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How Do Data Collection Strategy and Data Quality Influence the Outcomes of Digital Technology Adoption?

Xuejiao Li, Cheng Yang

arXiv:2609.12745v1cs.DB

TL;DR

The paper addresses limited empirical evidence on how data collection strategy and data quality relate to digital technology adoption outcomes. It analyzes survey data from manufacturing firms using PLS-SEM, finding that data quality mediates the relationship between collection strategy and implementation performance. Data quality also relates to both performance outcomes, while collection strategy has a direct relationship with operational performance.

  • Problem

    Empirical research has rarely examined how data collection strategy and data quality jointly relate to digital technology adoption outcomes.

    Source: §1 · p. 2, §2.3 · p. 8

  • Method

    The study analyzes survey data from manufacturing firms using PLS-SEM to examine data collection strategy, data quality, and implementation and operational performance.

    Source: §6.1 · p. 24

  • Results

    Data quality significantly influences implementation and operational performance, fully mediates collection strategy's effect on implementation performance, and partially mediates its effect on operational performance.

    Source: §4.3 · p. 22, §6.1 · p. 24

  • Takeaways & Limitations

    The findings identify data quality as a bridge between upstream data practices and downstream digital outcomes.

    Source: §4.3 · p. 22, §6.1 · p. 24

    The sample of 86 manufacturing firms may limit generalizability to sectors such as logistics, services, or healthcare.

    Source: §6.2 · p. 24

Abstract

from arXiv · show

In the era of Industry 4.0 (I4.0), data has become the essential foundation for digital transformation, yet many organizations still struggle to link data practices with digital performance outcomes. This study investigates how data collection strategy and data quality jointly influence the success of digital technology adoption (DTA) in manufacturing firms. Drawing on survey data from 86 firms, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among data collection strategy, data quality, implementation performance, and operational performance. The results show that both data collection strategy and data quality significantly influence implementation and operational performance. However, the effect of data collection strategy on implementation performance is indirect, fully mediated by data quality. The study also finds that data collection strategy influences data quality. These findings demonstrate that data quality acts as a critical bridge between upstream data practices and downstream digital outcomes. The study contributes to the digital transformation and data management literature by empirically validating the central role of data quality and offering practical insights for managers to design and govern data processes strategically. It also sets a foundation for future research on data governance frameworks that integrate data quality assurance, standardization, and lifecycle management to sustain data-driven and digital transformation.

1. Introduction

The introduction motivates examining data practices as foundations of digital technology adoption, addressing limited empirical evidence linking them to implementation and operational outcomes. The study uses a survey of 86 manufacturing firms and PLS-SEM to assess these relationships.

1. Introduction

  • Digital initiatives depend on available, high-quality data, yet organizations may overlook data while investing in digital technologies.The introduction frames data as a foundational resource for digital transformation.Source: §1 · p. 2
  • Research has rarely examined how data collection strategy and data quality jointly relate to digital technology adoption outcomes, especially through quantitative links to implementation and operational performance.The study examines these relationships and the interrelationship between data collection strategy and data quality.Source: §1 · pp. 2–3
  • The study distinguishes implementation performance from post-adoption operational outcomes, including efficiency, flexibility, delivery reliability, sales growth, and profitability.It seeks to link upstream data practices to both outcome layers.Source: §1 · pp. 2–3
  • Survey responses from 86 manufacturing firms are analyzed with PLS-SEM to assess data practices and digital implementation and operational performance.The measured constructs use reflective indicators on five-point Likert scales adapted from validated studies.Source: §1 · p. 3
  • The study contributes empirical evidence on data practices and performance outcomes and offers managers insights for aligning data processes with performance objectives.The introduction argues that technology investment alone is insufficient without systematically collected, high-quality data.Source: §1 · p. 3

2. Literature Review and Hypothesis Development

The literature review frames DTA outcomes as both implementation and operational performance, while identifying data foundations as an underexamined influence. It proposes a framework in which data collection strategy shapes data quality and both relate to DTA performance.

2.1 DTA and its Outcomes

  • DTA outcomes span implementation and operational performance, but prior work often studies implementation or operational results separately, leaving their connection to sustained value less clear.The review describes digital technologies as integrated into core operations and strategic practices.Source: §2.1 · pp. 4–5

2.2 The Enablers of DTA

  • Prior DTA research emphasizes leadership, organizational culture, infrastructure, and institutional conditions as adoption enablers.The reviewed work also addresses external support and data-related capabilities.Source: §2.2 · p. 5, §2.2.1 · p. 5, §2.2.2 · pp. 5–6

2.2.1 Leadership and Strategic Commitment

  • Leadership and strategic commitment are presented as adoption enablers, including leadership effectiveness, top-management commitment, and alignment of digital strategy with long-term objectives.The reviewed studies also connect leadership style and psychological traits with openness to innovation.Source: §2.2.1 · p. 5

2.2.2 Organizational and Sociotechnical Factors

  • The review describes sociotechnical and organizational influences on readiness, including work organization, collaboration, environmental preparedness, IT infrastructure, and data integration.It also notes that manufacturing strategy may mediate critical-success-factor effects under high I4.0 readiness.Source: §2.2.2 · pp. 5–6

2.2.3 Innovation, Culture, and Psychological Factors

  • Culture and psychological readiness are linked in prior work to learning attitudes, skill development, and decisions to adopt smart technologies.The review also identifies change readiness, communication, and stakeholder engagement as relevant factors.Source: §2.2.3 · p. 6

2.2.4 External and Institutional Support; 2.2.5 Data Governance, Infrastructure, and Legal Context

  • Prior research associates digital adoption with contextual and governance conditions, including economic factors, firm characteristics, data governance, infrastructure, security, and training.The literature review also discusses legal and regulatory aspects and support for digital initiatives.Source: §2.2.4 · p. 6, §2.2.5 · pp. 6–7

2.3 Data Collection Strategy, Data Quality, and the Missing Link in DTA Research

  • Data collection strategy and data quality are identified as underexamined foundations: collection processes determine what enters digital systems, while quality affects the data's reliability and usability.The review notes that collection strategy has often been subsumed under broader readiness concepts and that its relationship with DTA outcomes remains underexplored.Source: §2.3 · pp. 7–8

2.4 Conceptual Framework and Hypotheses Development

  • The proposed framework links data collection strategy and data quality to implementation and operational performance, with collection strategy shaping data quality.It posits that data quality affects both outcomes and mediates the data-collection relationship with performance; the hypotheses specify positive relationships and mediation.Source: §2.4 · pp. 9–10

3. Research Methodology

The study uses a quantitative survey design and PLS-SEM to test relationships among four DTA constructs. It measures each construct reflectively with multiple indicators on five-point Likert scales.

3.1 Research Design and Approach

  • The study adopts a quantitative, survey-based design and uses PLS-SEM to test the proposed relationships among data collection strategy, data quality, implementation performance, and operational performance.The authors describe PLS-SEM as suitable for small-to-medium samples and models with reflective and mediating constructs.Source: §3.1 · p. 10

3.2 Measurement of Constructs

  • All four constructs are measured reflectively using multi-item indicators adapted from prior studies and assessed on five-point Likert scales.Higher scale values indicate stronger implementation, coordination, or performance.Source: §3.1 · p. 10, §3.2 · p. 11

Data Collection Strategy

Data collection strategy is operationalized as the formalization and standardization of collection processes before digital implementation. Its two items address process definition and consistent data acquisition.

Data Collection Strategy

  • Data collection strategy captures how extensively firms formalize and standardize data collection before implementing digital technologies.The two-item construct covers defining data types and formats and standardizing data acquisition across business functions.Source: Data Collection Strategy · p. 11

Data Quality

Data quality is multidimensional, and this study operationalizes it through seven attributes selected for manufacturing and digital transformation.

Data Quality

  • Data quality encompasses accuracy, timeliness, completeness, consistency, interpretability, accessibility, and relevance to intended use.The study notes that Wang and Strong group data quality into intrinsic, contextual, representational, and accessibility categories.Source: Data Quality · p. 11
  • Wang and Strong’s framework divides data quality into intrinsic, contextual, representational, and accessibility categories.Table 2 is identified as presenting dimensions and definitions for these four categories.Source: Data Quality · p. 11, Table 2 · p. 12
  • The manufacturing study measures data quality with seven indicators: accuracy, timeliness, completeness, interpretability, representational consistency, accessibility, and relevancy.These indicators were selected as especially relevant to manufacturing and digital transformation.Source: Data Quality · p. 12

Implementation Performance

Implementation performance captures whether digital technology projects meet planned objectives for time, budget, functionality, and satisfaction.

Implementation Performance

  • Implementation performance reflects project success against planned objectives for time, budget, functionality, and satisfaction.The four measures ask whether implementation was on time, within budget, functioned as expected, and satisfied the organization.Source: Implementation Performance · p. 13

Operational Performance

The section defines operational performance measures and reports the sample and PLS-SEM model assessments. The reported direct data-collection-strategy path to implementation performance was not significant.

Operational Performance

  • Operational performance covers cost, quality, flexibility, and delivery, with two indicators for each area.The measures include ordering and unit production costs, conformance and product quality, volume and mix flexibility, and delivery speed and reliability.Source: Operational Performance · p. 13

3.3 Sampling and data collection

  • The survey covered 86 manufacturing firms in 13 Chinese provinces and cities, with varied company sizes, ages, and business scopes.Figures 2 and 3 show company regions and business scope; 69% of firms had more than 1,000 employees.Source: §3.3 · p. 14, Figure 2 · p. 14, Figure 3 · p. 14

3.4.1 Confirmatory Factor Analysis (CFA)

  • The PLS path model shows the constructs, their indicators, and their relationships; arrow directions indicate reflective measurement.The model followed PLS Algorithm calculation.Source: §3.4.1 · p. 15, Figure 4 · p. 15

3.4.2 Construct Reliability and Validity Metrics

  • Cronbach’s alpha values exceeded 0.7, composite reliability values exceeded 0.7, and AVE values exceeded 0.5 for all four constructs.The reported alpha values were 0.909, 0.947, 0.901, and 0.888; CR values were 0.956, 0.957, 0.930, and 0.912; AVE values were 0.917, 0.759, 0.770, and 0.576 for data collection strategy, data quality, implementation performance, and operational performance, respectively.Source: §3.4.2 · p. 16, Table 3 · p. 16

3.4.3 Discriminant Validity

3.4.4 Outer Loadings

  • Outer loadings for OPC1 and OPC2 were 0.589 and 0.394, respectively; because loadings should exceed 0.7 for acceptance, the indicators could be dropped to improve final results.Source: §3.4.4 · p. 17

3.4.5–3.4.7 Structural model assessment

  • Outer and inner VIF values were below 5; R² was 0.593 for data quality, 0.475 for implementation performance, and 0.375 for operational performance.Adjusted R² values were 0.588, 0.463, and 0.360, respectively; F-square was used to assess construct effect size.Source: §3.4.5 · p. 18, Table 7 · p. 18, §3.4.6 · p. 19

3.4.8 Path Coefficients from Bootstrapping

  • Data collection strategy’s path coefficients were 0.770 to data quality, 0.330 to operational performance, and 0.088 to implementation performance; only the last linkage was not significant.Data quality’s path coefficients were 0.321 to operational performance and 0.620 to implementation performance.Source: §3.4.1 · p. 16, Table 11 · p. 20, §3.4.8 · p. 20

4. Analyses and Results

The analyses link data collection strategy and data quality to implementation and operational performance, with data quality mediating both relationships. The mediation pattern differs by outcome: full for implementation and partial for operations.

4.1 Total Effects and Testing of Hypothesis

  • Data collection strategy has significant positive effects on data quality and operational performance, but no significant direct effect on implementation performance.The direct path to implementation performance is not significant (t = 0.633, p = 0.572); Table 12 presents total effects.Source: §4.1 · pp. 20–21, Table 12 · p. 21

4.2 Mediation Analysis

  • Data quality significantly mediates the relationships between data collection strategy and both performance outcomes.The reported total and specific indirect effects are presented in Tables 13 and 14.Source: §4.2 · p. 21, Table 13 · p. 21, Table 14 · p. 22

4.3 Summary of Hypothesis Testing:

  • Data quality fully mediates the relationship between data collection strategy and implementation performance, while partially mediating its relationship with operational performance.Data collection strategy also has a direct relationship with operational performance; data quality is linked to both outcomes.Source: §4.3 · p. 22

5. Discussion and Implication

The discussion connects structured data collection and data quality to performance outcomes, then draws theoretical and managerial implications. It emphasizes data quality management alongside formalized collection processes.

5.1 The Role of Data Collection Strategy

  • Standardized data collection can support data quality and performance, but its benefits for implementation depend on high-quality data.The discussion identifies validation, cleaning, and quality assurance as complementary mechanisms for ensuring data usability.Source: §5.1 · pp. 22–23

5.2 The Central Role of Data Quality

  • Data quality links data collection strategy to performance, with full mediation for implementation and partial mediation for operational outcomes.The discussion describes data quality as a product of systematic governance and management practices.Source: §5.2 · p. 23

5.3 Theoretical Implications

  • The findings distinguish data collection strategy from data quality and identify different mediation patterns across implementation and post-adoption performance.They position data-related practices as a dimension of digital-transformation readiness alongside technological and organizational factors.Source: §5.3 · pp. 23–24

5.4 Managerial Implications

  • Managers are advised to formalize collection processes early and invest in lifecycle data-quality management, governance, auditing, standardization, and training.The recommendations span implementation efficiency and long-term operational performance.Source: §5.4 · p. 24

6. Conclusion, Limitation, and Future Direction

In manufacturing firms, data collection strategy and data quality are linked to digital implementation and operational performance, with data quality fully mediating the strategy’s effect on implementation. The study notes limits in sample scope, survey measures, and construct coverage, and points toward broader data governance research.

6.1 Conclusion

  • Data collection strategy significantly influences data quality and operational performance, while its effect on implementation performance is fully mediated by data quality.Data quality also has significant positive effects on both implementation and operational performance.Source: §6.1 · p. 24

6.2 Limitation

  • The sample of 86 manufacturing firms may limit generalizability to sectors such as logistics, services, or healthcare.The authors suggest expanding empirical scope across industries and institutional contexts.Source: §6.2 · p. 24
  • Self-reported survey data may introduce subjectivity and common method bias, while data integration, governance structure, and metadata management were not included.Statistical tests indicated no major issues; the authors suggest combining perceptual measures with objective performance data or archival datasets.Source: §6.2 · p. 25

6.3 Future Direction

  • Future research should develop a comprehensive data governance framework integrating standardization, accountability, and lifecycle management to support data quality assurance across the data lifecycle.The proposed lifecycle spans data generation, storage, sharing, and utilization.Source: §6.3 · p. 25

Appendix:

The appendix presents the questionnaire used to gather basic firm information and details about digital technology adoption and data collection processes.

Appendix:

  • The questionnaire begins with basic firm information, including respondent position, location, company age, employee count, and industry sector.It also asks which digital technologies the company implemented and in which business areas.Source: Appendix · p. 31
  • Respondents are asked which digital technologies their company has implemented and in which business areas.Source: Appendix · p. 31
  • The appendix labels a section on the data collection process.The listed questionnaire section follows the basic-information items.Source: Appendix · p. 31

(Please indicate the extent to which you agree with each statement.)

The questionnaire measures data collection practices, generated-data quality, implementation performance, and post-adoption operational performance through respondent ratings.

(Please indicate the extent to which you agree with each statement.)

  • Data collection items ask whether processes and procedures were standardized before digital technology implementation to define data types, formats, and consistent collection.Source: (Please indicate the extent to which… · p. 31
  • Data quality items assess generated data’s accuracy and reliability, real-time updating, completeness, clarity, appropriate formats, and format consistency.The questionnaire specifies that “data” refers to data generated after digital technology implementation.Source: (Please indicate the extent to which… · p. 32
  • Data usability items ask whether data can be retrieved and accessed quickly and are helpful for current business operations.Source: (Please indicate the extent to which… · p. 32

(Please indicate your level of agreement with each statement.)

  • Implementation performance is rated through schedule, budget, expectations, and satisfaction with the implementation.Source: (Please indicate your level of… · p. 32
  • Operational performance items include ordering cost, quality consistency, product quality and reliability, and flexibility and delivery measures.Flexibility covers capacity and product mix; delivery items cover speed and reliability.Source: (Please indicate your level of… · pp. 32–33
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