Next Article in Journal
Identification and Factor Analysis of Traffic Conflicts in the Merge Area of Freeway Work Zone
Previous Article in Journal
A Simulation-Based Study on the Optimal Pricing Strategy of Supply Chain System
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 14
10.3390/su151411304
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

How Organizational Agility Promotes Digital Transformation: An Empirical Study

by
Hui Zhang
,
Huiying Ding
and
Jianying Xiao
*
Digital Rural Service Research Center, School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11304; https://doi.org/10.3390/su151411304
Submission received: 22 June 2023 / Revised: 16 July 2023 / Accepted: 18 July 2023 / Published: 20 July 2023
(This article belongs to the Section Sustainable Management)
Browse Figures
Versions Notes

Abstract

:
With the development of digital technologies and their increasing application in government, digital transformation is a wave rolling up the world. Previous studies had investigated some factors that affect digital transformation. But there is little research on the impact of organizational agility on digital transformation in government. To fill this gap, based on the dynamic capabilities view, this study aims to investigate how organizational agility affects digital transformation and dynamic capabilities as antecedents and factors impacting organizational agility. A survey study was conducted to empirically test the model. The data were collected from 313 government employees in government departments. The findings suggest that (1) organizational agility significantly influences digital transformation and (2) dynamic capabilities are important predictors of organizational agility.

1. Introduction

With the development of digital technologies and their increasing application in government, digital transformation is turning the tide in the world. COVID-19 accelerated the process of digital transformation [1,2]. Thus, in a VUCA environment, digital transformation is “not an option but a necessity for governments to respond to crises” [3,4] and citizens’ expectations [5,6]. Digital transformation has increasingly received attention from scholars and practitioners in the field of public administration.
Previous studies had investigated some factors that affect digital transformation [3,7,8,9,10]. But there is little research on organizational agility influencing digital transformation in government. While organizational agility plays an important role in digital transformation in a VUCA environment [11]. Moreover, in order to promote digital transformation, governments need to build dynamic capabilities [12,13]. Dynamic capabilities are the basis of organizational agility, which could respond to digital transformations [14]. But there is little research on dynamic capabilities affecting organizational agility in the topic of government digital transformation. Furthermore, previous studies have mainly relied on qualitative research; empirical studies are rare [9].
Thus, the main research questions of interest to this study were as follows: (1) What are the impacts of organizational agility on local governments’ digital transformation in China? (2) What are the degrees of influence of dynamic capabilities on organizational agility in digital transformation? To address these questions, the dynamic capabilities view is drawn upon to propose a model of digital transformation in government organizations in China.
The rest of the paper proceeds as follows. The second section reviews the literature in relevant fields. Theoretical analysis and research hypotheses are presented in Section 3. The fourth section describes the research methods used in this study. The results of the data analysis are presented in Section 5. The final section presents the findings and research implications of this study.

2. Literature Review

Digital transformation is a hot topic in the field of public administration. According to the framework of [5], the studies on government digital transformation could be divided into three categories: reasons, process, and results.
The reasons for government digital transformation around the world include (1) Environmental aspects. Digital transformation is an effective tool for the government’s VUCA environment to overcome multiple threats and social conflicts [3,4,15]. COVID-19 was an accelerator of digital transformation [1,2] by changing the attitudes of public managers and government employees toward digital transformation [16,17]. (2) Technology change. Technology advances play a key role in governments’ transformation [5,7,10]. With the application of digital technology in government, it has changed the government’s operations [18], structures [19], and public services [20]. (3) Organizational aspects. The government faced tightening fiscal difficulties [21,22] and cost reductions [5,23]. Thus, governments should increase their efficiency by using digital technology to change circumstances [24,25]. (4) People aspects: citizens expect to interact digitally with the government because of technical advancement and social evolution [2,5,6,16].
The digital transformation process includes: (1) digitizing relationships. Digital technology application in government has been promoting better collaborative partnerships between governments and citizens [25,26], the private sector [15], and stakeholders [27]. (2) Digitize the service. The government uses digital technology to digitize services for citizens, such as digital museums [1], digital healthcare services [28], and virtual courts [20]. (3) Using new technology. Digital transformation in government occurs through implementing or using such technologies as AI [29,30,31], big data [32,33,34], IoT [35,36], cloud computing [37], and blockchain [38,39].
The outcomes that appeared in studies included improved services, better relationships, and improved policies. (1) Improved services. The application of digital technology in government has improved some services, such as user-centric service improvement [21], an open platform for government service delivery [40], and speedy trials in the judiciary [41]. (2) Better relationships. The application of digital technology in government sectors has promoted better communication between citizens and the government [42]. Thus, promoting citizen participation [43] and enhancing citizens’ trust in government [44]. (3) Improved policies. The application of digital technology has influenced the public policy cycle [30,44], such as public policy analysis [45,46], policy-making [32,34,47], and policy decision-making [48]. With the references above, this study will mainly study the importance of organizational agility in government digital transformation.

3. Theoretical Analysis and Research Hypothesis

3.1. Theoretical Analysis Framework

The dynamic capabilities view is a further extension and development of the resource-based view [49,50,51]. Dynamic capabilities refer to “the firm’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments” [50]. Thus, dynamic capabilities focus on changing the organization’s resources and competencies to respond to turbulent environments and achieve sustainable competitive advantage [50,51].
Dynamic capabilities consist of three capabilities: sensing capability, seizing capability, and reconfiguring capability [51]. (1) Sensing capability is the ability of organizations to sense opportunities and threats, which involves “scanning, creating, learning, and interpreting activities” [51]. (2) Seizing capability could be understood as an organization’s ability to seize opportunities or respond to threats, which are “addressed through new products, processes, or services” [51]. (3) Reconfiguring capability, also known as transforming capability, is the “continuous alignment and realignment of specific tangible and intangible assets” [51].
Although the dynamic capability view originates from business management, it was also applied to the field of public administration [9,52,53,54]. There are many issues using the dynamic capability view as a theoretical lens, such as smart cities [55,56], emergency management [12,57], and public policy [58,59]. While the studies using dynamic capabilities in public administration are still limited [9,12,53,54], scholars should pay more attention to dynamic capabilities in public change management [12,60].
As for issues of digital transformation in government, the dynamic capabilities view is a guidance lens for the digital transformation process in public service organizations [61]. Dynamic capabilities help the government transform from one stage to the next [62]. Dynamic capabilities could prompt digital transformation through real-time sensing and response [35]. Dynamic capabilities also play an important role in improving the ability of platform leaders to create and capture value [63] and design and facilitate digital services [61]. Thus, the dynamic capability view as a theoretical foundation is suited for this research. Meanwhile, this research expands and deepens the application of dynamic capabilities in digital transformation in government.
Based on the dynamic capability view, the research model shown in Figure 1 was developed. The model suggests that organizational agility affects digital transformation performance and dynamic capabilities as antecedents and factors affecting organizational agility. Then, the analysis framework will be explained further.

3.2. Research Model and Research Hypothesis

Based on the dynamic capabilities view, the research model shown in Figure 2 was developed. The model suggests that organizational agility affects digital transformation performance and dynamic capabilities as antecedents and factors impacting organizational agility.
(1)
Dynamic capabilities and organizational agility
The rapid and unpredictable changes in the environment [54,64] create significant challenges for governments [12,65,66]. Thus, governments need highly dynamic capabilities to respond to these challenges and adapt to turbulence [9,52,53,67,68]. But dynamic capabilities are often missing in government sectors [12,58,69]. Dynamic capabilities are a precondition for government digital transformation [70]. Thus, the government needs to build dynamic capabilities for digital transformation [12,13].
Organizational agility is “a firm’s ability to cope with rapid, relentless, and uncertain changes and thrive in a competitive environment of continually and unpredictably changing opportunities” [71]. There are many antecedents and factors that affect organizational agility [72,73,74]. Dynamic capabilities play an important role in shaping organizational agility [71,75,76,77]. Dynamic capabilities are “necessary for fostering organizational agility to address deep uncertainty” [77]. Thus, dynamic capabilities are key predictors of organizational agility [71,76]. Previous conceptual and empirical research studies show that dynamic capabilities and their composition have positive effects on organizational agility and its dimensions [75,76,77,78,79,80].
Organizational agility in this study consists of operational adjustment agility and strategic agility. The reason is that organizational agility requires organizations to develop operational and strategic flexibility [81,82]. Previous studies have empirically shown that dynamic capabilities have a positive effect on operational adjustment agility [75,80,83] and strategic agility [84,85]. These lead to the second hypothesis:
H1. 
Dynamic capabilities have a positive impact on organizational agility.
H1a. 
Sensing capability has a positive impact on operational adjustment agility.
H1b. 
Seizing capability has a positive impact on operational adjustment agility.
H1c. 
Reconfiguring capability has a positive impact on operational adjustment agility.
H1d. 
Sensing capability has a positive impact on strategic agility.
H1e. 
Seizing capability has a positive impact on strategic agility.
H1f. 
Reconfiguring capability has a positive impact on strategic agility.
(2)
Organizational agility and digital transformation performance
Organizational agility has been proven to have a positive impact on organizational performance in the field of business management [11,86]. Organizational agility becomes an important determinant of governmental performance in changing environments [87,88]. Moreover, organizational agility plays an important role in digital transformation. Lack of organizational agility is one of the top barriers to government digital transformation [89]. Some studies have found the impact of organizational agility on digital government transformation [15,90,91]. Hence, the following hypothesis is formulated:
H2. 
Organizational agility is significantly and positively related to digital transformation performance.
H2a. 
Operational adjustment agility is significantly and positively related to digital transformation performance.
H2b. 
Strategic agility is significantly and positively related to digital transformation performance.

4. Materials and Methods

Variable Selection

The design of the questionnaire is based on the theoretical hypotheses of the research model. The survey contents include the hypothesis variables: (1) Sensing capability (SNC), involving three observation variables, respectively, is “our organization scan the environment and identify new opportunities (SNC1)”; “our organization reviews our service development efforts to ensure they are in line with what the citizens want (SNC2)”; “our organization implement ideas for new services and improve existing services (SNC3)” [80]. (2) Seizing capability (SIC), involving three observation variables. In addition, “our organization invests in finding solutions for our citizens (SIC1)” “our organization responds to defects pointed out by government employees (SIC2)” and “our organization changes our practices when citizen feedback gives us a reason to change (SIC3)” [92]. (3) Reconfiguring capability (RC), including three observation variables, “our organization can easily add an eligible new partner or remove ones (RC1)” “Our organization can adjust our business processes in response to shifts in our business priorities (RC2)” “Our organization can reconfigure our business processes in order to come up with new service assets (RC3)” [80]. (4) Operational adjustment agility (OAA), including “Our organization can better meet demands for rapid-response, special requests of our customers whenever such demands arise (OAA1)” “Our organization can quickly scale up or scale down our service levels to support fluctuations in demand from the citizens (OAA2)” “Whenever there is a disruption in supply our organization can quickly make necessary alternative arrangements and internal adjustments (OAA 3)” [71]. (5) Strategic agility (SA), including “If circumstances change, our organization can easily change its current plans (SA1)” “our organization is prepared to react in a modified and viable manner (SA2)” “our organization can control a shift in strategy (SA3)” “our organization can pro-actively develop a new project (SA4)” [93]. (6) Digital transformation performance (DTP), including “our organization implement a digital platform-based business model (DTP1)” “our organization flexibly adjust the structure of functional departments (DTP2)” “our organization establish a decision making and control system based on data analysis (DTP3)” [94].4.2. Study Area and Data Source
Jiangsu Province is located in the Yangtze River Delta. The 2022 GNP of Jiangsu Province ranks second in China and belongs to the economically developed region. In 2023, Jiangsu will make a series of deployments to promote the construction of a digital government. Jiangsu Province is at the forefront of the country in terms of digital transformation. Therefore, we chose Jiangsu as the sample population. Figure 3 shows the geographical location of Jiangsu Province in China.
The data used in the following empirical study is from public servants’ survey data, and 313 valid questionnaires were collected randomly. The survey respondents are mainly public servants from municipal government departments in northern Jiangsu, such as Xuzhou, Lianyungang, Suqian, Huaian, and Yancheng. In each city, about 60 servants were selected. Then the effective data collection is sorted out to obtain the preliminary statistical information.

5. Results

5.1. Descriptive Statistics

The questionnaire was mainly released to government department staff through the Questionnaire Star platform, and a total of 387 questionnaires were collected. In order to ensure the quality of the sample, the questionnaire was screened by two indicators. Questionnaires that were taken in less than 40 s were deleted first. Then, questionnaires with the same answers were also regarded as invalid responses. After strict screening, 74 invalid questionnaires were excluded, and the remaining 313 valid questionnaires were found, with an effective rate of 81%. Among the 313 valid questionnaires, the statistical table of basic information for the samples is shown in Table 1. In terms of gender, males accounted for 46.0 percent and females accounted for 54.0 percent. The respondents aged 18–25, 26–30, 31–40, and 41 years old accounted for 28.1 percent, 45.0 percent, and 3.8 percent, respectively. When it comes to education level (including reading), high school and below accounted for 1.3 percent, college and undergraduate accounted for 74.1 percent, and postgraduate accounted for 24.6 percent. 77.3 percent were less than three years old, and 22.7 percent were more than three years old.

5.2. Test of Validity and Reliability

To evaluate the adequacy of the measurement model, confirmatory factor analysis (CFA) was performed using the maximum likelihood approach. Validating a scale involves testing its reliability, convergent validity, and discriminant validity [95].
Construct reliability was commonly evaluated by Cronbach’s alpha values. As summarized in Table 2, six constructs in the research model showed good reliability, with alphas exceeding 0.8, an acceptable threshold recommended by [96].
Convergent validity for the six research constructs was assessed using criteria suggested by [95]. As listed in Table 2, all factor loading values ranged between 0.745 and 0.873 and were significant at p < 0.001. Composite reliability for six constructs greater than 0.8. Therefore, the test of convergent validity was met.
Discriminant validity was assessed using the criteria suggested by [95]: The average variance extracted values (AVE) of six factors were above 0.5. As shown in Table 3, the AVEs were all higher than 0.5. Hence, the test of discriminant validity was met.

5.3. Model Test

The structure model was tested using structural equation modeling (SEM) as performed by AMOS 7. A set of common model-fit measures was used to assess the overall goodness-of-fit of the model. The results of measures summarized in Table 4 χ2/df (χ2 = 205.378, df = 141) was 1.457 and less than 3; NFI, GFI, and CFI were all greater than 0.9; and RMSEA was 0.038 and less than 0.1. The result of model-fit met their respective common acceptance criteria, showing that all constructs have a very good fit.

5.4. Hypothesis Testing

The hypotheses were tested collectively by examining the significance of the relationships in the SEM model. The standardized path coefficients and path significances of the research model are presented in Figure 4.
All the paths, as shown in Figure 4, were significant. Sensing (β = 0.211, ρ = 0.023 < 0.05), seizing (β = 0.227, ρ = 0.012 < 0.05), and reconfiguring (β = 0.160, ρ = 0.019 < 0.05), respectively, had a significant effect on operational adjustment agility. Sensing (β = 0.196, ρ = 0.033 < 0.05), seizing (β = 0.200, ρ = 0.025 < 0.05), and reconfiguring (β = 0.209, ρ = 0.002 < 0.01), respectively, had a significant effect on strategic agility. Operational adjustment agility (β = 0.371, ρ = 0.000 < 0.001) and strategic agility (β = 0.267, ρ = 0.000 < 0.001), respectively, had a significant effect on operational adjustment agility. Thus, all hypotheses in the research model were supported (see Table 5).

6. Discussion and Implications

6.1. Discussion

Drawing upon the dynamic capabilities view, the study described organizational agility as affecting digital transformation performance and dynamic capabilities as antecedents and factors impacting organizational agility. The results suggest that organizational agility significantly influences digital transformation performance, and dynamic capability is an important antecedent and factor of organizational agility. Therefore, it could reasonably be concluded that organizational agility is an important antecedent of digital transformation performance in government agencies.
The results of the study indicate that dynamic capabilities are an important antecedent of organizational agility. The results are consistent with the findings of earlier empirical research in business management [75,79,80] and qualitative research in public administration [35]. This is because the government needs to build dynamic capabilities for digital transformation [12,13]. After the baptism of COVID-19, the government had partly built its dynamic capabilities. The dynamic capabilities could help governments sense and seize opportunities and reconfigure organizational resources for organizational agility [97]. According to the changing environment, the government can timely change its strategy and daily operations. Thus, the government can better respond to the VUCA environment and provide the services required by the public.
The study also found that organizational agility is an important determinant of digital transformation performance in government organizations. The results are consistent with the findings of previous empirical research in business management [11,83] and qualitative research in public administration [15,90,91]. The government senses change and responds quickly, so it can find solutions to cope with it. Thus, organizational agility can improve the efficiency of service delivery [87] and innovation [15] through digital technology. This is very helpful for governments’ working with other organizations and departments in the digital transformation process.

6.2. Implications

(1)
Implications for theory
Firstly, this study has widened and extended the topic of antecedents and factors impacting digital transformation. Although previous studies have investigated some factors having an effect on digital transformation, there is little research on organizational agility. Moreover, the research methods mainly depend on qualitative methods, such as case studies. Thus, the study empirically investigates digital transformation and offers more insight into this phenomenon. This study represents an empirical investigation into the influencing factors of digital transformation on the basis of dynamic capabilities. The result of this study is a parsimonious model that explains how organizational agility influences digital transformation.
Secondly, a detailed exposition of the dynamic capabilities view is undertaken, and its application to digital transformation in government organizations in China is illustrated. This study applies this theory to digital transformation in government. Based on previous studies, this study shows how the dynamic capabilities view could be used to explain organizational agility. The selection of relevant observation indicators refers to existing literature practices, and in the future, the indicators may be screened and improved for different regions and departments to make them more targeted.
(2)
Implications for practice
The findings of this study also have important practical implications for the digital transformation of government agencies. Firstly, an understanding of organizational agility impacting digital transformation will put practitioners in a better position to design suitable strategies and ordinary operations to respond to a VUCA environment through digital technology and, consequently, to provide required services to the public. Secondly, dynamic capabilities emerged as a crucial variable influencing organizational agility in the government context. In order to build micro-foundations of dynamic capabilities, the government must strengthen digital technology deployment, develop employee skills, and change government structures and procedures. These are the practical conclusions for China. For other countries, it may have a different effect, depending on culture, policy, and other factors. A comparative study of the situation in different countries could be carried out in the future.

Author Contributions

Conceptualization, H.Z.; literature review, J.X. and H.D.; methodology, H.Z. and H.D.; software, H.D. and J.X.; validation, H.Z. and H.D.; formal analysis, J.X., H.Z. and H.D.; investigation, H.Z. and H.D.; resources, H.D.; data curation, H.Z., H.D. and J.X.; writing—original draft preparation, H.Z. and H.D.; writing—review and editing, H.Z. and J.X.; visualization, H.D.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation Project (20BJY119), the Fundamental Research Funds for the Central Universities (2022SK07), a key project of th National Social Science Fund (22AZD086), and major projects of the National Society Fund (23AZD117).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Agostino, D.; Arnaboldi, M.; Lema, M.D. COVID-19 as an accelerator of digital transformation in public service delivery. Public Money Manag. 2020, 41, 69–72. [Google Scholar] [CrossRef]
  2. Gabryelczyk, R. Has COVID-19 accelerated digital transformation? Initial lessons learned for public administrations. Inf. Syst. Manag. 2020, 37, 303–309. [Google Scholar] [CrossRef]
  3. Eom, S.J. Digital government transformation in turbulent times: Responses, challenges, and future direction. Gov. Inf. Q. 2022, 39, 101690. [Google Scholar] [CrossRef] [PubMed]
  4. Fletcher, G.; Griffiths, M. Digital transformation during a lockdown. Int. J. Inf. Manag. 2020, 55, 102185. [Google Scholar] [CrossRef]
  5. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2019, 36, 101385. [Google Scholar] [CrossRef]
  6. Omar, A.; Weerakkody, V. Digitally enabled service transformation in UK public sector: A case analysis of universal credit. Int. J. Inf. Manag. 2017, 37, 350–356. [Google Scholar] [CrossRef]
  7. Luna-Reyes, L.F.; Gil-Garcia, J.R. Digital government transformation and internet portals: The co-evolution of technology, organizations, and institutions. Gov. Inf. Q. 2020, 31, 545–555. [Google Scholar] [CrossRef]
  8. Manny, L.; Duygan, M.; Fischer, M.; Rieckermann, J. Barriers to the digital transformation of infrastructure sectors. Policy Sci. 2021, 54, 943–983. [Google Scholar] [CrossRef]
  9. Xiao, J.; Han, L.; Zhang, H. Exploring Driving Factors of Digital Transformation among Local Governments: Foundations for Smart City Construction in China. Sustainability 2022, 14, 14980. [Google Scholar] [CrossRef]
  10. Tangi, L.; Janssen, M.; Benedetti, M.; Noci, G. Digital government transformation: A structural equation modelling analysis of driving and impeding factors. Int. J. Inf. Manag. 2021, 60, 102356. [Google Scholar] [CrossRef]
  11. Troise, C.; Corvello, V.; Ghobadian, A.; O’Regan, N. How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technol. Forecast. Soc. Chang. 2022, 174, 121227. [Google Scholar] [CrossRef]
  12. Mazzucato, M.; Kattel, R. COVID-19 and public-sector capacity. Oxf. Rev. Econ. Policy 2020, 36, 256–269. [Google Scholar] [CrossRef]
  13. Pool, H. The Chief Digital Officer: Building Dynamic Capabilities for Digital Transformation. Available online: https://repository.up.ac.za/handle/2263/80503 (accessed on 21 February 2023).
  14. Chirumalla, K. Building digitally-enabled process innovation in the process industries: A dynamic capabilities approach. Technovation 2021, 105, 102256. [Google Scholar] [CrossRef]
  15. Lee, C.; Lee, J.M.; Liu, Y. Catalysing innovation and digital transformation in combating the Covid-19 pandemic: Whole-of government collaborations in ICT, R&D, and business digitization in Singapore. Public Money Manag. 2021, 43, 340–348. [Google Scholar] [CrossRef]
  16. Ahn, M.J.; Chen, Y.C. Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Gov. Inf. Q. 2022, 39, 101664. [Google Scholar] [CrossRef]
  17. Barrutia, J.M.; Echebarria, C. Effect of the COVID-19 pandemic on public managers’ attitudes toward digital transformation. Technol. Soc. 2021, 67, 101776. [Google Scholar] [CrossRef]
  18. Newman, J.; Mintrom, M.; O’Neill, D. Digital technologies, artificial intelligence, and bureaucratic transformation. Futures 2022, 136, 102886. [Google Scholar] [CrossRef]
  19. Pittaway, J.J.; Montazemi, A.R. Know-how to lead digital transformation: The case of local governments. Gov. Inf. Q. 2020, 37, 101474. [Google Scholar] [CrossRef]
  20. Moore, S. Digital government, public participation and service transformation: The impact of virtual courts. Policy Politics 2019, 47, 495–509. [Google Scholar] [CrossRef]
  21. Curtis, S. Digital transformation—The silver bullet to public service improvement? Public Money Manag. 2019, 39, 322–324. [Google Scholar] [CrossRef]
  22. Simmonds, H.; Gazley, A.; Kaartemo, V.; Renton, M.; Hooper, V. Mechanisms of service ecosystem emergence: Exploring the case of public sector digital transformation. J. Bus. Res. 2021, 137, 100–115. [Google Scholar] [CrossRef]
  23. Gong, Y.; Yang, J.; Shi, X. Towards a comprehensive understanding of digital transformation in government: Analysis of flexibility and enterprise architecture. Gov. Inf. Q. 2020, 37, 101487. [Google Scholar] [CrossRef]
  24. Ali, O.; Soar, J.; Shrestha, A. Perceived potential for value creation from cloud computing: A study of the Australian regional government sector. Behav. Inf. Technol. 2018, 37, 1157–1176. [Google Scholar] [CrossRef]
  25. Androutsopoulou, A.; Karacapilidis, N.; Loukis, E.; Charalabidis, Y. Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 2019, 36, 358–367. [Google Scholar] [CrossRef]
  26. Styrin, E.; Mossberger, K.; Zhulin, A. Government as a platform: Intergovernmental participation for public services in the Russian Federation. Gov. Inf. Q. 2022, 39, 101627. [Google Scholar] [CrossRef]
  27. Fleischer, J.; Carstens, N. Policy labs as arenas for boundary spanning: Inside the digital transformation in Germany. Public Manag. Rev. 2021, 24, 1208–1225. [Google Scholar] [CrossRef]
  28. Bogumil-Uçan, S.; Klenk, T. Varieties of health care digitalization: Comparing advocacy coalitions in Austria and Germany. Rev. Policy Res. 2021, 38, 478–503. [Google Scholar] [CrossRef]
  29. Janssen, M.; Hartog, M.; Matheus, R.; Ding, A.Y.; Kuk, G. Will algorithms blind people? The effect of explainable AI and decision-makers’ experience on AI-supported decision-making in government. Soc. Sci. Comput. Rev. 2022, 4, 478–493. [Google Scholar] [CrossRef]
  30. Valle-Cruz, D.; Criado, J.I.; Sandoval-Almazán, R.; Ruvalcaba-Gomez, E.A. Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation. Gov. Inf. Q. 2020, 37, 101509. [Google Scholar] [CrossRef]
  31. Vogl, T.M.; Seidelin, C.; Ganesh, B.; Bright, J. Smart technology and the emergence of algorithmic bureaucracy: Artificial intelligence in UK local authorities. Public Adm. Rev. 2020, 80, 946–961. [Google Scholar] [CrossRef]
  32. Giest, S. Big data for policymaking: Fad or fasttrack? Policy Sci. 2017, 50, 367–382. [Google Scholar] [CrossRef] [Green Version]
  33. Kempeneer, S. A big data state of mind: Epistemological challenges to accountability and transparency in data-driven regulation. Gov. Inf. Q. 2021, 38, 101578. [Google Scholar] [CrossRef]
  34. McNeely, C.L.; Hahm, J.O. The big (data) bang: Policy, prospects, and challenges. Rev. Policy Res. 2014, 31, 304–310. [Google Scholar] [CrossRef]
  35. Chatfield, A.T.; Reddick, C.G. A framework for Internet of Things-enabled smart government: A case of IoT cybersecurity policies and use cases in US federal government. Gov. Inf. Q. 2019, 36, 346–357. [Google Scholar] [CrossRef]
  36. Wirtz, B.W.; Weyerer, J.C.; Schichtel, F.T. An integrative public IoT framework for smart government. Gov. Inf. Q. 2019, 36, 333–345. [Google Scholar] [CrossRef]
  37. Nanos, I.; Papaioannou, E.; Androutsou, E.; Manthou, V. The role of cloud computing and citizens relationship management in digital government transformation. Int. J. Internet Mark. Advert. 2019, 13, 120–136. [Google Scholar] [CrossRef]
  38. Rainero, C.; Modarelli, G. Blockchain informative infrastructure: A conceptual reflection on public administrative procedures and a citizen-centred view. Inf. Technol. People 2021, 34, 1252–1284. [Google Scholar] [CrossRef]
  39. Yfantis, V.; Leligou, H.C.; Ntalianis, K. Blockchain—A revolutionary tool for the public sector. Public Money Manag. 2020, 41, 408–411. [Google Scholar] [CrossRef]
  40. Mukhopadhyay, S.; Bouwman, H.; Jaiswal, M.P. An open platform centric approach for scalable government service delivery to the poor: The Aadhaar case. Gov. Inf. Q. 2019, 36, 437–448. [Google Scholar] [CrossRef]
  41. De Sousa, W.G.; Fidelis, R.A.; de Souza Bermejo, P.H.; da Silva Gonçalo, A.G.; de Souza Melo, B. Artificial intelligence and speedy trial in the judiciary: Myth, reality or need? A case study in the Brazilian Supreme Court (STF). Gov. Inf. Q. 2022, 39, 101660. [Google Scholar] [CrossRef]
  42. Ebbers, W.E.; van de Wijngaert, L.A. Paper beats ping: On the effect of an increasing separation of notification and content due to digitization of government communication. Gov. Inf. Q. 2020, 37, 101396. [Google Scholar] [CrossRef]
  43. Van den Berg, A.C.; Giest, S.N.; Groeneveld, S.M.; Kraaij, W. Inclusivity in online platforms: Recruitment strategies for improving participation of diverse sociodemographic groups. Public Adm. Rev. 2020, 80, 989–1000. [Google Scholar] [CrossRef]
  44. Mahmood, M. Enhancing citizens’ trust and confidence in government through digital transformation. Int. J. Electron. Gov. Res. 2016, 12, 99–110. [Google Scholar] [CrossRef] [Green Version]
  45. Jarmin, R.S.; O’Hara, A.B. Big data and the transformation of public policy analysis. J. Policy Anal. Manag. 2016, 35, 715–721. [Google Scholar] [CrossRef]
  46. Schintler, L.A.; Kulkarni, R. Big data for policy analysis: The good, the bad, and the ugly. Rev. Policy Res. 2014, 31, 343–348. [Google Scholar] [CrossRef]
  47. Stough, R.; McBride, D. Big data and US public policy. Rev. Policy Res. 2014, 31, 339–342. [Google Scholar] [CrossRef]
  48. Höchtl, J.; Parycek, P.; Schöllhammer, R. Big data in the policy cycle: Policy decision making in the digital era. J. Organ. Comput. Electron. Commer. 2016, 26, 147–169. [Google Scholar] [CrossRef]
  49. Xiao, J.; Zhang, H.; Han, L. How digital transformation improve government peformane: The mediating role of partnering agility. IEEE Access 2023, 11, 59274–59285. [Google Scholar] [CrossRef]
  50. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic capabilities and strategic management. Strateg. Manag. J. 1997, 18, 509–533. [Google Scholar] [CrossRef]
  51. Teece, D.J. Explicating dynamic capabilities: The nature and micro-foundations of (sustainable) enterprise performance. Strateg. Manag. J. 2007, 28, 1319–1350. [Google Scholar] [CrossRef] [Green Version]
  52. Bryson, J.M.; Ackermann, F.; Eden, C. Putting the resource-based view of strategy and distinctive competencies to work in public organizations. Public Adm. Rev. 2007, 28, 702–717. [Google Scholar] [CrossRef]
  53. Piening, E.P. Dynamic capabilities in public organizations: A literature review and research agenda. Public Manag. Rev. 2011, 44, 687–708. [Google Scholar] [CrossRef]
  54. Hansen, J.R.; Ferlie, E. Applying strategic management theories in public sector organizations: Developing a typology. Public Manag. Rev. 2014, 18, 1–19. [Google Scholar] [CrossRef]
  55. Chong, M.; Habib, A.; Evangelopoulos, N.; Park, H.W. Dynamic capabilities of a smart city: An innovative approach to discovering urban problems and solutions. Gov. Inf. Q. 2018, 35, 682–692. [Google Scholar] [CrossRef]
  56. Linde, L.; Sjödin, D.; Parida, V.; Wincent, J. Dynamic capabilities for ecosystem orchestration A capability-based framework for smart city innovation initiatives. Technol. Forecast. Soc. Chang. 2021, 166, 120614. [Google Scholar] [CrossRef]
  57. Mahama, H.; Elbashir, M.; Sutton, S.; Arnold, V. Enabling enterprise risk management maturity in public sector organizations. Public Money Manag. 2020, 42, 403–407. [Google Scholar] [CrossRef]
  58. Kattel, R.; Mazzucato, M. Mission-oriented innovation policy and dynamic capabilities in the public sector. Ind. Corp. Chang. 2018, 27, 787–801. [Google Scholar] [CrossRef]
  59. Ridder, H.G.; Bruns, H.J.; Spier, F. Managing implementation processes: The role of public managers in the implementation of accrual accounting–evidence from six case studies in Germany. Public Manag. Rev. 2007, 8, 87–118. [Google Scholar] [CrossRef]
  60. Tuan, L.T. Reform in public organizations: The roles of ambidextrous leadership and moderating mechanisms. Public Manag. Rev. 2016, 19, 518–541. [Google Scholar] [CrossRef]
  61. Trischler, J.; Westman, T.J. Design for experience–a public service design approach in the age of digitalization. Public Manag. Rev. 2021, 24, 1251–1270. [Google Scholar] [CrossRef]
  62. Klievink, B.; Janssen, M. Realizing joined-up government—Dynamic capabilities and stage models for transformation. Gov. Inf. Q. 2009, 26, 275–284. [Google Scholar] [CrossRef]
  63. Helfat, C.E.; Raubitschek, R.S. Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Res. Policy 2018, 47, 1391–1399. [Google Scholar] [CrossRef]
  64. Porwol, L.; Ojo, A.; Breslin, J.G. Social software infrastructure for e-participation. Gov. Inf. Q. 2018, 35, S88–S98. [Google Scholar] [CrossRef]
  65. Boin, A.; Ansell, C.; Lodge, M.; Parrado, S.; Wegrich, K.; Xue, L. A time for public administration. Public Adm. 2011, 89, 221–225. [Google Scholar] [CrossRef]
  66. Sarkar, S. Breaking the chain: Governmental frugal innovation in Kerala to combat the COVID-19 pandemic. Gov. Inf. Q. 2021, 38, 101549. [Google Scholar] [CrossRef]
  67. Madan, R.; Ashok, M. AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Gov. Inf. Q. 2023, 40, 101774. [Google Scholar] [CrossRef]
  68. Trivellato, B.; Martini, M.; Cavenago, D. How do organizational capabilities sustain continuous innovation in a public setting? Am. Rev. Public Adm. 2023, 51, 57–71. [Google Scholar] [CrossRef]
  69. Panagiotopoulos, P.; Klievink, B.; Cordella, A. Public value creation in digital government. Gov. Inf. Q. 2019, 36, 101421. [Google Scholar] [CrossRef]
  70. Mergel, I.; Kattel, R.; Lember, V.; McBride, K. Citizen-oriented digital transformation in the public sector. In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, Delft, The Netherlands, 30 May–1 June 2018; pp. 1–3. [Google Scholar] [CrossRef] [Green Version]
  71. Lu, Y.; Ramamurthy, K. Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Q. 2011, 35, 931–954. [Google Scholar] [CrossRef] [Green Version]
  72. Tallon, P.P.; Queiroz, M.; Coltman, T.; Sharma, R. Information technology and the search for organizational agility: A systematic review with future research possibilities. J. Strateg. Inf. Syst. 2019, 28, 218–237. [Google Scholar] [CrossRef]
  73. Walter, A.T. Organizational agility: Ill-defined and somewhat confusing? A systematic literature review and conceptualization. Manag. Rev. Q. 2020, 71, 343–391. [Google Scholar] [CrossRef] [Green Version]
  74. Zhang, D.Z. Towards theory building in agile manufacturing strategies—Case studies of an agility taxonomy. Int. J. Prod. Econ. 2020, 131, 303–312. [Google Scholar] [CrossRef]
  75. Ilmudeen, A. Leveraging IT-enabled dynamic capabilities to shape business process agility and firm innovative capability: Moderating role of turbulent environment. Rev. Manag. Sci. 2021, 16, 2341–2379. [Google Scholar] [CrossRef]
  76. Karimi-Alaghehband, F.; Rivard, S. Information technology outsourcing and architecture dynamic capabilities as enablers of organizational agility. J. Inf. Technol. 2019, 34, 129–159. [Google Scholar] [CrossRef]
  77. Teece, D.; Peteraf, M.; Leih, S. Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. Calif. Manag. Rev. 2016, 58, 13–35. [Google Scholar] [CrossRef] [Green Version]
  78. Chatfield, A.T.; Reddick, C.G. Customer agility and responsiveness through big data analytics for public value creation: A case study of Houston 311 on-demand services. Gov. Inf. Q. 2018, 35, 336–347. [Google Scholar] [CrossRef]
  79. Hussain, M.; Malik, M. How do dynamic capabilities enable hotels to be agile and resilient? A mediation and moderation analysis. Calif. Manag. Rev. 2022, 106, 103266. [Google Scholar] [CrossRef]
  80. Mikalef, P.; Pateli, A. Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA. J. Bus. Res. 2017, 70, 1–16. [Google Scholar] [CrossRef]
  81. Lee OK, D.; Xu, P.; Kuilboer, J.P.; Ashrafi, N. Idiosyncratic values of IT-enabled agility at the operation and strategic levels. Commun. Assoc. Inf. Syst. 2016, 39, 13. [Google Scholar] [CrossRef] [Green Version]
  82. Haider, S.A.; Tehseen, S.; Khan, S.; Mata, M.N.; Martins, J.M.; Abreu, A. A literature review on agility-is there a need to develop a new instrument? Int. J. Entrep. 2021, 25, 1–14. [Google Scholar]
  83. Li, L.; Tong, Y.; Wei, L.; Yang, S. Digital technology-enabled dynamic capabilities and their impacts on firm performance: Evidence from the COVID-19 pandemic. Inf. Manag. 2022, 59, 103689. [Google Scholar] [CrossRef]
  84. Warner, K.S.; Wäger, M. Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Plan. 2019, 52, 326–349. [Google Scholar] [CrossRef]
  85. Zahoor, N.; Golgeci, I.; Haapanen, L.; Ali, I.; Arslan, A. The role of dynamic capabilities and strategic agility of B2B high-tech small and medium-sized enterprises during COVID-19 pandemic: Exploratory case studies from Finland. Ind. Mark. Manag. 2022, 105, 502–514. [Google Scholar] [CrossRef]
  86. Chakravarty, A.; Grewal, R.; Sambamurthy, V. Information technology competencies, organizational agility, and firm performance: Enabling and facilitating roles. Inf. Syst. Res. 2013, 24, 976–997. [Google Scholar] [CrossRef]
  87. Mergel, I.; Ganapati, S.; Whitford, A.B. Agile: A new way of governing. Public Adm. Rev. 2020, 81, 161–165. [Google Scholar] [CrossRef]
  88. Soe, R.M.; Drechsler, W. Agile local governments: Experimentation before implementation. Gov. Inf. Q. 2018, 35, 323–335. [Google Scholar] [CrossRef]
  89. Eggers, W.D.; Bellman, J. The Journey to Government’s Digital Transformation. Available online: https://www2.deloitte.com/uk/en/pages/public-sector/articles/the-journey-to-governments-digital-transformation.html (accessed on 21 February 2023).
  90. AlNuaimi, B.K.; Singh, S.K.; Ren, S.; Budhwar, P.; Vorobyev, D. Mastering digital transformation: The nexus between leadership, agility, and digital strategy. J. Bus. Res. 2022, 145, 636–648. [Google Scholar] [CrossRef]
  91. Faro, B.; Abedin, B.; Cetindamar, D. Hybrid organizational forms in public sector’s digital transformation: A technology enactment approach. J. Enterp. Inf. Manag. 2021, 35, 1742–1763. [Google Scholar] [CrossRef]
  92. Wilden, R.; Gudergan, S.P.; Nielsen, B.B.; Lan, L. Dynamic capabilities and performance:strategy, structure and environment. Long Range Plan. 2013, 46, 72–96. [Google Scholar] [CrossRef] [Green Version]
  93. Miroshnychenko, I.; Strobl, A.; Matzler, K.; De Massis, A. Absorptive capacity, strategic flexibility, and business model innovation: Empirical evidence from Italian SMEs. J. Bus. Res. 2021, 130, 670–682. [Google Scholar] [CrossRef]
  94. Shen, L.; Zhang, X.; Liu, H. Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: Moderating role of digital innovation orientation. Manag. Decis. Econ. 2021, 43, 2038–2054. [Google Scholar] [CrossRef]
  95. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  96. Nunnally, J.C. Psychometric Theory; McGraw-Hill: New York, NY, USA, 1978. [Google Scholar]
  97. Chen, J.; Wang, D.; Chen, J. Understanding organizational agility development for a government: A process model of resource configuration. Front. Bus. Res. China 2013, 8, 73–97. [Google Scholar] [CrossRef]
Sustainability 15 11304 g001 550
Figure 1. Diagram of analysis framework.
Figure 1. Diagram of analysis framework.
Sustainability 15 11304 g001
Sustainability 15 11304 g002 550
Figure 2. The research model.
Figure 2. The research model.
Sustainability 15 11304 g002
Sustainability 15 11304 g003 550
Figure 3. Location of Jiangsu Province in China.
Figure 3. Location of Jiangsu Province in China.
Sustainability 15 11304 g003
Sustainability 15 11304 g004 550
Figure 4. Structural equation model diagram of the research model. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 4. Structural equation model diagram of the research model. * p < 0.05; ** p < 0.01; *** p < 0.001.
Sustainability 15 11304 g004
Table
Table 1. Descriptive statistical analysis of samples.
Table 1. Descriptive statistical analysis of samples.
Demographic VariableClassification ItemFrequencyPercentage (%)Cumulative Percentage (%)
GenderMale14446.0146.01
Female16953.99100
Age18–25 years8828.1228.12
26–30 years14145.0573.17
31–40 years7223.0096.17
Over 41 years old123.83100
EducationHigh school and below41.281.28
College and undergraduate23274.1275.40
Postgraduate7724.60100
Years of serviceLess than three years24277.3277.32
Three years and above7122.68100
Table
Table 2. Combined reliability and convergent validity of latent variables.
Table 2. Combined reliability and convergent validity of latent variables.
VariablesLoadingCRAVE
SNC 10.794 ***0.8280.617
SNC 20.797 ***
SNC 30.765 ***
SIC 10.851 ***0.8430.642
SIC 20.782 ***
SIC 30.769 ***
RC 10.757 ***0.8070.582
RC 20.745 ***
RC 30.787 ***
OAA 10.845 ***0.8920.734
OAA 20.873 ***
OAA 30.852 ***
SA 10.852 ***0.9120.721
SA 20.866 ***
SA 30.841 ***
SA 40.836 ***
DTP 10.815 ***0.8390.635
DTP 20.788 ***
DTP 30.788 ***
*** p < 0.001.
Table
Table 3. AVE and correlation of latent variables.
Table 3. AVE and correlation of latent variables.
ConstructAVEFactor Correlation
SNCSICRCOAASADTP
SNC0.6170.785
SIC0.6420.6710.801
RC0.5820.3790.3420.763
OAA0.7210.3950.3910.3330.849
SA0.7340.4070.4120.2900.4810.857
DTP0.6350.3150.2600.4590.4170.4740.797
Table
Table 4. Overall model-fit indices for the research model.
Table 4. Overall model-fit indices for the research model.
Model-Fit Indicesχ2/dfNFIGFICFIRMSEA
Recommended value<3>0.9>0.9>0.9<0.1
Results1.4570.9390.9370.980.038
Table
Table 5. Research hypothesis testing results.
Table 5. Research hypothesis testing results.
Research HypothesisT-ValuepβR2
SNC—OAA2.513*0.2110.236
SIC—OAA2.352*0.227
RC—OAA2.244*0.160
SNC—SA2.268*0.1960.235
SIC—SA3.090*0.200
RC—SA2.136**0.209
OAA—DTP5.821***0.3710.255
SA—DTP4.340***0.267
*** p < 0.001; ** p < 0.01; * p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, H.; Ding, H.; Xiao, J. How Organizational Agility Promotes Digital Transformation: An Empirical Study. Sustainability 2023, 15, 11304. https://doi.org/10.3390/su151411304

AMA Style

Zhang H, Ding H, Xiao J. How Organizational Agility Promotes Digital Transformation: An Empirical Study. Sustainability. 2023; 15(14):11304. https://doi.org/10.3390/su151411304

Chicago/Turabian Style

Zhang, Hui, Huiying Ding, and Jianying Xiao. 2023. "How Organizational Agility Promotes Digital Transformation: An Empirical Study" Sustainability 15, no. 14: 11304. https://doi.org/10.3390/su151411304

APA Style

Zhang, H., Ding, H., & Xiao, J. (2023). How Organizational Agility Promotes Digital Transformation: An Empirical Study. Sustainability, 15(14), 11304. https://doi.org/10.3390/su151411304

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop