally, the strong reliability of several scales suggests some item similarity even though HTMT results support discriminant validity; future instrument development should retain conceptual breadth while avoiding redundant phrasing.

This study examines whether digital transformation improves supply chain efficiency in manufacturing firms in Ghana, and whether this relationship operates through supply chain visibility, supply chain integration, and reconfiguration capability. It also considers whether environmental uncertainty and internal digital capability influence the strength of this relationship.

You have been invited because you hold a managerial role with direct knowledge of your firm’s supply chain operations, digital systems, planning processes, logistics activities, procurement activities, or operational coordination.

You will be asked to complete one structured questionnaire covering your firm’s supply-chain operations, digital systems and related organisational capabilities. The survey should take approximately 10 to 15 minutes.

Your participation is entirely voluntary. You may decline to answer any question, and you may withdraw before final submission of the questionnaire.

No personal names will be disclosed in any publication or report arising from this research. Firm names will be anonymised, and all responses will be used only for academic purposes.

There are no significant risks associated with participation beyond the time required to complete the questionnaire. Although there is no direct financial benefit, your participation may contribute to better understanding of how digital transformation affects supply chain efficiency in Ghanaian manufacturing firms.

Researcher contact

Researcher: [Insert Name]

Institution: [Insert Institution]

Email: [Insert Email]

Phone: [Insert Contact]

Consent statement

Please tick one option:

[ ] I have read and understood the information above and agree to participate in this study.

[ ] I do not agree to participate in this study.

Unless otherwise stated, all items use the following response scale:

1 = Strongly disagree

2 = Disagree

3 = Somewhat disagree

4 = Neither agree nor disagree

5 = Somewhat agree

6 = Agree

7 = Strongly agree

Section 1. Firm and Respondent Identification

Please complete the following:

1) Firm code: ____________________

2) Respondent code: ____________________

3) Region/location: ____________________

4) Industry sector:

[ ] Food and beverages

[ ] Consumer goods

[ ] Chemicals

[ ] Plastics

[ ] Construction materials

[ ] Light industrial products

[ ] Other: ____________________

5) Your current position/title: ____________________

6) Years in current role:

[ ] Less than 1 year

[ ] 1 - 3 years

[ ] 4 - 6 years

[ ] 7 - 10 years

[ ] More than 10 years

7) Total years with this firm:

[ ] Less than 1 year

[ ] 1 - 3 years

[ ] 4 - 6 years

[ ] 7 - 10 years

[ ] More than 10 years

Section 2. Firm Profile and Control Variables

1) Number of full-time employees:

[ ] 30 - 49

[ ] 50 - 99

[ ] 100 - 149

[ ] 150 - 249

[ ] 250 - 499

[ ] 500 or more

2) Age of firm:

[ ] 3 - 5 years

[ ] 6 - 10 years

[ ] 11 - 15 years

[ ] 16 - 20 years

[ ] More than 20 years

3) Ownership structure:

[ ] Fully Ghanaian-owned

[ ] Majority Ghanaian-owned

[ ] Majority foreign-owned

[ ] Fully foreign-owned

[ ] Joint venture

4) Main market orientation:

[ ] Mainly local

[ ] Mainly national

[ ] National and export

[ ] Mainly export

5) Approximate proportion of annual sales to the largest customer:

[ ] Less than 10%

[ ] 10% - 24%

[ ] 25% - 39%

[ ] 40% - 59%

[ ] 60% or more

6) Approximate proportion of major inputs sourced from the largest supplier:

[ ] Less than 10%

[ ] 10% - 24%

[ ] 25% - 39%

[ ] 40% - 59%

[ ] 60% or more

Section 3. Supply Chain Visibility

Instruction: Please indicate the extent to which each statement reflects your firm’s current operations.

Section 4. Supply Chain Integration

Section 5. Reconfiguration Capability

Section 6. Supply Chain Efficiency

Section 7. Environmental Uncertainty

Section 8. Comparative Performance Anchors

1) Compared with key competitors, our order fulfilment speed is:

[ ] Much lower

[ ] Lower

[ ] Slightly lower

[ ] About the same

[ ] Slightly higher

[ ] Higher

[ ] Much higher

2) Compared with key competitors, our supply chain operating cost efficiency is:

[ ] Much lower

[ ] Lower

[ ] Slightly lower

[ ] About the same

[ ] Slightly higher

[ ] Higher

[ ] Much higher

Section 9. Digital Role Verification

1) Does your current role involve responsibility for any of the following?

[ ] Digital systems

[ ] ERP/MRP/planning systems

[ ] Process digitalization

[ ] Data analytics/reporting

[ ] Operational technology

[ ] Digital transformation projects

2) How familiar are you with your firm’s digital systems used in supply chain operations?

[ ] Very unfamiliar

[ ] Unfamiliar

[ ] Slightly unfamiliar

[ ] Neutral

[ ] Slightly familiar

[ ] Familiar

[ ] Very familiar

Section 10. Digital Transformation

Section 11. Internal Digital Capability

Section 12. Digital Adoption Profile

Please indicate the extent to which your firm currently uses each of the following in supply chain operations.

Response options:

1 = Not used

2 = Used in limited areas

3 = Used in several areas

4 = Widely used across operations

Section 13. Digital Maturity Indicators

1) Approximate number of years your firm has used integrated digital systems in supply chain operations:

[ ] Less than 1 year

[ ] 1 - 2 years

[ ] 3 - 5 years

[ ] 6 - 10 years

[ ] More than 10 years

2) How would you rate your firm’s overall digital maturity in supply chain operations?

[ ] Very low

[ ] Low

[ ] Slightly low

[ ] Moderate

[ ] Slightly high

[ ] High

[ ] Very high