New-Quality Productive Forces: Theoretical Frameworks and Policy Implications in the Digital Economy Era

Haoxuan Yin; Haichu Pan; Junbo Chen; Yunting Fan

doi:10.54097/497e4g63

Authors

Haoxuan Yin
Haichu Pan
Junbo Chen
Yunting Fan

DOI:

https://doi.org/10.54097/497e4g63

Keywords:

New Productivity, Digital Economy, Comparative Politics, Collaborative Governance, Policy Implications

Abstract

With the rise of the digital economy, new productivity is becoming a key driving force for economic growth and social change. With artificial intelligence, big data, cloud computing and other technologies as the core, and data-driven, intelligent decision-making and collaborative innovation as the distinctive characteristics, the new quality productivity puts forward new requirements for traditional production methods and governance models. However, different countries exhibit very different characteristics in managing the new quality of productive forces due to differences in political systems. From the perspective of comparative politics, this paper explores the similarities and differences between democratic and authoritarian political systems, constructs relevant theoretical frameworks, and analyzes the advantages and disadvantages of policy design and implementation based on the practical experience of typical countries (such as the United States, Germany, China, and Singapore). The results show that in the democratic system, the management model pays more attention to market leadership, data transparency and technological innovation, but there are challenges in data privacy protection and regulatory efficiency. However, under the centralized system, state-led strategic planning has obvious advantages and high policy implementation efficiency, but it may face innovation constraints and privacy disputes brought about by technology centralization. These differences reflect the diversity of resource allocation and governance logic under different systems. This paper further points out that the management of new productivity faces multiple challenges, such as technological ethics, policy lag, transnational collaboration and social inequality, and puts forward policy suggestions, including strengthening the construction of international cooperation framework, promoting multi-party collaborative governance between government and society, and optimizing the balance between social equity and technological innovation. The purpose of this study is to provide theoretical support and practical enlightenment for new quality productivity management in the context of globalization, and to contribute to the sustainable development of the digital economy era.

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References

[1] Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

[2] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

[3] Gandomi, A., & Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, 35(2), 137-144.

[4] Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE Publications.

[5] Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.

[6] Tapscott, D., & Williams, A. D. (2006). Wikinomics: How Mass Collaboration Changes Everything. Portfolio.

[7] Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New Public Management Is Dead—Long Live Digital-Era Governance. Journal of Public Administration Research and Theory, 16(3), 467-494.

[8] Osborne, S. P., Radnor, Z., & Nasi, G. (2013). A New Theory for Public Service Management? Towards a (Public) Service-Dominant Approach. American Review of Public Administration, 43(2), 135-158.

[9] Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, Adoption Barriers, and Myths of Open Data and Open Government. Information Systems Management, 29(4), 258-268.

[10] Helbing, D. (2019). Towards Digital Enlightenment: Essays on the Dark and Light Sides of the Digital Revolution. Springer.

[11] Meijer, A., & Bolívar, M. P. R. (2016). Governing the Smart City: A Review of the Literature on Smart Urban Governance. International Review of Administrative Sciences, 82(2), 392-408.

[12] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 205395171667967.

[13] Floridi, L., & Taddeo, M. (2016). What Is Data Ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360.

[14] Crawford, K., & Calo, R. (2016). There Is a Blind Spot in AI Research. Nature, 538(7625), 311-313.

[15] Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

[16] Whittlestone, J., Nyrup, R., Alexandrova, A., & Dihal, K. (2019). Ethical and Societal Implications of Algorithms, Data, and Artificial Intelligence: A Roadmap for Research. Nuffield Foundation.

[17] Bollier, D. (2010). The Promise and Peril of Big Data. Aspen Institute.

[18] George, G., Haas, M. R., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57(2), 321-326.

[19] Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The Real-World Use of Big Data. IBM Institute for Business Value.

[20] Kankanhalli, A., Zuiderwijk, A., & Tayi, G. K. (2017). Open Innovation in the Public Sector: A Research Agenda. Government Information Quarterly, 34(1), 84-89.

[21] Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy Practice and Digital Science: Integrating Complex Systems, Social Simulation and Public Administration in Policy Research. Springer.

[22] Brynjolfsson, E., & McAfee, A. (2011). Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Digital Frontier Press.

[23] Acemoglu, D., & Restrepo, P. (2018). Artificial Intelligence, Automation, and Work. Econometrics Laboratory Working Paper.

[24] Frey, C. B., & Osborne, M. A. (2017). The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254-280.

[25] Wang, F.-Y. (2010). The Emergence of Intelligent Enterprises: From CPS to CPSS. IEEE Intelligent Systems, 25(4), 85-88.

[26] Kitchin, R. (2014). Big Data, New Epistemologies, and Paradigm Shifts. Big Data & Society, 1(1), 1-12.