Policy Innovation Diffusion via Network Embeddedness: A Study of Big Data Policing Infrastructure Adoptions among Local Police Departments in Taiwan
Pages 754 - 763
Abstract
This paper examines how network embeddedness influences the diffusion of big data policing infrastructure among local police departments in Taiwan. Using the case of the electronic patrol signature system (EPSS), which replaces paper-based patrol logs with digital records to collect, store, and manage day-to-day patrol data (location, time, and person), a theoretical framework that links three policy diffusion mechanisms (learning, imitation, and competition) with network features and theories is proposed to improve identification. Through dyadic analysis of intrinsic characteristics and pairwise network relations of 22 local police departments, the results suggest that competitive pressure between structurally equivalent departments is the dominant factor that drives the rapid diffusion of EPSS, more than internal budgetary constraints or citizen-to-police ratio, while geographic proximity and larger centrality difference to adopters also facilitate policy learning and imitation. The paper contributes to the literature by providing a comprehensive identification strategy of policy diffusion mechanisms via network embeddedness, and by shedding light on the motivations and conditions for adopting big data policing technology in police organizations.
References
[1]
Eric Abrahamson and Lori Rosenkopf. 1997. Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation. Organization Science 8, 3 (June 1997), 289-309. https://doi.org/10.1287/orsc.8.3.289
[2]
Ministry of Interior Affairs. 2023. 全國警察機關陞遷序列表. Retrieved November 30 2023 from https://law.moj.gov.tw/LawClass/LawGetFile.ashx?FileId=0000345372&lan=C
[3]
National Police Agency. 2022. 警察偵查犯罪手冊. Retrieved March 5 2022 from https://www.ccpb.gov.tw/download/?parent_id=10439&type_id=10649
[4]
National Police Agency. 2022. 警察大事記. Retrieved March 5 2022 from https://www.npa.gov.tw/ch/app/folder/535
[5]
Venkatesh Bala and Sanjeev Goyal. 1998. Learning from Neighbours. The Review of Economic Studies 65, 3 (July 1998), 595-621. https://doi.org/10.1111/1467-937X.00059
[6]
Victor Bekkers, Jurian Edelenbos and Bram Steijn. 2011. Innovation in the public sector. Springer, 2011.
[7]
Christopher M. Weible and Paul A. Sabatier (Ed.). 2018. Theories of the Policy Process (4 ed.). Innovation and Diffusion Models in Policy Research. Routledge, New York. https://doi.org/10.4324/9780429494284
[8]
Frances Stokes Berry and William D. Berry. 1990. State Lottery Adoptions as Policy Innovations: An Event History Analysis. American Political Science Review 84, 2 (June 1990), 395-415. https://doi.org/10.2307/1963526
[9]
Joachim Blatter, Lea Portmann and Frowin Rausis. 2022. Theorizing policy diffusion: from a patchy set of mechanisms to a paradigmatic typology. Journal of European Public Policy 29, 6 (June 2022), 805-825. https://doi.org/10.1080/13501763.2021.1892801
[10]
Frederick J. Boehmke. 2009. Policy Emulation or Policy Convergence? Potential Ambiguities in the Dyadic Event History Approach to State Policy Emulation. The Journal of Politics 71, 3 (July 2009), 1125-1140. https://doi.org/10.1017/s0022381609090926
[11]
Sarah Brayne and Angèle Christin. 2020. Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts. Social Problems 68, 3 (March 2020), 608-624. https://doi.org/10.1093/socpro/spaa004
[12]
George W. Burruss and Matthew J. Giblin. 2014. Modeling Isomorphism on Policing Innovation:The Role of Institutional Pressures in Adopting Community-Oriented Policing. Crime & Delinquency 60, 3, 331-355. https://doi.org/10.1177/0011128709340225
[13]
Ronald S. Burt. 1987. Social Contagion and Innovation: Cohesion versus Structural Equivalence. American Journal of Sociology 92, 6 (May 1987), 1287-1335. https://doi.org/10.1086/228667
[14]
Ronald S. Burt. 2000. Decay functions. Social Networks 22, 1 (May 2000), 1-28. https://doi.org/10.1016/S0378-8733(99)00015-5
[15]
Xun Cao. 2010. Networks as Channels of Policy Diffusion: Explaining Worldwide Changes in Capital Taxation, 1998–2006. International Studies Quarterly 54, 3 (Sept. 2010), 823-854. https://doi.org/10.1111/j.1468-2478.2010.00611.x
[16]
Xun Cao and Aseem Prakash. 2010. Growing Exports by Signaling Product Quality: Trade Competition and the Cross-National Diffusion of ISO 9000 Quality Standards. Journal of Policy Analysis and Management 30, 1 (Nov. 2010), 111-135. https://doi.org/10.1002/pam.20546
[17]
David Chandler and Hokyu Hwang. 2015. Learning From Learning Theory: A Model of Organizational Adoption Strategies at the Microfoundations of Institutional Theory. Journal of Management 41, 5 (July 2015), 1446-1476. https://doi.org/10.1177/0149206315572698
[18]
Hsu-Ting Chen and Tong-Yi Huang. 2011. Why did the Pioneer Turn into a Follower? The Policy Learning Process of Taipei City's Adoption of Traffic Signal Countdown Displays. Journal of Public Administration (National Chengchi University), 40 (Sept. 2011), 77-112. https://doi.org/10.30409/JPA.201109_(40).0003
[19]
Skyler J. Cranmer, Bruce A. Desmarais and Benjamin W. Campbell. 2020. The contagion of democracy through international networks. Social Networks 61(May 2020), 87-98. https://doi.org/10.1016/j.socnet.2019.08.009
[20]
Hanna De Vries, Victor Bekkers and Lars Tummers. 2016. Innovation in the public sector: a systematic review and future research agenda. Public Adm. 94, 1 (Sept. 2015), 146-166. https://doi.org/10.1111/padm.12209
[21]
Paul J. DiMaggio and Walter W. Powell. 1983. The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review 48, 2 (April 1983), 147-160. https://doi.org/10.2307/2095101
[22]
Patrick Dunleavy. 1994. The Globalization of Public Services Production: Can Government be 'Best in World'? Public Policy and Administration 9, 2 ( June 1994), 36-64. https://doi.org/10.1177/095207679400900204
[23]
Patrick Dunleavy, Helen Margetts, Simon Bastow and Jane Tinkler. 2005. New Public Management Is Dead—Long Live Digital-Era Governance. Journal of Public Administration Research and Theory 16, 3 (Sept. 2005), 467-494. https://doi.org/10.1093/jopart/mui057
[24]
Michael Egnoto, Gary Ackerman, Irina Iles, Holly Ann Roberts, Daniel Steven Smith, Brooke Fisher Liu and Brandon Behlendorf. 2017. What motivates the blue line for technology adoption? Insights from a police expert panel and survey. Policing: An International Journal of Police Strategies & Management 40, 2 (May 2017), 306-320. https://doi.org/10.1108/PIJPSM-03-2016-0031
[25]
Fabrizio Gilardi and Katharina Füglister. 2008. Empirical Modeling of Policy Diffusion in Federal States: The Dyadic Approach. Swiss Political Science Review 14, 3 (Jan. 2008), 413-450. https://doi.org/10.1002/j.1662-6370.2008.tb00108.x
[26]
Fabrizio Gilardi. 2010. Who Learns from What in Policy Diffusion Processes? American Journal of Political Science 54, 3 (July 2010), 650-666. https://doi.org/10.1111/j.1540-5907.2010.00452.x
[27]
Nihit Goyal. 2022. Policy Diffusion Through Multiple Streams: The (Non-)Adoption of Energy Conservation Building Code in India. Policy Studies Journal 50, 3 (Jan. 2022), 641-669. https://doi.org/10.1111/psj.12415
[28]
Mark Granovetter. 1985. Economic Action and Social Structure: The Problem of Embeddedness. American Journal of Sociology 91, 3 (Nov. 1985), 481-510. https://doi.org/10.1086/228311
[29]
Virginia Gray. 1973. Innovation in the States: A Diffusion Study. American Political Science Review 67, 4 (Dec. 1973), 1174-1185. https://doi.org/10.2307/1956539
[30]
Ranjay Gulati. 1998. Alliances and Networks. Strategic Management Journal 19, 4 (Dec. 1998), 293-317. https://doi.org/10.1002/(SICI)1097-0266(199804)19:4<293::AID-SMJ982>3.0.CO;2-M
[31]
Markku Jokisaari and Jukka Vuori. 2010. The Role of Reference Groups and Network Position in the Timing of Employment Service Adoption. Journal of Public Administration Research and Theory 20, 1 (Feb. 2010), 137-156. https://doi.org/10.1093/jopart/mun039
[32]
Marlene Kammerer and Chandreyee Namhata. 2018. What drives the adoption of climate change mitigation policy? A dynamic network approach to policy diffusion. Policy Sciences 51, 4 (Dec. 2018), 477-513. https://doi.org/10.1007/s11077-018-9332-6
[33]
Andrew Karch and Matthew Cravens. 2014. Rapid Diffusion and Policy Reform: The Adoption and Modification of Three Strikes Laws. State Politics & Policy Quarterly 14, 4 (Dec. 2014), 461-491. https://doi.org/10.1177/1532440014561867
[34]
Gary King and Langche Zeng. 2001. Logistic Regression in Rare Events Data. Political Analysis 9, 2 (Jan. 2017), 137-163. https://doi.org/10.1093/oxfordjournals.pan.a004868
[35]
Julian Laufs and Hervé Borrion. 2021. Technological innovation in policing and crime prevention: Practitioner perspectives from London. International Journal of Police Science & Management 24, 2 (June 2022), 190-209. https://doi.org/10.1177/14613557211064053
[36]
Chang Kil Lee and David Strang. 2006. The International Diffusion of Public-Sector Downsizing: Network Emulation and Theory-Driven Learning. International Organization 60, 4 (Oct. 2006), 883-909. https://doi.org/10.1017/S0020818306060292
[37]
In-Won Lee, Richard C. Feiock and Youngmi Lee. 2012. Competitors and Cooperators: A Micro-Level Analysis of Regional Economic Development Collaboration Networks. Public Administration Review 72, 2 (March 2012), 253-262. https://doi.org/10.1111/j.1540-6210.2011.02501.x
[38]
Jun Li and Ken Chung. 2018. Decomposing Isomorphism: What Drives Similarity in the Adoption of New Public Management? Administration & Society 52, 3 (March 2020), 375-404. https://doi.org/10.1177/0095399718811307
[39]
Chun-Sung Liao and Hsien-Hua Tseng. 2006. A Primer on Interdisciplinary Governance of Local Police Under Globalization. Chinese Local Self-Government 59, 2 (Feb. 2006), 3-18. https://doi.org/10.6581/lsgc.2006.59(2).02
[40]
Yueh Lin. 2017. Regional Drug Detection and Prevention Conference: Yun Jia Nan Police, Coast Guard, and Customs Gathered at the Tainan District Prosecutors Office (June 8 2017). Retrieved June 20, 2021 from https://www.ettoday.net/news/20170608/941088.htm
[41]
Weixing Liu and Hongtao Yi. 2023. Policy diffusion through leadership transfer networks: Direct or indirect connections? Governance 36, 2 (April 2023), 359-378. https://doi.org/10.1111/gove.12609
[42]
François Lorrain and Harrison C. White. 1971. Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology 1, 1 (1971), 49-80. https://doi.org/10.1080/0022250X.1971.9989788
[43]
Mark Lubell, John Scholz, Ramiro Berardo and Garry Robins. 2012. Testing Policy Theory with Statistical Models of Networks. Policy Studies Journal 40, 3 (Aug. 2012), 351-374. https://doi.org/10.1111/j.1541-0072.2012.00457.x
[44]
Martino Maggetti and Fabrizio Gilardi. 2016. Problems (and solutions) in the measurement of policy diffusion mechanisms. Journal of Public Policy 36, 1 (March 2015), 87-107. https://doi.org/10.1017/S0143814X1400035X
[45]
Daniel J. Mallinson. 2021. Growth and gaps: a meta-review of policy diffusion studies in the American states. Policy & Politics 49, 3 (July 2021), 369-389. https://doi.org/10.1332/030557321X16119271286848
[46]
Edward McCord. 1980. Structural-functionalism and the network idea: towards an integrated methodology. Social Networks 2, 4 (1980), 371-383. https://doi.org/10.1016/0378-8733(80)90004-0
[47]
Xerxes Minocher and Caelyn Randall. 2020. Predictable policing: New technology, old bias, and future resistance in big data surveillance. Convergence 26, 5-6 (Dec. 2020), 1108-1124. https://doi.org/10.1177/1354856520933838
[48]
Michael Mintrom and Sandra Vergari. 1998. Policy Networks and Innovation Diffusion: The Case of State Education Reforms. The Journal of Politics 60, 1 (Nov. 1998), 126-148. https://doi.org/10.2307/2648004
[49]
Joshua L. Mitchell. 2018. Does Policy Diffusion Need Space? Spatializing the Dynamics of Policy Diffusion. Policy Studies Journal 46, 2 (May 2018), 424-451. https://doi.org/10.1111/psj.12226
[50]
Ishmael Mugari and Emeka E. Obioha. 2021. Predictive Policing and Crime Control in The United States of America and Europe: Trends in a Decade of Research and the Future of Predictive Policing. Social Sciences 10, 6 (June 2021), 234. https://doi.org/10.3390/socsci10060234
[51]
Sean Nicholson-Crotty and Sanya Carley. 2015. Effectiveness, Implementation, and Policy Diffusion: Or “Can We Make That Work for Us?”. State Politics & Policy Quarterly 16, 1 (March 2016), 78-97. https://doi.org/10.1177/1532440015588764
[52]
Laurence J. O'Toole Jr. 2015. Networks and Networking: The Public Administrative Agendas. Public Administration Review 75, 3 (Oct. 2015), 361-371. https://doi.org/10.1111/puar.12281
[53]
Francesca Pallotti and A. Lomi. 2011. Network influence and organizational performance: The effects of tie strength and structural equivalence. European Management Journal 29, 5 (Oct. 2011 ), 389-403. https://doi.org/10.1016/j.emj.2011.02.005
[54]
Gabriele Pellegrino and Maria Savona. 2017. No money, no honey? Financial versus knowledge and demand constraints on innovation. Research Policy 46, 2 (March 2017), 510-521. https://doi.org/10.1016/j.respol.2017.01.001
[55]
Henning Piezunka, Wonjae Lee, Richard Haynes and Matthew S. Bothner. 2018. Escalation of competition into conflict in competitive networks of Formula One drivers. Proceedings of the National Academy of Sciences 115, 15 (April 2018), E3361-E3367. https://doi.org/10.1073/pnas.1717303115
[56]
E.M. Rogers. 2003. Diffusion of Innovations, 5th Edition. Free Press, 2003.
[57]
Lee Douglas Sailer. 1978. Structural equivalence: Meaning and definition, computation and application. Social Networks 1, 1 (1978), 73-90. https://doi.org/10.1016/0378-8733(78)90014-X
[58]
Charles R. Shipan and Craig Volden. 2008. The Mechanisms of Policy Diffusion. American Journal of Political Science 52, 4 (Oct. 2008), 840-857. https://www.jstor.org/stable/25193853
[59]
Charles R. Shipan and Craig Volden. 2012. Policy Diffusion: Seven Lessons for Scholars and Practitioners. Public Administration Review 72, 6 (Aug. 2012), 788-796. https://doi.org/10.1111/j.1540-6210.2012.02610.x
[60]
Peter Starke. 2013. Qualitative Methods for the Study of Policy Diffusion: Challenges and Available Solutions. Policy Studies Journal 41, 4 (Nov. 2013), 561-582. https://doi.org/10.1111/psj.12032
[61]
Hamed Taherdoost. 2018. A review of technology acceptance and adoption models and theories. Procedia Manufacturing 22(2018), 960-967. https://doi.org/10.1016/j.promfg.2018.03.137
[62]
Manuel P. Teodoro. 2009. Bureaucratic Job Mobility and The Diffusion of Innovations. American Journal of Political Science 53, 1 (2009), 175-189. https://doi.org/10.1111/j.1540-5907.2008.00364.x
[63]
Police Torch. 2022. 人事動態. Retrieved March 12 2022 from https://police.npa.gov.tw/ch/app/data/list?module=wg014&id=1836
[64]
Nicola Ulibarri and Tyler A. Scott. 2017. Linking Network Structure to Collaborative Governance. Journal of Public Administration Research and Theory 27, 1 (Jan. 2017), 163-181. https://doi.org/10.1093/jopart/muw041
[65]
Machiel van der Heijden and Jelmer Schalk. 2020. Network relationships and standard adoption: Diffusion effects in transnational regulatory networks. Public Adm. 98, 3 (Sept. 2020), 768-784. https://doi.org/10.1111/padm.12627
[66]
Anders R. Villadsen. 2011. Structural Embeddedness of Political Top Executives as Explanation of Policy Isomorphism. Journal of Public Administration Research and Theory 21, 4 (Oct. 2011), 573-599. https://doi.org/10.1093/jopart/mur007
[67]
Craig Volden. 2006. States as Policy Laboratories: Emulating Success in the Children's Health Insurance Program. American Journal of Political Science 50, 2 (April 2006), 294-312. https://doi.org/10.1111/j.1540-5907.2006.00185.x
[68]
Jack L. Walker. 1969. The Diffusion of Innovations among the American States. The American Political Science Review 63, 3 (Sept. 1969), 880-899. https://doi.org/10.2307/1954434
[69]
Guang-Xu Wang. 2015. The Application of Social Network Analysis to Public Administration Research. Survey Research—Method and Application, 34 (Oct. 2015), 67-134.
[70]
Hugh Ward and Peter John. 2013. Competitive Learning in Yardstick Competition: Testing Models of Policy Diffusion With Performance Data. Political Science Research and Methods 1, 1 (June 2013), 3-25. https://doi.org/10.1017/psrm.2013.1
[71]
Stanley Wasserman and Katherine Faust. 1994. Social network analysis: Methods and applications. Cambridge University Press, New York, NY, US, 1994. https://doi.org/10.1017/CBO9780511815478
[72]
I-Sen Weng. 2021. Yun Jia Nan's "Regional Joint-Defense" found 5 DUIs and arrested 3 people. (May 1). Retrieved June 20, 2021 from https://www.ettoday.net/news/20210501/1971925.htm#ixzz6ytM0L0xN
[73]
Hongtao Yi and John T. Scholz. 2016. Policy Networks in Complex Governance Subsystems: Observing and Comparing Hyperlink, Media, and Partnership Networks. Policy Studies Journal 44, 3 (Aug. 2016), 248-279. https://doi.org/10.1111/psj.12141
[74]
Hongtao Yi, Frances Stokes Berry and Wenna Chen. 2018. Management Innovation and Policy Diffusion through Leadership Transfer Networks: An Agent Network Diffusion Model. Journal of Public Administration Research and Theory 28, 4 (Oct. 2018), 457-474. https://doi.org/10.1093/jopart/muy031
[75]
Hongtao Yi and Wenna Chen. 2019. Portable Innovation, Policy Wormholes, and Innovation Diffusion. Public Administration Review 79, 5 (Sept. 2019), 737-748. https://doi.org/10.1111/puar.13090
[76]
Jacob T. N. Young and Justin T. Ready. 2015. Diffusion of Ideas and Technology:The Role of Networks in Influencing the Endorsement and Use of On-Officer Video Cameras. Journal of Contemporary Criminal Justice 31, 3 (Aug. 2015), 243-261. https://doi.org/10.1177/1043986214553380
Index Terms
- Policy Innovation Diffusion via Network Embeddedness: A Study of Big Data Policing Infrastructure Adoptions among Local Police Departments in Taiwan
Recommendations
Multi-network embeddedness and innovation performance of R&D employees
AbstractTaking the perspective of multi-network embeddedness, this paper constructs the collaboration network of R&D organizations, the collaboration network and knowledge network of R&D employees based on the patent data of 879 R&D employees from 224 R&D ...
Start-up firms' networks for innovation: embedded in entrepreneurs' networks in private and public spheres
ASONAM '14: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningWe develop a model of how a start-up firm's networking for innovation is embedded in the personal network around the entrepreneur. Using data from the Global Entrepreneurships Monitor including 11,792 start-ups from 38 countries surveyed in 2012--13, we ...
Structural embeddedness and innovation diffusion: the moderating role of industrial technology grouping
Industrial technology grouping is a common phenomenon that occurs as an industry develops and evolves. However, the research on innovation diffusion has given little attention to the role of industrial technology grouping. This paper extends the prior ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2024
1089 pages
ISBN:9798400709883
DOI:10.1145/3657054
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
dg.o 2024
dg.o 2024: 25th Annual International Conference on Digital Government Research
June 11 - 14, 2024
Taipei, Taiwan
Acceptance Rates
Overall Acceptance Rate 150 of 271 submissions, 55%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 25Total Downloads
- Downloads (Last 12 months)25
- Downloads (Last 6 weeks)8
Reflects downloads up to 20 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format