Longitudinal Analysis of Big Data and Technological Interventions in Industrial Performance


Journal of Computer and Communication Networks

Received On : 23 March 2025

Revised On : 25 May 2025

Accepted On : 30 June 2025

Published On : 30 July 2025

Volume 01, 2025

Pages : 151-161


PDF
Download

DOI
https://doi.org/10.64026/JCCN/2025015
Article Views
Abstract

This research aims to fill a gap in the body of knowledge by examining the impacts of technological advancements on operational productivity across various industries. Employing a longitudinal case study design encompassing three distinct industries (an Agri-Food firm, a construction firm, and a logistics firm), the research unfolds in two main phases aligned with Lewin's change management model: an unfreezing phase that was witnessed in January 2023 and refreezing phases that existed up to December 2023. Quantitative and qualitative data collection techniques used include face-to-face interviews, document reviews, and regression analysis to establish positive effects of technological solutions on organizational performance, sales, efficiency, and customer satisfaction. The research also recommends that increase of software infrastructure and engineering, market decision making and data analysis management are key factors to the performance of the industries in each sector. Using regression models, it also establishes that all the independent variables (SEI, DAMS, MDM) are majorly related to dependent variables, with coefficients (β) from 0. R2 value of 0.72 is relatively high, which indicates that the model has efficient explanatory power. These insights present recommendations to managers and policymakers who intend to utilize technology for organizational development and competitiveness.

Keywords

Technological Interventions, Big Data Solutions, Operational Performance, User-Driven Innovation, Data Analysis Management, Customer Efficiency and Satisfaction.

  1. W. Dyduch, P. Chudziński, S. Cyfert, and M. Zastempowski, “Dynamic capabilities, value creation and value capture: Evidence from SMEs under Covid-19 lockdown in Poland,” PLOS ONE, vol. 16, no. 6, p. e0252423, Jun. 2021, doi: 10.1371/journal.pone.0252423.
  2. L. Rodríguez-Mazahua, C.-A. Rodríguez-Enríquez, J. L. Sánchez-Cervantes, J. Cervantes, J. L. García-Alcaraz, and G. Alor-Hernández, “A general perspective of Big Data: applications, tools, challenges and trends,” The Journal of Supercomputing, vol. 72, no. 8, pp. 3073–3113, Aug. 2015, doi: 10.1007/s11227-015-1501-1.
  3. D. Romero and A. Molina, “Collaborative networked organisations and customer communities: value co-creation and co-innovation in the networking era,” Production Planning & Control, vol. 22, no. 5–6, pp. 447–472, Mar. 2011, doi: 10.1080/09537287.2010.536619.
  4. M. M. Mariani and S. Fosso Wamba, “Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies,” Journal of Business Research, vol. 121, pp. 338–352, Dec. 2020, doi: 10.1016/j.jbusres.2020.09.012.
  5. T. Fernandes and P. Remelhe, “How to engage customers in co-creation: customers’ motivations for collaborative innovation,” Journal of Strategic Marketing, vol. 24, no. 3–4, pp. 311–326, Dec. 2015, doi: 10.1080/0965254x.2015.1095220.
  6. B. Baldassarre, G. Calabretta, N. M. P. Bocken, and T. Jaskiewicz, “Bridging sustainable business model innovation and user-driven innovation: A process for sustainable value proposition design,” Journal of Cleaner Production, vol. 147, pp. 175–186, Mar. 2017, doi: 10.1016/j.jclepro.2017.01.081.
  7. B. Yenicioglu and A. Suerdem, “Participatory New Product Development–A Framework for Deliberately Collaborative and Continuous Innovation Design,” Procedia - Social and Behavioral Sciences, vol. 195, pp. 1443–1452, Jul. 2015, doi: 10.1016/j.sbspro.2015.06.442.
  8. O. Troisi, G. Maione, M. Grimaldi, and F. Loia, “Growth hacking: Insights on data-driven decision-making from three firms,” Industrial Marketing Management, vol. 90, pp. 538–557, Oct. 2020, doi: 10.1016/j.indmarman.2019.08.005.
  9. G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: Certain investigations for research and applications,” International Journal of Production Economics, vol. 176, pp. 98–110, Jun. 2016, doi: 10.1016/j.ijpe.2016.03.014.
  10. M. Z. Yaqub and A. Alsabban, “Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies,” Sustainability, vol. 15, no. 11, p. 8553, May 2023, doi: 10.3390/su15118553.
  11. H. Bouwman, S. Nikou, and M. de Reuver, “Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs?,” Telecommunications Policy, vol. 43, no. 9, p. 101828, Oct. 2019, doi: 10.1016/j.telpol.2019.101828.
  12. J. Chen, R. Zhang, and D. Wu, “Equipment Maintenance Business Model Innovation for Sustainable Competitive Advantage in the Digitalization Context: Connotation, Types, and Measuring,” Sustainability, vol. 10, no. 11, p. 3970, Oct. 2018, doi: 10.3390/su10113970.
  13. S. Hart, S. Sharma, and M. Halme, “Poverty, Business Strategy, and Sustainable Development,” Organization & Environment, vol. 29, no. 4, pp. 401–415, Nov. 2016, doi: 10.1177/1086026616677170.
  14. A. Z. Faroukhi, I. El Alaoui, Y. Gahi, and A. Amine, “Big data monetization throughout Big Data Value Chain: a comprehensive review,” Journal of Big Data, vol. 7, no. 1, Jan. 2020, doi: 10.1186/s40537-019-0281-5.
  15. R. T. Rust, C. Moorman, and P. R. Dickson, “Getting Return on Quality: Revenue Expansion, Cost Reduction, or Both?,” Journal of Marketing, vol. 66, no. 4, pp. 7–24, Oct. 2002, doi: 10.1509/jmkg.66.4.7.18515.
  16. M. R. Llave, “Business Intelligence and Analytics in Small and Medium-sized Enterprises: A Systematic Literature Review,” Procedia Computer Science, vol. 121, pp. 194–205, 2017, doi: 10.1016/j.procs.2017.11.027.
  17. O. E. Williamson, “Transaction-Cost Economics: The Governance of Contractual Relations,” The Journal of Law and Economics, vol. 22, no. 2, pp. 233–261, Oct. 1979, doi: 10.1086/466942.
  18. A. Di Nardo et al., “Smart Urban Water Networks: Solutions, Trends and Challenges,” Water, vol. 13, no. 4, p. 501, Feb. 2021, doi: 10.3390/w13040501.
  19. S. P. Calhoun, D. Akin, B. Zimmerman, and H. Neeman, “Large scale research data archiving: Training for an inconvenient technology,” Journal of Computational Science, vol. 36, p. 100523, Sep. 2019, doi: 10.1016/j.jocs.2016.07.005.
  20. J. D. Gomez and J. L. Wilson, “Age distributions and dynamically changing hydrologic systems: Exploring topography‐driven flow,” Water Resources Research, vol. 49, no. 3, pp. 1503–1522, Mar. 2013, doi: 10.1002/wrcr.20127.
  21. G. J. Schmitz et al., “Towards a metadata scheme for the description of materials – the description of microstructures,” Science and Technology of Advanced Materials, vol. 17, no. 1, pp. 410–430, Jan. 2016, doi: 10.1080/14686996.2016.1194166.
  22. V. Biagi and A. Russo, “Data Model Design to Support Data-Driven IT Governance Implementation,” Technologies, vol. 10, no. 5, p. 106, Oct. 2022, doi: 10.3390/technologies10050106.
  23. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
  24. M. A. Waller and S. E. Fawcett, “Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain,” Journal of Business Logistics, vol. 34, no. 4, pp. 249–252, Dec. 2013, doi: 10.1111/jbl.12024.
  25. Chen, Chiang, and Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, vol. 36, no. 4, p. 1165, 2012, doi: 10.2307/41703503.
  26. H. Hu, Y. Wen, T.-S. Chua, and X. Li, “Toward Scalable Systems for Big Data Analytics: A Technology Tutorial,” IEEE Access, vol. 2, pp. 652–687, 2014, doi: 10.1109/access.2014.2332453.
  27. S. Akter, S. F. Wamba, A. Gunasekaran, R. Dubey, and S. J. Childe, “How to improve firm performance using big data analytics capability and business strategy alignment?,” International Journal of Production Economics, vol. 182, pp. 113–131, Dec. 2016, doi: 10.1016/j.ijpe.2016.08.018.
  28. S. Mishra and A. Misra, “Structured and Unstructured Big Data Analytics,” 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 740–746, Sep. 2017, doi: 10.1109/ctceec.2017.8454999.
  29. S. M. Smith et al., “Advances in functional and structural MR image analysis and implementation as FSL,” NeuroImage, vol. 23, pp. S208–S219, Jan. 2004, doi: 10.1016/j.neuroimage.2004.07.051.
  30. I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan, “The rise of ‘big data’ on cloud computing: Review and open research issues,” Information Systems, vol. 47, pp. 98–115, Jan. 2015, doi: 10.1016/j.is.2014.07.006.
  31. A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. Maasberg, and K.-K. R. Choo, “Multimedia big data computing and Internet of Things applications: A taxonomy and process model,” Journal of Network and Computer Applications, vol. 124, pp. 169–195, Dec. 2018, doi: 10.1016/j.jnca.2018.09.014.
  32. R. Kitchin and G. McArdle, “What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets,” Big Data & Society, vol. 3, no. 1, Feb. 2016, doi: 10.1177/2053951716631130.
  33. N. Almazmomi, A. Ilmudeen, and A. A. Qaffas, “The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage,” Benchmarking: An International Journal, vol. 29, no. 4, pp. 1264–1283, Aug. 2021, doi: 10.1108/bij-01-2021-0021.
  34. Y. Shou, X. Zhao, and L. Chen, “Operations strategy of cloud-based firms: achieving firm growth in the Big Data era,” International Journal of Operations & Production Management, vol. 40, no. 6, pp. 873–896, Oct. 2019, doi: 10.1108/ijopm-01-2019-0089.
  35. F. Ciampi, S. Demi, A. Magrini, G. Marzi, and A. Papa, “Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation,” Journal of Business Research, vol. 123, pp. 1–13, Feb. 2021, doi: 10.1016/j.jbusres.2020.09.023.
  36. V. Kumar and D. Ramachandran, “Developing firms’ growth approaches as a multidimensional decision to enhance key stakeholders’ wellbeing,” International Journal of Research in Marketing, vol. 38, no. 2, pp. 402–424, Jun. 2021, doi: 10.1016/j.ijresmar.2020.09.004.
  37. H. Keshavarz, A. M. Mahdzir, H. Talebian, N. Jalaliyoon, and N. Ohshima, “The Value of Big Data Analytics Pillars in Telecommunication Industry,” Sustainability, vol. 13, no. 13, p. 7160, Jun. 2021, doi: 10.3390/su13137160.
  38. P. Galán-GarcÍa, J. G. D. L. Puerta, C. L. Gómez, I. Santos, and P. G. Bringas, “Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying,” Logic Journal of IGPL, p. jzv048, Oct. 2015, doi: 10.1093/jigpal/jzv048.
  39. G. Niu, R. S. Segall, Z. Zhao, and Z. Wu, “A Survey of Open-Source Statistical Software (OSSS) and Their Data Processing Functionalities,” International Journal of Open-Source Software and Processes, vol. 12, no. 1, pp. 1–20, Jan. 2021, doi: 10.4018/ijossp.2021010101.
  40. M. Fotache and C. Strimbei, “SQL and Data Analysis. Some Implications for Data Analysits and Higher Education,” Procedia Economics and Finance, vol. 20, pp. 243–251, 2015, doi: 10.1016/s2212-5671(15)00071-4.
  41. S. S. K. Seakomo, “Coupling databases and advanced analytic tools (R),” 2014. [Online]. Available: https://upcommons.upc.edu/bitstream /2099.1/24739/1/100281.pdf
  42. S. Oki et al., “Ch IP ‐Atlas: a data‐mining suite powered by full integration of public Ch IP ‐seq data,” EMBO Reports, vol. 19, no. 12, Nov. 2018, doi: 10.15252/embr.201846255.
  43. S. M. Dol and P. M. Jawandhiya, “Use of Data mining Tools in Educational Data Mining,” 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), pp. 380–387, Jul. 2022, doi: 10.1109/ccict56684.2022.00075.
  44. E. Ahmed et al., “The role of big data analytics in Internet of Things,” Computer Networks, vol. 129, pp. 459–471, Dec. 2017, doi: 10.1016/j.comnet.2017.06.013.
  45. S. C. Lewis, A. L. Guzman, and T. R. Schmidt, “Automation, Journalism, and Human–Machine Communication: Rethinking Roles and Relationships of Humans and Machines in News,” Digital Journalism, vol. 7, no. 4, pp. 409–427, Apr. 2019, doi: 10.1080/21670811.2019.1577147.
  46. R. A. Igawa et al., “Account classification in online social networks with LBCA and wavelets,” Information Sciences, vol. 332, pp. 72–83, Mar. 2016, doi: 10.1016/j.ins.2015.10.039.
  47. B. E. Wiggins and G. B. Bowers, “Memes as genre: A structurational analysis of the memescape,” New Media & Society, vol. 17, no. 11, pp. 1886–1906, May 2014, doi: 10.1177/1461444814535194.
  48. F. Kache and S. Seuring, “Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management,” International Journal of Operations & Production Management, vol. 37, no. 1, pp. 10–36, Jan. 2017, doi: 10.1108/ijopm-02-2015-0078.
  49. V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, “Creating Strategic Business Value from Big Data Analytics: A Research Framework,” Journal of Management Information Systems, vol. 35, no. 2, pp. 388–423, Apr. 2018, doi: 10.1080/07421222.2018.1451951.
  50. et al., “Big Data Research in Information Systems: Toward an Inclusive Research Agenda,” Journal of the Association for Information Systems, vol. 17, no. 2, pp. I–XXXII, Feb. 2016, doi: 10.17705/1jais.00423.
  51. D. J. Teece, “Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance,” Strategic Management Journal, vol. 28, no. 13, pp. 1319–1350, Aug. 2007, doi: 10.1002/smj.640.
  52. M. E. Prescott, “Big data and competitive advantage at Nielsen,” Management Decision, vol. 52, no. 3, pp. 573–601, May 2014, doi: 10.1108/md-09-2013-0437.
  53. R. F. Keefe et al., “Positioning Methods and the Use of Location and Activity Data in Forests,” Forests, vol. 10, no. 5, p. 458, May 2019, doi: 10.3390/f10050458.
  54. S. Khanra, A. Dhir, and M. Mäntymäki, “Big data analytics and enterprises: a bibliometric synthesis of the literature,” Enterprise Information Systems, vol. 14, no. 6, pp. 737–768, Mar. 2020, doi: 10.1080/17517575.2020.1734241.
  55. T. R. Shah, “Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry,” Technology in Society, vol. 68, p. 101801, Feb. 2022, doi: 10.1016/j.techsoc.2021.101801.
  56. R. Dahiya, S. Le, J. K. Ring, and K. Watson, “Big data analytics and competitive advantage: the strategic role of firm-specific knowledge,” Journal of Strategy and Management, vol. 15, no. 2, pp. 175–193, Feb. 2021, doi: 10.1108/jsma-08-2020-0203.
  57. C. Yew Wong and N. Karia, “Explaining the competitive advantage of logistics service providers: A resource-based view approach,” International Journal of Production Economics, vol. 128, no. 1, pp. 51–67, Nov. 2010, doi: 10.1016/j.ijpe.2009.08.026.
  58. P. J. DiMaggio and W. W. Powell, “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields,” American Sociological Review, vol. 48, no. 2, p. 147, Apr. 1983, doi: 10.2307/2095101.
  59. T. S. Gruca and L. L. Rego, “Customer Satisfaction, Cash Flow, and Shareholder Value,” Journal of Marketing, vol. 69, no. 3, pp. 115–130, Jul. 2005, doi: 10.1509/jmkg.69.3.115.66364.
  60. K. Barad, “Posthumanist Performativity: Toward an Understanding of How Matter Comes to Matter,” Signs: Journal of Women in Culture and Society, vol. 28, no. 3, pp. 801–831, Mar. 2003, doi: 10.1086/345321.
  61. Y. Simmhan et al., “Cloud-Based Software Platform for Big Data Analytics in Smart Grids,” Computing in Science & Engineering, vol. 15, no. 4, pp. 38–47, Jul. 2013, doi: 10.1109/mcse.2013.39.
  62. Y. K. Dwivedi et al., “Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 66, p. 102542, Oct. 2022, doi: 10.1016/j.ijinfomgt.2022.102542.
  63. M. Bertrand, S. Mullainathan, and E. Shafir, “Behavioral Economics and Marketing in Aid of Decision Making among the Poor,” Journal of Public Policy & Marketing, vol. 25, no. 1, pp. 8–23, Apr. 2006, doi: 10.1509/jppm.25.1.8.
  64. M. Marolt, H.-D. Zimmermann, A. Žnidaršič, and A. Pucihar, “Exploring Social Customer Relationship Management Adoption in Micro, Small and Medium-Sized Enterprises,” Journal of theoretical and applied electronic commerce research, vol. 15, no. 2, pp. 38–58, 2020, doi: 10.4067/s0718-18762020000200104.
  65. P. Parsa, M. D. Rossetti, S. Zhang, and E. A. Pohl, “Quantifying the benefits of continuous replenishment program for partner evaluation,” International Journal of Production Economics, vol. 187, pp. 229–245, May 2017, doi: 10.1016/j.ijpe.2017.02.017.
  66. A. Watkins and R. P. Hill, “A simulation of business-to-business decision making in a relationship marketing context,” Industrial Marketing Management, vol. 38, no. 8, pp. 994–1005, Nov. 2009, doi: 10.1016/j.indmarman.2008.06.002.
  67. L. Townsend, C. Wallace, A. Smart, and T. Norman, “Building Virtual Bridges: How Rural Micro‐Enterprises Develop Social Capital in Online and Face‐to‐Face Settings,” Sociologia Ruralis, vol. 56, no. 1, pp. 29–47, Nov. 2014, doi: 10.1111/soru.12068.
  68. S. Mouzas, “Efficiency versus effectiveness in business networks,” Journal of Business Research, vol. 59, no. 10–11, pp. 1124–1132, Oct. 2006, doi: 10.1016/j.jbusres.2006.09.018.
  69. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
  70. M. Grieger, “Electronic marketplaces: A literature review and a call for supply chain management research,” European Journal of Operational Research, vol. 144, no. 2, pp. 280–294, Jan. 2003, doi: 10.1016/s0377-2217(02)00394-6.
  71. S. Sakr, A. Liu, D. M. Batista, and M. Alomari, “A Survey of Large Scale Data Management Approaches in Cloud Environments,” IEEE Communications Surveys & Tutorials, vol. 13, no. 3, pp. 311–336, 2011, doi: 10.1109/surv.2011.032211.00087.
  72. R. Behrens et al., “Leveraging analytics to produce compelling and profitable film content,” Journal of Cultural Economics, vol. 45, no. 2, pp. 171–211, Jan. 2020, doi: 10.1007/s10824-019-09372-1.
  73. H. Zhou and W. C. Benton, “Supply chain practice and information sharing,” Journal of Operations Management, vol. 25, no. 6, pp. 1348–1365, Jan. 2007, doi: 10.1016/j.jom.2007.01.009.
  74. J. A. Blaya, H. S. F. Fraser, and B. Holt, “E-Health Technologies Show Promise In Developing Countries,” Health Affairs, vol. 29, no. 2, pp. 244–251, Feb. 2010, doi: 10.1377/hlthaff.2009.0894.
  75. S. Escolar, F. J. Villanueva, M. J. Santofimia, D. Villa, X. del Toro, and J. C. López, “A Multiple-Attribute Decision Making-based approach for smart city rankings design,” Technological Forecasting and Social Change, vol. 142, pp. 42–55, May 2019, doi: 10.1016/j.techfore.2018.07.024.
  76. D. S. Johnson, F. Bardhi, and D. T. Dunn, “Understanding how technology paradoxes affect customer satisfaction with self‐service technology: The role of performance ambiguity and trust in technology,” Psychology & Marketing, vol. 25, no. 5, pp. 416–443, Apr. 2008, doi: 10.1002/mar.20218.
  77. O. Eriksson and G. Goldkuhl, “Preconditions for public sector e-infrastructure development,” Information and Organization, vol. 23, no. 3, pp. 149–176, Jul. 2013, doi: 10.1016/j.infoandorg.2013.04.001.
  78. S. Aivaliotis et al., “An augmented reality software suite enabling seamless human robot interaction,” International Journal of Computer Integrated Manufacturing, vol. 36, no. 1, pp. 3–29, Jul. 2022, doi: 10.1080/0951192x.2022.2104459.
  79. W. Liu, J. Moultrie, and S. Ye, “The Customer-Dominated Innovation Process: Involving Customers as Designers and Decision-Makers in Developing New Product,” The Design Journal, vol. 22, no. 3, pp. 299–324, Apr. 2019, doi: 10.1080/14606925.2019.1592324.
  80. C. Yoon, C. A. Cole, and M. P. Lee, “Consumer decision making and aging: Current knowledge and future directions,” Journal of Consumer Psychology, vol. 19, no. 1, pp. 2–16, Jan. 2009, doi: 10.1016/j.jcps.2008.12.002.
  81. L. L. Gu, D. Skierkowski, P. Florin, K. Friend, and Y. Ye, “Facebook, Twitter, & Qr codes: An exploratory trial examining the feasibility of social media mechanisms for sample recruitment,” Computers in Human Behavior, vol. 60, pp. 86–96, Jul. 2016, doi: 10.1016/j.chb.2016.02.006.
CRediT Author Statement

The author reviewed the paper and approved the final version of the manuscript.

Acknowledgements

Authors thanks to School of Environmental Science for this research support.

Funding

No funding was received to assist with the preparation of this manuscript.

Ethics Declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of Data and Materials

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Author Information

Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.

Corresponding Author



Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/

Cite this Article

Bian Xiuwu Maochun, “Longitudinal Analysis of Big Data and Technological Interventions in Industrial Performance”, Journal of Computer and Communication Networks, pp. 151-161, 2025, doi: 10.64026/JCCN/2025015.

Copyright

© 2025 Bian Xiuwu Maochun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Journals
JCCN JDBIM JSSF
Policies
Publication Ethics Copyright Policy Open Access Policy Article Posting Policy Advertising Policy Privacy Policy Data Availability Policy
Ethics
Conflicts of interest/Competing interests Allegations of Misconduct Appeals Process Complaints Process Peer Review Process Authorship Criteria Terms and Conditions
Resources
Ansis Manager Contact Us
Information
Authors Editors Librarians Societies Journal News
© Ansis Publications, All Right Reserved. Designed By Ansis Publications
Home About Us Resource Contact