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arxiv: 2606.26186 · v1 · pith:PHA55YVWnew · submitted 2026-06-24 · 💻 cs.CY

Enterprise Data Asset Quality: A Management-Standard Conformity-Benefit Realization Framework and Formation Mechanisms

Yixuan Zhu , Aian Wu , Runhan Zhang , Xiyi Wang , Junfeng Yu , Aihua Han This is my paper

Pith reviewed 2026-06-26 00:54 UTC · model grok-4.3

classification 💻 cs.CY
keywords data asset qualitymanagement capabilitystandard conformitybenefit realizationchain mechanismnecessary conditionsconfigurational pathsenterprise data governance
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The pith

Data asset quality forms via a chain where management capability drives standard conformity which promotes benefit realization, and all three are necessary conditions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper constructs a three-dimensional framework for enterprise data asset quality consisting of management capability, standard conformity, and benefit realization. Through empirical analysis combining structural, necessary condition, and configurational methods, it demonstrates positive relationships forming a chain from management to standards to benefits. The work shows that each dimension is a necessary condition for high quality and identifies multiple equivalent paths to achieve it. A sympathetic reader would care because it offers a structured way to link data management practices to actual value creation in organizations facing data standardization challenges.

Core claim

The study establishes that significant positive relationships exist among the three dimensions, with Data Asset Management Capability exerting the strongest effect on Data Quality Standard Conformity and further promoting Data Asset Benefit Realization Capability, forming a chain mechanism of management foundation-standard enhancement-value realization. In addition, all three dimensions constitute critical necessary conditions for achieving high data asset quality, and multiple equivalent configurational paths reflecting different combinations of Management, Standard, and Benefit are identified.

What carries the argument

The three-dimensional framework of Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability, analyzed via PLS-SEM for net effects, NCA for thresholds, and fsQCA for configurations.

If this is right

  • Data Asset Management Capability exerts the strongest effect and serves as the foundation influencing the other dimensions.
  • Data Quality Standard Conformity acts as an intermediary that further promotes Data Asset Benefit Realization Capability.
  • All three dimensions are critical necessary conditions that must be met to achieve high data asset quality.
  • Multiple equivalent configurational paths exist, including governance-oriented and benefit-driven mechanisms.
  • The integrated multi-method approach captures net effects, thresholds, and configurations within one framework.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Enterprises could prioritize developing management capabilities first to initiate the chain toward conformity and realized benefits.
  • The existence of multiple paths suggests organizations can achieve high quality through different emphasis combinations suited to their context.
  • The framework could inform data factor market policies by stressing investments across all three dimensions rather than isolated ones.
  • Longitudinal application of the model might reveal how the chain mechanism strengthens or weakens over time in specific sectors.

Load-bearing premise

The three dimensions identified through grounded theory and LDA topic modeling are valid, distinct, and accurately measurable constructs, and the empirical data collected reliably reflects enterprise realities without major selection bias or omitted variables.

What would settle it

Replicating the PLS-SEM, NCA, and fsQCA analyses on an independent sample of enterprises and finding that management capability does not positively affect conformity or that one dimension is not a necessary condition for high quality would falsify the central claims.

Figures

Figures reproduced from arXiv: 2606.26186 by Aian Wu, Aihua Han, Junfeng Yu, Runhan Zhang, Xiyi Wang, Yixuan Zhu.

Figure 1
Figure 1. Figure 1: Data Asset Quality Scores Across the Five “Data Element [PITH_FULL_IMAGE:figures/full_fig_p018_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Scores for Data Asset Management, Standard, and Benefit Across the Five “Data Elements ×” Sectors Across the five sectors, the mean scores are 3.54 for Data Asset Management Capability, 3.77 for Data Quality Standard Conformity, and 3.90 for Data Asset Benefit Realization Capability. The within-sector ranges are 1.85, 1.65, and 1.72, respectively. These results indicate moderate overall development across … view at source ↗
read the original abstract

Motivated by the limited standardization of enterprise data asset quality evaluation and the unclear relationship between assessment outcomes and value realization, this study develops a three-dimensional framework comprising Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability, based on grounded theory and LDA topic modeling. To examine the formation mechanisms of data asset quality, this study adopts a multi-method approach combining PLS-SEM, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA), to capture net effects, capability thresholds, and configurational paths. The results show that significant positive relationships exist among the three dimensions, with Data Asset Management Capability exerting the strongest effect on Data Quality Standard Conformity and further promoting Data Asset Benefit Realization Capability, forming a chain mechanism of management foundation-standard enhancement-value realization. In addition, all three dimensions constitute critical necessary conditions for achieving high data asset quality, and multiple equivalent configurational paths reflecting different combinations of Management, Standard, and Benefit are identified, such as governance-oriented and benefit-driven mechanisms. This study integrates structural (PLS-SEM), necessary-condition (NCA), and configurational (fsQCA) analyses within a unified framework, providing a systematic approach to understanding data asset quality formation and offering practical insights for enterprise data governance and data factor market development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript develops a three-dimensional framework for enterprise data asset quality—Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability—identified via grounded theory and LDA topic modeling. It applies PLS-SEM, NCA, and fsQCA to a survey dataset to demonstrate positive interrelationships (with Management exerting the strongest effect on Standard, which promotes Benefit), forming a chain mechanism; all three dimensions as necessary conditions for high quality; and multiple equivalent configurational paths (e.g., governance-oriented and benefit-driven).

Significance. If the measurement model and sampling prove sound, the work contributes a systematic multi-method integration of structural, necessary-condition, and configurational analyses to explain data asset quality formation. The complementary use of net effects, thresholds, and equifinal paths is a clear strength, yielding practical implications for enterprise data governance and data factor market development.

major comments (3)
  1. [Construct development and measurement model sections] The validity, distinctiveness, and reliable measurement of the three dimensions (identified via grounded theory and LDA) are load-bearing for the chain mechanism, necessity claims, and configurational paths. The manuscript should report inter-construct correlations, discriminant validity metrics (e.g., HTMT or Fornell-Larcker), factor loadings, and AVE values in the measurement model section to confirm the dimensions are empirically separable rather than artifacts of the qualitative coding process.
  2. [PLS-SEM analysis and results] The PLS-SEM results supporting the chain mechanism (strongest Management→Standard path) require reported model fit indices (SRMR, CFI, RMSEA), R² values, effect sizes (f²), and tests for common method bias or endogeneity. Without these, the mediation claim and relative effect strengths cannot be evaluated as robust.
  3. [NCA and fsQCA sections] The NCA necessity thresholds and fsQCA consistency/coverage cutoffs are fitted to the same survey data; the manuscript should include sensitivity analyses varying these parameters and alternative specifications to show that the 'critical necessary conditions' and 'equivalent paths' are not sensitive to arbitrary cutoffs.
minor comments (2)
  1. [Abstract] The abstract would benefit from stating the sample size, response rate, and industry coverage to allow immediate assessment of generalizability.
  2. [Throughout] Ensure consistent acronym expansion and avoid undefined abbreviations when first introducing constructs in the results sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which will help improve the rigor of our manuscript. We address each major comment below.

read point-by-point responses

  1. Referee: The validity, distinctiveness, and reliable measurement of the three dimensions (identified via grounded theory and LDA) are load-bearing for the chain mechanism, necessity claims, and configurational paths. The manuscript should report inter-construct correlations, discriminant validity metrics (e.g., HTMT or Fornell-Larcker), factor loadings, and AVE values in the measurement model section to confirm the dimensions are empirically separable rather than artifacts of the qualitative coding process.

    Authors: We fully agree with this assessment. The revised manuscript will include a comprehensive measurement model evaluation section reporting factor loadings, AVE values, inter-construct correlations, and HTMT ratios to establish discriminant validity and confirm that the three dimensions are empirically distinct. revision: yes

  2. Referee: The PLS-SEM results supporting the chain mechanism (strongest Management→Standard path) require reported model fit indices (SRMR, CFI, RMSEA), R² values, effect sizes (f²), and tests for common method bias or endogeneity. Without these, the mediation claim and relative effect strengths cannot be evaluated as robust.

    Authors: We appreciate this point. Although PLS-SEM has different fit assessment conventions than covariance-based SEM, we will report SRMR as the primary fit index, R² values for endogenous constructs, f² effect sizes, and conduct tests for common method bias using Harman's single-factor test and for endogeneity where feasible. These additions will bolster confidence in the reported path coefficients and chain mechanism. revision: yes

  3. Referee: The NCA necessity thresholds and fsQCA consistency/coverage cutoffs are fitted to the same survey data; the manuscript should include sensitivity analyses varying these parameters and alternative specifications to show that the 'critical necessary conditions' and 'equivalent paths' are not sensitive to arbitrary cutoffs.

    Authors: We acknowledge the importance of robustness checks for configurational methods. In the revision, we will perform sensitivity analyses by adjusting the consistency and coverage thresholds in fsQCA and necessity thresholds in NCA, reporting how the core findings (necessary conditions and configurational paths) hold across reasonable variations. revision: yes

Circularity Check

0 steps flagged

No circularity: standard empirical multi-method analysis on survey data

full rationale

The paper identifies three dimensions via grounded theory and LDA topic modeling on qualitative data, then applies PLS-SEM, NCA, and fsQCA to separate survey responses to estimate path coefficients, necessity thresholds, and configurational paths. These outputs are direct empirical results from fitting models to external respondent data rather than any self-definitional reduction, fitted parameter renamed as prediction, or self-citation chain. No equations or claims reduce by construction to the inputs; the chain mechanism and necessary conditions are falsifiable interpretations of the statistical outputs. The derivation chain is self-contained against the collected data and standard methodological benchmarks.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 3 invented entities

Based on abstract only; ledger inferred from described methods. The three dimensions function as invented constructs, and all quantitative results depend on fitted parameters from survey data with no independent evidence supplied.

free parameters (3)
  • PLS-SEM path coefficients
    Fitted from survey responses to quantify effects between the three dimensions.
  • NCA necessity thresholds
    Determined empirically to establish critical conditions for high quality.
  • fsQCA consistency and coverage cutoffs
    Chosen to identify equivalent configurational paths.
axioms (2)
  • domain assumption Grounded theory and LDA topic modeling produce valid and exhaustive dimensions for data asset quality.
    Invoked in framework construction.
  • domain assumption Survey data provides unbiased measures of the latent constructs.
    Required for all three quantitative analyses.
invented entities (3)
  • Data Asset Management Capability no independent evidence
    purpose: Framework dimension capturing management skills for data assets.
    Introduced via grounded theory; no independent evidence.
  • Data Quality Standard Conformity no independent evidence
    purpose: Framework dimension for adherence to quality standards.
    Introduced via grounded theory; no independent evidence.
  • Data Asset Benefit Realization Capability no independent evidence
    purpose: Framework dimension for realizing value from data assets.
    Introduced via grounded theory; no independent evidence.

pith-pipeline@v0.9.1-grok · 5785 in / 1720 out tokens · 48679 ms · 2026-06-26T00:54:21.824629+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

60 extracted references · 41 canonical work pages

  1. [1]

    How Research in Production and Operations Management May Evolve in the Era of Big Data

    Shanthikumar J. How Research in Production and Operations Management May Evolve in the Era of Big Data. Prod Oper Manag 2017; 27. https://doi.org/10.1111/poms.12836

    work page doi:10.1111/poms.12836 2017

  2. [2]

    Big data, big decisions: The impact of big data on board level decision- making

    Merendino A, Dibb S, Meadows M, Quinn L, Wilson D, Simkin L, et al. Big data, big decisions: The impact of big data on board level decision- making. J Bus Res 2018;93:67–78. https://doi.org/10.1016/j.jbusres.2018.08.029

    work page doi:10.1016/j.jbusres.2018.08.029 2018

  3. [3]

    Creating Strategic Business Value from Big Data Analytics: A Research Framework

    Grover V , Chiang RHL, Liang T-P, Zhang D. Creating Strategic Business Value from Big Data Analytics: A Research Framework. J Manag Inf Syst 2018;35:388–423. https://doi.org/10.1080/07421222.2018.1451951

    work page doi:10.1080/07421222.2018.1451951 2018

  4. [4]

    A Framework for Current and New Data Quality Dimensions: An Overview

    Miller R, Whelan H, Chrubasik M, Whittaker D, Du ncan P, Gregório J. A Framework for Current and New Data Quality Dimensions: An Overview. Data 2024;9:151. https://doi.org/10.3390/data9120151

    work page doi:10.3390/data9120151 2024

  5. [5]

    Discovering Data Quality Problems: The Case of Repurposed Data

    Zhang R, Indulska M, Sadiq S. Discovering Data Quality Problems: The Case of Repurposed Data. Bus Inf Syst E ng 2019;61:575 –93. https://doi.org/10.1007/s12599-019-00608-0

    work page doi:10.1007/s12599-019-00608-0 2019

  6. [6]

    DQ -MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems

    Buelvas JH, Múnera D, Gaviria N. DQ -MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems. Internet Things 2023;22:100769. https://doi.org/10.1016/j.iot.2023.100769

    work page doi:10.1016/j.iot.2023.100769 2023

  7. [7]

    Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage

    Laney DB. Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage. 1st ed. New Yor k, NY : Routledge, 2018.: Routledge; 2017. https://doi.org/10.4324/9781315108650

    work page doi:10.4324/9781315108650 2018

  8. [8]

    Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech

    Birch K, Cochrane D, Ward C. Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech. Big Data Soc 2021;8:20539517211017308. https://doi.org/10.1177/20539517211017308

    work page doi:10.1177/20539517211017308 2021

  9. [9]

    Analyzing the game pricing mechanism of data assets: Theoretical evidence considering market structure and competitive characteristics

    Li W, Yang W, Zhou Z, Wu S, Mo C. Analyzing the game pricing mechanism of data assets: Theoretical evidence considering market structure and competitive characteristics. Int Rev Econ Finance 2025;99:104043. https://doi.org/10.1016/j.iref.2025.104043

    work page doi:10.1016/j.iref.2025.104043 2025

  10. [10]

    Data Asset Quality Evaluation Framework Based on a Hybrid Multi‐Criteria Decision‐Making Method

    Xu T, You X, Yang M, Shi Y , Mao R. Data Asset Quality Evaluation Framework Based on a Hybrid Multi‐Criteria Decision‐Making Method. Qual Reliab Eng Int 2024;41:801–20. https://doi.org/10.1002/qre.3692

    work page doi:10.1002/qre.3692 2024

  11. [11]

    Investments in data quality: Evaluating impacts of faulty data on asset management in power systems

    Koziel S, Hilber P , Westerlund P, Shayesteh E. Investments in data quality: Evaluating impacts of faulty data on asset management in power systems. Appl Energy 2021;281:116057. https://doi.org/10.1016/j.apenergy.2020.116057

    work page doi:10.1016/j.apenergy.2020.116057 2021

  12. [12]

    Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems

    Piryonesi SM, El-Diraby TE. Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems. J Transp Eng Part B Pavements 2020;146:04020022. https://doi.org/10.1061/JPEODX.0000175

    work page doi:10.1061/jpeodx.0000175 2020

  13. [13]

    Conceptual Framework for Financial Reporting 2018

    International Accounting Standards Board. Conceptual Framework for Financial Reporting 2018

    2018

  14. [14]

    Assessment and pricing of data assets: research review and prospect

    Yin C, Jin T, Zhang P, Wang J, Chen J. Assessment and pricing of data assets: research review and prospect. 大数据 2021;7:14–27

    2021

  15. [15]

    Data Asset Management Practice White Paper (Version 5.0) 2021

    China Academy of Information and Communications Technology. Data Asset Management Practice White Paper (Version 5.0) 2021

    2021

  16. [16]

    Beyond Accuracy: What Data Quality Means to Data Consumers

    Wang RY , Strong DM. Beyond Accuracy: What Data Quality Means to Data Consumers. J Manag Inf Syst 1996;12:5 –33. https://doi.org/10.1080/07421222.1996.11518099

    work page doi:10.1080/07421222.1996.11518099 1996

  17. [17]

    Ahmed A, Shahzad A, Naseem A, Ali S, Ahmad I. Evaluating the effectiveness of data governance frameworks in ensuring security and privacy of healthcare data: A q uantitative analysis of ISO standards, GDPR, and HIPAA in blockchain technology. PLOS One 2025;20:e0324285. https://doi.org/10.1371/journal.pone.0324285. 45

    work page doi:10.1371/journal.pone.0324285 2025

  18. [18]

    Dismantling data silos with blockchain to unleash collaborative innovation among govern ment departments in South Africa

    Chuma KG. Dismantling data silos with blockchain to unleash collaborative innovation among govern ment departments in South Africa. Glob Knowl Mem Commun 2026;75:1–18. https://doi.org/10.1108/GKMC-07-2024-0464

    work page doi:10.1108/gkmc-07-2024-0464 2026

  19. [19]

    Teece, Gary Pisano, and Amy Shuen

    Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic management. Strateg Manag J 1997;18:509 –33. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%253C509::AID-SMJ882%253E3.0.CO;2-Z

    work page doi:10.1002/(sici)1097-0266(199708)18:7 1997

  20. [20]

    Debating big data: A literature review on realizing value from big data

    Günther WA, Rezazade Mehrizi MH, Huysman M, Feldberg F. Debating big data: A literature review on realizing value from big data. J Strateg Inf Syst 2017;26:191–209. https://doi.org/10.1016/j.jsis.2017.07.003

    work page doi:10.1016/j.jsis.2017.07.003 2017

  21. [21]

    Competitive Advantage: Creating and Sustaining Superior Performance

    Porter ME. Competitive Advantage: Creating and Sustaining Superior Performance. Free Press; 1985

    1985

  22. [22]

    The Rapid Adoption of Data -Driven Decision -Making

    Brynjolfsson E, McElheran K. The Rapid Adoption of Data -Driven Decision -Making. Am Econ Rev 2016;106:133 –9. https://doi.org/10.1257/aer.p20161016

    work page doi:10.1257/aer.p20161016 2016

  23. [23]

    Data governance: A conceptual framework, structured review, and research agenda

    Abraham R, Schneider J, V om Brocke J. Data governance: A conceptual framework, structured review, and research agenda. Int J Inf Manag 2019;49:424–38. https://doi.org/10.1016/j.ijinfomgt.2019.07.008

    work page doi:10.1016/j.ijinfomgt.2019.07.008 2019

  24. [24]

    Data Governance

    Otto B. Data Governance. Bus Inf Syst Eng 2011;3:241–4. https://doi.org/10.1007/s12599-011-0162-8

    work page doi:10.1007/s12599-011-0162-8 2011

  25. [25]

    Business models and dynamic capabilities

    Teece DJ. Business models and dynamic capabilities. Long Range Plann 2018;51:40–9. https://doi.org/10.1016/j.lrp.2017.06.007

    work page doi:10.1016/j.lrp.2017.06.007 2018

  26. [26]

    Unpacking Digital Transformation: Identifying key enablers, transition stages and digital archetypes

    Gillani F, Chatha KA, Jajja SS, Cao D, Ma X. Unpacking Digital Transformation: Identifying key enablers, transition stages and digital archetypes. Technol Forecast Soc Change 2024;203:123335. https://doi.org/10.1016/j.techfore.2024.123335

    work page doi:10.1016/j.techfore.2024.123335 2024

  27. [27]

    Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

    Mikalef P, Krogstie J, Pappas IO, Pavlou P. Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Inf Manage 2020;57:103169. https://doi.org/10.1016/j.im.2019.05.004

    work page doi:10.1016/j.im.2019.05.004 2020

  28. [28]

    Data Qualit y and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

    Byabazaire J, O’Hare G, Delaney D. Data Qualit y and Trust: Review of Challenges and Opportunities for Data Sharing in IoT. Electronics 2020;9:2083. https://doi.org/10.3390/electronics9122083

    work page doi:10.3390/electronics9122083 2020

  29. [29]

    Evaluation of Enterprise Accounting Data Management Based on Maturity Model

    Xiang J. Evaluation of Enterprise Accounting Data Management Based on Maturity Model. Comput Intell Neuro sci 2022;2022:1 –9. https://doi.org/10.1155/2022/3891475

    work page doi:10.1155/2022/3891475 2022

  30. [30]

    The Discovery of Grounded Theory: Strategies for Qualitative Research

    Glaser BG, Strauss AL. The Discovery of Grounded Theory: Strategies for Qualitative Research. 1st ed. Routledge; 2017. https://doi.org/10.4324/9780203793206

    work page doi:10.4324/9780203793206 2017

  31. [31]

    Distributed Tuning of Boundary Resources: The Case of Apple’s iOS Service System1

    Eaton B, Elaluf-Calderwood S, Sørensen C, Yoo Y . Distributed Tuning of Boundary Resources: The Case of Apple’s iOS Service System1. MIS Q 2015;39:217–43. https://doi.org/10.25300/MISQ/2015/39.1.10

    work page doi:10.25300/misq/2015/39.1.10 2015

  32. [32]

    The Actual Value of the Classical Grounded Theor y and Its Spirit to China Management Research

    Jia X, Tan X. The Actual Value of the Classical Grounded Theor y and Its Spirit to China Management Research. Chinese Journal of Management 2010;7:656–65

    2010

  33. [33]

    Construction of an Evaluation Indicator System for High-Quality Datasets in the Data Element Market

    Lin Z, Wu J, Hu X, Wang J, Y uan Y , Du L. Construction of an Evaluation Indicator System for High-Quality Datasets in the Data Element Market. J Inf Resour Manag 2025;15:52–66. https://doi.org/10.13365/j.jirm.2025.06.052

    work page doi:10.13365/j.jirm.2025.06.052 2025

  34. [34]

    Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory

    Corbin J, Strauss A. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. SAGE Publications; 2014

    2014

  35. [35]

    Text as Data : A New Framework for Machine Learning and the Social Sciences

    Grimmer J, Roberts ME, Stewart BM. Text as Data : A New Framework for Machine Learning and the Social Sciences. Princeton University Press; 2022

    2022

  36. [36]

    Mixing Qualitative and Quantitative Methods: Triangulation in Action

    Jick TD. Mixing Qualitative and Quantitative Methods: Triangulation in Action. Adm Sci Q 1979;24:602–11

    1979

  37. [37]

    Topic Modeling in Management Research: Rendering New Theory from Textual Data

    Hannigan TR, Haans RFJ, Vakili K, Tchalian H, Glaser VL, Wang MS, et al. Topic Modeling in Management Research: Rendering New Theory from Textual Data. Acad Manag Ann 2019;13:586–632. https://doi.org/10.5465/annals.2017.0099

    work page doi:10.5465/annals.2017.0099 2019

  38. [38]

    Latent dirichlet allocation

    Blei DM, Ng A, Jordan MI. Latent dirichlet allocation. J Mach Learn Res 2003

    2003

  39. [39]

    Probabilistic topic models

    Blei DM. Probabilistic topic models. Commun ACM 2012;55:77–84. https://doi.org/10.1145/2133806.2133826

    work page doi:10.1145/2133806.2133826 2012

  40. [40]

    Research on building Chinese basic semantic lexicon

    Liu W, Zhu Y , Li C, Xiang H, Wen Z. Research on building Chinese basic semantic lexicon. J Comput Appl 2009;29:2875–7

    2009

  41. [41]

    Semiotic mediation in data governance: Towards valuing data as assets

    Benfeldt O, Persson JS. Semiotic mediation in data governance: Towards valuing data as assets. Inf Organ 2025;35:100588. https://doi.org/10.1016/j.infoandorg.2025.100588

    work page doi:10.1016/j.infoandorg.2025.100588 2025

  42. [42]

    Data Management Capability Maturity Assessment Model 2018

    National Information Center. Data Management Capability Maturity Assessment Model 2018

    2018

  43. [43]

    Commercial Bank Data Asset Quality Evaluation Model Based on Fermatean Fuzzy TOPSIS

    Dong L, Zhang M. Commercial Bank Data Asset Quality Evaluation Model Based on Fermatean Fuzzy TOPSIS. Procedia Comput Sci 2023;221:565–72. https://doi.org/10.1016/j.procs.2023.08.023

    work page doi:10.1016/j.procs.2023.08.023 2023

  44. [44]

    Carrying capacity evaluation of Beijing emergency shelters based on AHP-EWM multi- 46 objective decision modeling

    You D, Wang S, Wang F, Yan J, Wang Z, Liu W, et al. Carrying capacity evaluation of Beijing emergency shelters based on AHP-EWM multi- 46 objective decision modeling. Natl Remote Sens Bull 2024;28:2276–92

    2024

  45. [45]

    Research on the Construction of a Public Data Asset Quality Evaluation System

    Zheng F, Zhao M, Cao L. Research on the Construction of a Public Data Asset Quality Evaluation System. Data Asset Eval 2024:4–13

    2024

  46. [46]

    Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA

    Abbasi GA, Sandran T, Ganesan Y , Iranmanesh M. Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA. Technol Soc 2022;68:101937. https://doi.org/10.1016/j.techsoc.2022.101937

    work page doi:10.1016/j.techsoc.2022.101937 2022

  47. [47]

    Net and configurational effects of determinants on cloud computing adoption by SMEs under c loud promotion policy using PLS-SEM and fsQCA

    Chen M, Wang H, Liang Y , Zhang G. Net and configurational effects of determinants on cloud computing adoption by SMEs under c loud promotion policy using PLS-SEM and fsQCA. J Innov Knowl 2023;8:100388. https://doi.org/10.1016/j.jik.2023.100388

    work page doi:10.1016/j.jik.2023.100388 2023

  48. [48]

    Psychological and situational determinants shaping consumers’ intention to purchase eco -friendly personal care products: PLS -SEM and fsQCA insights

    Sobgo ADZ, Sun L, Atingabili S, Ngansom Kewou NY , Temagna Tcheudjeu A. Psychological and situational determinants shaping consumers’ intention to purchase eco -friendly personal care products: PLS -SEM and fsQCA insights. J Clean Prod 2025;522:146281. https://doi.org/10.1016/j.jclepro.2025.146281

    work page doi:10.1016/j.jclepro.2025.146281 2025

  49. [49]

    Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making

    Sukhov A, Friman M, Olsson LE. Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making. J Retail Consum Serv 2023;74:103424. https://doi.org/10.1016/j.jretconser.2023.103424

    work page doi:10.1016/j.jretconser.2023.103424 2023

  50. [50]

    Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models

    Becker J-M, Klein K, Wetzels M. Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Plann 2012;45:359–94. https://doi.org/10.1016/j.lrp.2012.10.001

    work page doi:10.1016/j.lrp.2012.10.001 2012

  51. [51]

    A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

    Hair J, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2022

    2022

  52. [52]

    Necessary But Not Sufficient

    Dul J. Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary But Not Sufficient” Causality. SSRN Electron J 2015. https://doi.org/10.2139/ssrn.2588480

    work page doi:10.2139/ssrn.2588480 2015

  53. [53]

    When predictors of outcomes are necessary: guidelines for the combined use of PLS- SEM and NCA

    Richter NF, Schubring S, Hauff S, Ringle CM, Sarstedt M. When predictors of outcomes are necessary: guidelines for the combined use of PLS- SEM and NCA. Ind Manag Data Syst 2020;120:2243–67. https://doi.org/10.1108/IMDS-11-2019-0638

    work page doi:10.1108/imds-11-2019-0638 2020

  54. [54]

    Using necessary condition analysis to complement multigroup analysis in partial least sq uares structural equation modeling

    Troiville J, Moisescu OI, Radomir L. Using necessary condition analysis to complement multigroup analysis in partial least sq uares structural equation modeling. J Retail Consum Serv 2025;82:104018. https://doi.org/10.1016/j.jretconser.2024.104018

    work page doi:10.1016/j.jretconser.2024.104018 2025

  55. [55]

    Conducting Necessary Condition Analysis

    Dul J. Conducting Necessary Condition Analysis. United Kingdom: SAGE Publishing Inc.; 2020

    2020

  56. [56]

    Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing

    Pappas IO, Woodside AG. Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. Int J Inf Manag 2021;58:102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310

    work page doi:10.1016/j.ijinfomgt.2021.102310 2021

  57. [57]

    Redesigning social inquiry: fuzzy sets and beyond

    Ragin CC. Redesigning social inquiry: fuzzy sets and beyond. Chicago (Ill.): University of Chicago Press; 2008. 47 Appendix A. Detailed Definitions of Indicators in the Data Asset Quality Assessment Framework I. Data Asset Management Capability 1.Data Strategy Refers to the organization’s top -level planning and implementation framework for data acquisiti...

    2008

  58. [58]

    It reflects process control, traceability, and long-term governance continuity

    Data Lifecycle Management Refers to the capability to manage data throughout its lifecycle, from creation and development to maintenance and retirement. It reflects process control, traceability, and long-term governance continuity

  59. [59]

    Data Integrated Environment Denotes the organizational context supporting data management, including leadership commitment, resource allocation, institutional support, and cultural embedding of data-driven practices. II. Data Quality Standard Conformity 1.Accuracy Refers to the extent to which data correctly represent real-world business conditions. It re...

  60. [60]

    It reflects spillover effects and shared-value realization beyond direct corporate profit

    Stakeholder Benefit Refers to the broader value created by data assets for multiple stakeholders, including customers, employees, shareholders, and partners. It reflects spillover effects and shared-value realization beyond direct corporate profit