Next Article in Journal
Upcycling of Sunflower and Sesame Press Cakes as Functional Ingredients in Cookies
Previous Article in Journal
Harnessing Artificial Intelligence and Human Resource Management for Circular Economy and Sustainability: A Conceptual Integration
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 15
10.3390/su17157055
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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Antecedents and Consequences of Strategic Renewal in Digital Transformation in the Context of Sustainability: An Empirical Analysis

by
Jianying Xiao
,
Yitong Lu
and
Hui Zhang
*
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 7055; https://doi.org/10.3390/su17157055 (registering DOI)
Submission received: 14 June 2025 / Revised: 22 July 2025 / Accepted: 31 July 2025 / Published: 4 August 2025
(This article belongs to the Section Sustainable Management)
Browse Figures
Versions Notes

Abstract

Sustainability has emerged as a critical issue in development. Digital transformation functions as both an enabler and an effective tool for promoting sustainability. Strategy plays a pivotal role in the process of digital transformation. However, there is a paucity of existing research focused on strategic renewal in digital transformation within the context of China. This study employs organizational learning theory to examine the antecedents and consequences of strategic renewal in digital transformation. Data were collected from 389 government employees through a questionnaire survey and a quantitative analysis was performed to evaluate four hypotheses using structural equation modeling (SEM). The results indicate that knowledge acquisition and organizational memory significantly influence strategic renewal, which in turn affects government performance. The findings of this study could serve as a guide and provide concrete practical approaches for successful digital transformation among governments, thereby laying a foundation for sustainable development.

1. Introduction

Sustainability has emerged as a critical issue in development, prompting the United Nations to establish Sustainable Development Goals [1]. Digital technologies play a pivotal role in achieving these goals [2,3,4]. Furthermore, the increasing application of digital technology within government has led to substantial changes in governmental organizations [5,6,7,8], resulting in the emergence of digital transformation. This transformation serves as both an enabler and an effective tool for sustainable development [9,10,11,12]. Digital transformation can revolutionize organizational models [13], enhance process efficiency [14], and deliver sustainable products and services [15] to achieve environmental, social, and economic sustainability [16,17]. Consequently, digital transformation in government transitioned from being an option to a strategic necessity [18,19,20]. Despite the global efforts undertaken by governments to implement digital transformation initiatives, it remains in its infancy [21] and lags behind the private sector [22].
Strategy is a critical factor in achieving the success of digital transformation [23] and serves as an important indicator for evaluating digital maturity [24]. The disruptive nature of the digital age, coupled with the dynamic process of strategy, necessitates ongoing strategic adjustments [20,22]. Existing literature on digital transformation in government primarily focuses on the antecedents and consequences of this transformation. Strategic renewal enables organizations to better navigate internal and external pressures [25,26], making it essential for organizational survival and success [27,28]. Strategic renewal is defined as “the process, content, and outcome of refreshment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects” [27]. However, there is a significant gap in research that examines the strategic renewal of digital transformation in government.
To address this gap, the primary research questions of this study are as follows: (1) What factors influence the strategic renewal of digital transformation in government? (2) How does strategic renewal impact government performance? Organizational learning plays a significant role in achieving strategic renewal within organizations [29,30]. To explore these questions, this study employs organizational learning theory to propose a model that illustrates the effects of organizational learning on strategic renewal and the subsequent effects of strategic renewal on government performance.
This study contributes to the understanding of strategic renewal and digital transformation in three ways. First, it enhances the literature on digital transformation in government by introducing strategic renewal as a novel perspective. Second, it further expands research on strategic renewal, being one of the few studies that link strategic renewal with digital transformation in government. Third, it provides empirical evidence demonstrating how strategic renewal can enhance government performance in the context of sustainable development.

2. Literature Review

The definition of digital transformation remains unstandardized among scholars and practitioners [2,20]. The commonly referenced definition in the literature is derived from [4,31]. While variations exist among these definitions, they consistently capture the core essence of digital transformation: the integration of digital technologies into internal government processes and stakeholder interactions to foster governmental digital innovation [4,31]. Consequently, it encompasses technical, socio-technical, and socio-political solutions [32]. Digital transformation is characterized by three significant traits: an evolutionary process, a holistic approach, and radical innovation [32,33,34].
The antecedents of digital transformation can be categorized into four aspects: technology, organization, people, and environment. (1) Digital technology serves as a primary driver of digital transformation [23]. These technologies encompass artificial intelligence, cloud computing, and social media, among others [21,35]. (2) Organizational factors predominantly focus on cultural change [19], structural modifications [36], organizational context [18], and institutional arrangements [37]. (3) People-related factors encompass leadership [18,19], government employees [38], and stakeholder participation [39]. (4) Environmental factors primarily consist of external pressures [40].
Digital transformation profoundly alters the structure, processes, culture, and personnel of government [4,41]. Consequently, governments must reevaluate and redesign their information systems, management activities, and foster collaboration with the public [3,4,18,31]. This shift results in a paradigm change [31,42] and enhances public value creation [43,44] within the government. Digital transformation contributes to improved service efficiency [3,45], increased transparency [1,32,33], and evidence-based policymaking [37]. Furthermore, it reshapes individuals’ work and lives [31,33], raising expectations for digital services [31,33], and enhancing citizen engagement [23].
Based on the aforementioned discussion, two observations have emerged. First, existing literature on digital transformation in government primarily focuses on the antecedents and consequences of this transformation. However, there is a notable gap in research that examines the strategic renewal of digital transformation in government. Second, there are few studies that have addressed the antecedents and consequences of strategy renewal in digital transformation in government.

3. Conceptual Model and Hypotheses

According to organizational learning theory, the conceptual model is depicted in Figure 1. This research model delineates the effect of organizational learning on strategic renewal and the subsequent influence of strategic renewal on government performance.

3.1. Organizational Learning

Organizational learning has become a pivotal topic of focus for both researchers and practitioners across various fields [46]. Researchers have approached its definition from multiple perspectives [47]. Studies have explored organizational learning through the lenses of process [48,49], change [50], resource [51,52], capability [53], and social relations [47]. Nonetheless, the process perspective remains the most prevalent. In this article, organizational learning is defined as “the process of improving actions through better knowledge and understanding” [48]. Scholars have delineated the sub-processes of organizational learning from various perspectives. For instance, Crossan et al. propose that it encompasses intuiting, interpreting, integrating, and institutionalizing [54]. Huber contends that it includes knowledge acquisition, information distribution, information interpretation, and organizational memory [55].
Organizational learning is a crucial mechanism for organizations to adapt and respond to environmental changes [56,57]. Consequently, it is vital for the survival and growth of public organizations [57,58]. Furthermore, organizational learning plays a significant role in achieving strategic renewal within organizations [29,30]. Two studies have identified the impact of organizational and inter-organizational learning on strategic renewal through case studies [59,60]. Additionally, prior research has empirically demonstrated that organizational learning positively influences strategic renewal [25]. In the context of digital transformation, organizational learning serves as a vital resource that significantly contributes to the adjustment and flexibility of digital transformation strategies [61,62,63]. Hence, the first research hypothesis is presented as follows:
H1. 
Organizational learning has a positive influence on strategic renewal in digital transformation.

3.1.1. Knowledge Acquisition (KA)

Knowledge acquisition is the process through which organizations obtain new and usable information and knowledge [64]. It serves as a catalyst for organizational learning [50]. Knowledge can be sourced both internally and externally [65]. Furthermore, knowledge acquisition facilitates the harnessing of digital technology, thereby promoting organizational digital transformation [66]. Additionally, previous studies have indicated that knowledge acquisition can significantly enhance strategic renewal [60,67]. Strategic renewal encompasses exploitative renewal, and exploratory renewal [68,69]. Consequently, the following research hypotheses are proposed:
H1a. 
Knowledge acquisition positively influences developmental renewal.
H1b. 
Knowledge acquisition positively influences exploratory renewal.

3.1.2. Knowledge Distribution (KD)

Knowledge distribution is the second sub-process of organizational learning [55] and refers to the process of sharing information among employees in the organization [64]. This process facilitates the internalization of new knowledge [70] and can occur through both formal and informal interactions among staff members [65]. Effective knowledge distribution serves as a critical foundation for successful digital transformation in government organizations [45]. Furthermore, prior studies have demonstrated that knowledge distribution can significantly enhance strategic renewal [60]. Drawing on the insights from these studies, the following research hypotheses are formulated:
H1c. 
Knowledge dissemination positively affects developmental renewal.
H1d. 
Knowledge dissemination positively affects exploratory renewal.

3.1.3. Shared Interpretation (SI)

Shared interpretation is defined as the agreement among organizational members regarding the meaning of information [71]. Its objective is to analyze information from a broad perspective [65] and it serves as an intermediary between individual and group levels [29,72]. Shared interpretation emphasizes the generation of new ideas or insights [73] and can clarify strategic purpose [74]. Consequently, new interpretations guide the renewal processes in the organization [60]. Prior studies have demonstrated that shared interpretation can significantly enhance strategic flexibility [65]. Therefore, the following research hypotheses are proposed:
H1e. 
Shared interpretation positively influences exploitative renewal.
H1f. 
Shared interpretation positively influences exploratory renewal.

3.1.4. Organizational Memory (OM)

Organizational memory, as the fourth sub-process of organizational learning [55], refers to “the process of storing information and knowledge for future use” [64]. It can be categorized into two types: declarative and procedural memory [75]. Furthermore, organizational memory operates at three levels: individual, non-centralized collective, and centralized [76]. Changes in organizational memory are reflected in organizational strategy [77]. Previous studies have demonstrated that organizational memory has a significant impact on strategic renewal [29,78]. Based on the insights from these studies, the following research hypotheses are formulated:
H1g. 
Organizational memory positively influences developmental renewal.
H1h. 
Organizational memory positively influences exploratory updates.

3.2. Strategic Renewal

Strategic renewal has surfaced as a significant research topic in the field of organization and management [79,80]. However, scholars have yet to arrive at a uniform definition of strategic renewal [25,78,81]. The definition most widely recognized in existing literature is provided by [79] and [27]. Strategic renewal refers to “the process, content, and outcome of refreshment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects” [27]. Strategic renewal possesses two fundamental characteristics [79,82,83]. First, it entails change, which refers to the “refresh and replacement of strategic attributes” [27]. These changes are reflected not only in organizational resources and competences but also in path dependence [79,84]. Second, it is an evolutionary process [85], representing the organization’s progression through various stages of change as influenced by shifts in both the internal and external environment [83,86].
Strategic renewal enables organizations to better navigate internal and external pressures [26,81]. Consequently, it is essential for the organization’s survival and success [27,28,87]. The outcomes of the strategic renewal constitute a significant component of existing research [81,82]. Based on the nature of the strategic renewal, these actions can be categorized into either exploratory or exploitative actions [68,69,82]. Exploratory strategic renewal actions involve long-term activities that introduce new initiatives within the organization [68,69], such as creating new digital transformation initiatives, and developing novel e-services. In contrast, exploitative renewal actions are short-term activities that focus on the current range of operations [68,69], including expanding departments undergoing digital transformation and enhancing government capacity. Previous empirical studies have demonstrated that strategic updates can significantly enhance organizational performance [25,87]. Therefore, the following research hypotheses are presented:
H2. 
Strategic update positively influences government performance.
H2a. 
Exploitative strategic renewal actions positively influence government performance.
H2b. 
Exploratory strategic renewal actions positively influence government performance.

4. Methodology

4.1. Measurement of Variables

To enhance the accumulation of knowledge, constructs from previous research were operationalized. This research investigates antecedents and consequences of strategic renewal of digital transformation, necessitating several adjustments to the existing measurement scale. A five-point Likert scale (1 = strongly disagree, 5 = strongly agree) was employed for all items.
Knowledge acquisition, knowledge distribution, shared interpretation, and organizational memory were assessed using measurement scales derived from [65]. Exploitative and exploratory strategic renewal actions were evaluated through measurement scales adapted from [25]. All scales were rephrased to specifically address the context of government.

4.2. Data Collection

Data for this study was gathered from a sample using a questionnaire survey. The questionnaire was developed based on existing relevant literature and includes demographic characteristics along with measurement scales for the research variables under investigation. Additionally, the questionnaire was translated into Chinese to ensure accessibility for respondents.
Questionnaires were distributed to civil servants involved in digital technology and digital work across various government agencies. From this population, a random sample of 400 individuals was selected for participation. A total of 350 questionnaires were returned; however, 37 were deemed invalid due to incompleteness. Consequently, the number of valid questionnaires amounted to 313, resulting in a response rate of 89.43%. Demographic information of the respondents is presented in Table 1.
According to Table 1, the distribution of gender among respondents was 59.8% male and 40.2% female. Over 50% of the respondents were aged between 25 and 34 years, while 30. 2% were below 25 years. The educational qualifications of the respondents are detailed as follows: 0.3% had completed junior college, 5.7% held a college degree, 55.3% possessed a university degree, and 38.7% obtained a postgraduate degree or higher. Regarding work experience, 48.4% of respondents had less than 3 years of experience, 33.6% had 3 to 5 years, 11.4% had 6 to 10 years, and 6.6% had more than 10 years of experience.

5. Data Analysis and Results

5.1. Scale Validation

Validating a measurement scale requires an examination of its reliability, along with its convergent and discriminant validity [88]. Construct reliability and convergent validity are commonly evaluated through Cronbach’s alpha coefficients and factor loadings. As indicated in Table 2, the five constructs exhibited strong reliability with alpha values exceeding 0.7. Additionally, all factor loading values fell between 0.626 and 0.931, achieving statistical significance at p = 0.001. Consequently, the criteria for both construct reliability and convergent validity were satisfied.
The validity test primarily focuses on evaluating the discriminative validity among variables. To assess discriminant validity, the following proposed criteria were employed: the square root of the average variance extracted (AVE) must be greater than the correlations between a given construct and the other constructs within the model. As illustrated in Table 3, the AVEs for all constructs exceeded their corresponding cross-correlations, thereby confirming that the criteria for discriminant validity have been satisfied.

5.2. Model Testing Results

The structural model was assessed through SEM conducted in AMOS 27. The significance of each hypothesis in the SEM model and the variance explained by each variable (R2) were calculated.
All paths illustrated in Figure 2 exhibit statistical significance. The effects of knowledge acquisition (β = 0.402, p < 0.001; β = 0.291, p < 0.001) and organizational memory (β = 0.407, p < 0.001; β = 0.448, p < 0.001) on both exploitative renewal and exploratory renewal are significant. In contrast, knowledge distribution and shared interpretation have no effect on exploitative renewal and exploratory renewal. Furthermore, exploitative renewal (β = 0.411, p < 0.001) and exploratory renewal (β = 0.326, p < 0.001) have a significant influence on government performance.

5.3. Model-Fit Indices

A collection of model-fit measures was employed to evaluate the overall goodness-of-fit of the model. As presented in Table 4, χ2/df (χ2 = 316.143, df = 214) was 1.477 and less than 3.0. Furthermore, NFI, GFI, and CFI all surpassed 0.9, whereas RMSEA was 0.039 and less than 0.1. These results suggest that the model-fit exceeded commonly accepted standards, indicating that all indices reflect an excellent fit.
The hypotheses were collectively tested by assessing the significance of the relationships in the SEM. Table 5 summarizes the results of the hypothesis testing, indicating that all proposed causal paths in the research model were statistically significant.

6. Discussion

Sustainability has emerged as a critical issue in development. Digital transformation serves as both an enabler and an effective tool for promoting sustainability. Strategy plays a pivotal role in the process of digital transformation. However, there is a paucity of existing research focused on strategic renewal in digital transformation. This study utilizes organizational learning theory to examine the antecedents and consequences of strategic renewal in digital transformation, utilizing SEM to test the proposed research model. The findings indicate that two dimensions of organizational learning significantly affect strategic renewal, which in turn impacts government performance.
The findings of this study suggest that knowledge acquisition significantly influences strategic renewal. Information and knowledge are fundamental resources for digital transformation in organizations [89,90]. Consequently, the greater the knowledge an organization possesses regarding digital technologies, the more favorable its attitude towards digital transformation [91]. Acquiring new knowledge enables organizations to swiftly master and apply digital technologies [92], thereby facilitating organizational change. Furthermore, the internalization of new knowledge in the organization contributes to its renewal [60]. Additionally, an organization’s knowledge informs its digital transformation strategy [87]. Organizations with high efficiency in knowledge acquisition can obtain more new knowledge and engage in the subsequent phase of strategic renewal [67].
The study demonstrates that organizational memory significantly affects strategic renewal. Organizational memory serves as a foundation for change and influences the information that organizations seek and analyze [71]. Therefore, organizations may choose to acquire different information and interpret the same information in varied ways, leading to diverse changes within the organization. Additionally, organizational memory encompasses “routines and schema for thought and action” [58], guiding members’ exploration and exploratory actions. Moreover, it is a component of organizational design [93] and plays a pivotal role in all forms of organizational innovation. Thus, organizational memory can significantly contribute to strategic renewal.
The study found that knowledge distribution and shared interpretation have no impact on strategic renewal. This finding can be attributed to two primary reasons: First, the lack of time. The heavy workload within government departments has diminished communication among civil servants, resulting in inadequate knowledge distribution and subsequent challenges in developing a shared interpretation [94,95]. Second, the hierarchical nature of government agencies impedes knowledge transfer, particularly in cross-level and cross-functional contexts [96]. These factors contribute to the difficulty in forming a shared interpretation. Therefore, knowledge distribution and shared interpretation do not influence strategic renewal.
The study further identified strategic renewal as a significant predictor of government performance. The introduction of digital technologies and new knowledge transforms organizational information systems and path dependence [84], thereby enabling the creation of innovative products and services. Furthermore, government organizations must continuously upgrade their core competencies to effectively leverage information technology [79,82]. These improvements will enhance government performance and increase public satisfaction with public services.

7. Implications

7.1. Implications for Theory

First, this study offers a meaningful contribution to the existing body of literature surrounding digital transformation. The role of strategy is vital in facilitating this transformation. However, there are relatively few studies focused on strategic renewal in government digital transformation. Therefore, drawing on organizational learning theory, this study conducts a quantitative investigation into the antecedents and consequences of strategic renewal in digital transformation. The results provide a parsimonious model that elucidates how organizational learning affects strategic renewal, which in turn impacts government performance.
Second, this study further expands the research on strategic renewal. Previous research on strategic renewal has primarily focused on business management with limited attention given to its application in government. Additionally, existing studies tend to predominantly emphasize qualitative approaches, leaving a gap in empirical research. The findings indicate that two dimensions of organizational learning significantly influence strategic renewal, which subsequently affects government performance. Consequently, this study broadens the research scope and deepens our understanding of strategic renewal.
Third, this study contributes to the literature on digital transformation and sustainability. Previous research has primarily focused on the enterprise level, with less attention given to the government level. It conducts a quantitative investigation into the impact of strategic renewal in digital transformation on government performance in the context of sustainable development. The findings reveal that strategic renewal significantly influences government performance.

7.2. Implications for Practice

The results of this study have significant practical implications for government agencies. Firstly, it is crucial for governments to consistently improve the digital technology expertise and digital capabilities of their public servants. To achieve this, the government must conduct expertise and capacity training on a regular or irregular basis. Secondly, the government can reduce the administrative burden of civil servants by means of inter-departmental meetings and reduction of formalism. This will enable them to have sufficient time and energy for self-improvement. Third, governments can facilitate the diffusion of knowledge among civil servants and within organizations by establishing and implementing digital knowledge-sharing platforms.

8. Limitations and Further Research

This study focused on a limited number of factors influencing strategic renewal in government through the lens of organizational learning. Future research could explore this topic from alternative perspectives or theories. Furthermore, subsequent studies should utilize organizational learning theory to investigate the effects of strategic renewal in domains such as public value.
The sample of this study is limited to China and involves a small number of senior managers. To enhance the robustness of the findings, it is essential to replicate the research in a broader context, particularly in Western countries, with a larger sample of senior managers. Thus, future research should aim to increase the sample size, especially among senior managers, to improve the stability and generalizability of the findings.

Author Contributions

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

Funding

This research supported by Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (2024SJYB0771), Key Projects of the 14th Five-Year Plan for Education Science Planning in Jiangsu Province (B-b/2024/01/164), the Fundamental Research Funds of the Central Universities (2024JCXKSK05).

Institutional Review Board Statement

According to Article 32 of “Ethical Review Measures for Human Life Sciences and Medical Research” issued by the National Health Commission of China (https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm, accessed on 29 December 2024) and the Research Ethics Review Guidelines of the National Social Science Fund of China, ethical review and approval were waived for this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Transforming Our World: The 2030 Agenda for Sustainable Development. Available online: https://www.un.org/sustainabledevelopment/development-agenda/ (accessed on 20 June 2025).
  2. Camodeca, R.; Almici, A. Digital transformation and convergence toward the 2030 agenda’s sustainability development goals: Evidence from Italian listed firms. Sustainability 2021, 13, 11831. [Google Scholar] [CrossRef]
  3. Asi, Y.M.; Williams, C. The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations. Int. J. Med. Inform. 2018, 114, 114–120. [Google Scholar] [CrossRef]
  4. Guandalini, I. Sustainability through digital transformation: A systematic literature review for research guidance. J. Bus. Res. 2022, 148, 456–471. [Google Scholar] [CrossRef]
  5. Bogusz, C.I.; Magnusson, J.; Rost, M. Leave it to the parents: How hacktivism-as-tuning reconfigures public sector digital transformation. Gov. Inf. Q. 2025, 42, 101996. [Google Scholar] [CrossRef]
  6. 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]
  7. 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]
  8. 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]
  9. Seele, P.; Lock, I. The game-changing potential of digitalization for sustainability: Possibilities, perils, and pathways. Sustain. Sci. 2017, 12, 183–185. [Google Scholar] [CrossRef]
  10. Sachs, J.D.; Schmidt-Traub, G.; Mazzucato, M.; Messner, D.; Nakicenovic, N.; Rockström, J. Six transformations to achieve the sustainable development goals. Nat. Sustain. 2019, 2, 805–814. [Google Scholar] [CrossRef]
  11. Gouvea, R.; Kapelianis, D.; Kassicieh, S. Assessing the nexus of sustainability and information & communications technology. Technol. Forecast. Soc. Change 2018, 130, 39–44. [Google Scholar]
  12. Diaz, R.; Montalvo, R. Digital transformation as an enabler to become more efficient in sustainability: Evidence from five leading companies in the Mexican market. Sustainability 2022, 14, 15436. [Google Scholar] [CrossRef]
  13. Katsamakas, E. Digital transformation and sustainable business models. Sustainability 2022, 14, 6414. [Google Scholar] [CrossRef]
  14. Li, L. Digital transformation and sustainable performance: The moderating role of market turbulence. Ind. Mark. Manag. 2022, 104, 28–37. [Google Scholar] [CrossRef]
  15. Rosário, A.T.; Dias, J.C. Sustainability and the digital transition: A literature review. Sustainability 2022, 14, 4072. [Google Scholar] [CrossRef]
  16. Pappas, I.O.; Mikalef, P.; Dwivedi, Y.K.; Jaccheri, L.; Krogstie, J. Responsible digital transformation for a sustainable society. Inf. Syst. Front. 2023, 25, 945–953. [Google Scholar] [CrossRef]
  17. Hajishirzi, R.; Costa, C.J.; Aparicio, M. Boosting sustainability through digital transformation’s domains and resilience. Sustainability 2022, 14, 1822. [Google Scholar] [CrossRef]
  18. Eom, S.J.; Lee, J. Digital government transformation in turbulent times: Responses, challenges, and future direction. Gov. Inf. Q. 2022, 39, 101690. [Google Scholar] [CrossRef] [PubMed]
  19. Curtis, S. Digital transformation—The silver bullet to public service improvement? Public Money Manag. 2019, 39, 322–324. [Google Scholar] [CrossRef]
  20. Gritt, E.; Forsgren, E.; Pandza, K. Liminal digital transformation in public sector: The case of UK policing. J. Strateg. Inf. Syst. 2024, 33, 101851. [Google Scholar] [CrossRef]
  21. Carter, L.; Desouza, K.C.; Dawson, G.S.; Pardo, T. Digital transformation of the public sector: Designing strategic information systems. J. Strateg. Inf. Syst. 2024, 33, 101853. [Google Scholar] [CrossRef]
  22. Faro, B.; Abedin, B.; Cetindamar, D. Hybrid organizational forms in public sector’s digital transformation: A technology enactment approach. J. Enterp. Inf. Manag. 2022, 35, 1742–1763. [Google Scholar] [CrossRef]
  23. Venson, E.; da Costa Figueiredo, R.M.; Canedo, E.D. Leveraging a startup-based approach for digital transformation in the public sector: A case study of Brazil’s startup gov. br program. Gov. Inf. Q. 2024, 41, 101943. [Google Scholar] [CrossRef]
  24. Gabryelczyk, R. Has COVID-19 accelerated digital transformation? Initial lessons learned for public administrations. Inf. Syst. Manag. 2020, 37, 303–309. [Google Scholar] [CrossRef]
  25. Klammer, A.; Gueldenberg, S.; Kraus, S.; O’Dwyer, M. To change or not to change–antecedents and outcomes of strategic renewal in SMEs. Int. Entrep. Manag. J. 2017, 13, 739–756. [Google Scholar] [CrossRef]
  26. Vaillant, Y.; Vendrell-Herrero, F.; Bustinza, O.F.; Xing, Y. HRM algorithms: Moderating the relationship between chaotic markets and strategic renewal. Br. J. Manag. 2025, 36, 529–545. [Google Scholar] [CrossRef]
  27. Agarwal, R.; Helfat, C.E. Strategic renewal of organizations. Organ. Sci. 2009, 20, 281–293. [Google Scholar] [CrossRef]
  28. Khan, S.H.; Majid, A.; Yasir, M. Strategic renewal of SMEs: The impact of social capital, strategic agility and absorptive capacity. Manag. Decis. 2021, 59, 1877–1894. [Google Scholar] [CrossRef]
  29. Crossan, M.M.; Lane, H.W.; White, R.E. An organizational learning framework: From intuition to institution. Acad. Manag. Rev. 1999, 24, 522–537. [Google Scholar] [CrossRef]
  30. Vera, D.; Crossan, M. Strategic leadership and organizational learning. Acad. Manag. Rev. 2004, 29, 222–240. [Google Scholar] [CrossRef]
  31. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2019, 36, 101385. [Google Scholar] [CrossRef]
  32. Datta, P. Digital transformation of the Italian public administration: A case study. Commun. Assoc. Inf. Syst. 2020, 46, 252–272. [Google Scholar] [CrossRef]
  33. Begany, G.M.; Gil-Garcia, J.R. Open government data initiatives as agents of digital transformation in the public sector: Exploring the extent of use among early adopters. Gov. Inf. Q. 2024, 41, 101955. [Google Scholar] [CrossRef]
  34. Hernandez, M.G. Reflections on three decades of digital transformation in local governments. Local Gov. Stud. 2024, 50, 1028–1040. [Google Scholar] [CrossRef]
  35. Yuan, Y.P.; Dwivedi, Y.K.; Tan, G.W.H.; Cham, T.H.; Ooi, K.B.; Aw, E.C.X.; Currie, W. Government digital transformation: Understanding the role of government social media. Gov. Inf. Q. 2023, 40, 101775. [Google Scholar] [CrossRef]
  36. Crusoe, J.; Magnusson, J.; Eklund, J. Digital transformation decoupling: The impact of willful ignorance on public sector digital transformation. Gov. Inf. Q. 2024, 41, 101958. [Google Scholar] [CrossRef]
  37. 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]
  38. 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]
  39. Afzal, M.; Panagiotopoulos, P. Coping with digital transformation in frontline public services: A study of user adaptation in policing. Gov. Inf. Q. 2024, 41, 101977. [Google Scholar] [CrossRef]
  40. Moser-Plautz, B.; Schmidthuber, L. Digital government transformation as an organizational response to the COVID-19 pandemic. Gov. Inf. Q. 2023, 40, 101815. [Google Scholar] [CrossRef]
  41. Kuhlmann, S.; Heuberger, M. Digital transformation going local: Implementation, impacts and constraints from a German perspective. Public Money Manag. 2023, 43, 147–155. [Google Scholar] [CrossRef]
  42. Berman, S.J. Digital transformation: Opportunities to create new business models. Strategy Leadersh. 2012, 40, 16–24. [Google Scholar] [CrossRef]
  43. Cordella, A.; Bonina, C.M. A public value perspective for ICT enabled public sector reforms: A theoretical reflection. Gov. Inf. Q. 2012, 29, 512–520. [Google Scholar] [CrossRef]
  44. Twizeyimana, J.D.; Andersson, A. The public value of E-Government–A literature review. Gov. Inf. Q. 2019, 36, 167–178. [Google Scholar] [CrossRef]
  45. Yang, C.; Gu, M.; Albitar, K. Government in the digital age: Exploring the impact of digital transformation on governmental efficiency. Technol. Forecast. Soc. Change 2024, 208, 123722. [Google Scholar] [CrossRef]
  46. Karataş-Özkan, M.; Murphy, W.D. Critical theorist, postmodernist and social constructionist paradigms in organizational analysis: A paradigmatic review of organizational learning literature. Int. J. Manag. Rev. 2010, 12, 453–465. [Google Scholar] [CrossRef]
  47. Valentine, M.A. Renegotiating spheres of obligation: The role of hierarchy in organizational learning. Adm. Sci. Q. 2018, 63, 570–606. [Google Scholar] [CrossRef]
  48. Fiol, C.M.; Lyles, M.A. Organizational learning. Acad. Manag. Rev. 1985, 10, 803–813. [Google Scholar] [CrossRef]
  49. Edmondson, A.C. The local and variegated nature of learning in organizations: A group-level perspective. Organ. Sci. 2002, 13, 128–146. [Google Scholar] [CrossRef]
  50. Argote, L.; Miron-Spektor, E. Organizational learning: From experience to knowledge. Organ. Sci. 2011, 22, 1123–1137. [Google Scholar] [CrossRef]
  51. Ding, Z.; Li, M.; Yang, X.; Xiao, W. Ambidextrous organizational learning and performance: Absorptive capacity in small and medium-sized enterprises. Manag. Decis. 2023, 61, 3610–3634. [Google Scholar] [CrossRef]
  52. Real, J.C.; Roldán, J.L.; Leal, A. From entrepreneurial orientation and learning orientation to business performance: Analysing the mediating role of organizational learning and the moderating effects of organizational size. Br. J. Manag. 2014, 25, 186–208. [Google Scholar] [CrossRef]
  53. Aragón-Correa, J.A.; García-Morales, V.J.; Cordón-Pozo, E. Leadership and organizational learning’s role on innovation and performance: Lessons from Spain. Ind. Mark. Manag. 2007, 36, 349–359. [Google Scholar] [CrossRef]
  54. Koo, Y.; Lee, J.N.; Heng, C.S.; Park, J. Effect of multi-vendor outsourcing on organizational learning: A social relation perspective. Inf. Manag. 2017, 54, 396–413. [Google Scholar] [CrossRef]
  55. Huber, G.P. Organizational learning: The contributing processes and the literatures. Organ. Sci. 1991, 2, 88–115. [Google Scholar] [CrossRef]
  56. Mahler, J. Influences of organizational culture on learning in public agencies. J. Public Adm. Res. Theory 1997, 7, 519–540. [Google Scholar] [CrossRef]
  57. Janson, M.; Cecez-Kecmanovic, D.; Zupančič, J. Prospering in a transition economy through information technology-supported organizational learning. Inf. Syst. J. 2007, 17, 3–36. [Google Scholar] [CrossRef]
  58. Phang, C.W.; Kankanhalli, A.; Ang, C. Investigating organizational learning in eGovernment projects: A multi-theoretic approach. J. Strateg. Inf. Syst. 2008, 17, 99–123. [Google Scholar] [CrossRef]
  59. Crossan, M.M.; Berdrow, I. Organizational learning and strategic renewal. Strateg. Manag. J. 2003, 24, 1087–1105. [Google Scholar] [CrossRef]
  60. Jones, O.; Macpherson, A. Inter-organizational learning and strategic renewal in SMEs: Extending the 4I framework. Long Range Plan. 2006, 39, 155–175. [Google Scholar] [CrossRef]
  61. Nadkarni, S.; Prügl, R. Digital transformation: A review, synthesis and opportunities for future research. Manag. Rev. Q. 2021, 71, 233–341. [Google Scholar] [CrossRef]
  62. Meena, A.; Dhir, S.; Sushil, S. Coopetition, strategy, and business performance in the era of digital transformation using a multi-method approach: Some research implications for strategy and operations management. Int. J. Prod. Econ. 2024, 270, 109068. [Google Scholar] [CrossRef]
  63. Zhang, Z.; Jin, J.; Li, S.; Zhang, Y. Digital transformation of incumbent firms from the perspective of portfolios of innovation. Technol. Soc. 2023, 72, 102149. [Google Scholar] [CrossRef]
  64. Jiménez-Jiménez, D.; Sanz-Valle, R. Innovation, organizational learning, and performance. J. Bus. Res. 2011, 64, 408–417. [Google Scholar] [CrossRef]
  65. Santos-Vijande, M.L.; López-Sánchez, J.Á.; Trespalacios, J.A. How organizational learning affects a firm’s flexibility, competitive strategy, and performance. J. Bus. Res. 2012, 65, 1079–1089. [Google Scholar] [CrossRef]
  66. Mann, G.; Karanasios, S.; Breidbach, C.F. Orchestrating the digital transformation of a business ecosystem. J. Strateg. Inf. Syst. 2022, 31, 101733. [Google Scholar] [CrossRef]
  67. Shin, K.; Pérez-Nordtvedt, L. Knowledge acquisition efficiency, strategic renewal frequency and firm performance in high velocity environments. J. Knowl. Manag. 2020, 24, 2035–2055. [Google Scholar] [CrossRef]
  68. Kwee, Z.; Van Den Bosch, F.A.; Volberda, H.W. The influence of top management team’s corporate governance orientation on strategic renewal trajectories: A longitudinal analysis of Royal Dutch Shell plc, 1907–2004. J. Manag. Stud. 2011, 48, 984–1014. [Google Scholar] [CrossRef]
  69. Volberda, H.W.; Van den Bosch, F.A.; Flier, B.; Gedajlovic, E.R. Following the herd or not?: Patterns of renewal in the Netherlands and the UK. Long Range Plan. 2001, 34, 209–229. [Google Scholar] [CrossRef]
  70. García-Morales, V.J.; Jiménez-Barrionuevo, M.M.; Gutiérrez-Gutiérrez, L. Transformational leadership influence on organizational performance through organizational learning and innovation. J. Bus. Res. 2012, 65, 1040–1050. [Google Scholar] [CrossRef]
  71. Slater, S.F.; Narver, J.C. Market orientation and the learning organization. J. Mark. 1995, 59, 63–74. [Google Scholar] [CrossRef]
  72. Real, J.C.; Leal, A.; Roldán, J.L. Information technology as a determinant of organizational learning and technological distinctive competencies. Ind. Mark. Manag. 2006, 35, 505–521. [Google Scholar] [CrossRef]
  73. Cegarra-Navarro, J.G.; Jiménez, D.J.; Martínez-Conesa, E.Á. Implementing e-business through organizational learning: An empirical investigation in SMEs. Int. J. Inf. Manag. 2007, 27, 173–186. [Google Scholar] [CrossRef]
  74. Yu, Y.; Dong, X.Y.; Shen, K.N.; Khalifa, M.; Hao, J.X. Strategies, technologies, and organizational learning for developing organizational innovativeness in emerging economies. J. Bus. Res. 2013, 66, 2507–2514. [Google Scholar] [CrossRef]
  75. Moorman, C.; Miner, A.S. Organizational improvisation and organizational memory. Acad. Manag. Rev. 1998, 23, 698–723. [Google Scholar] [CrossRef]
  76. Girod, M.S. La mémoire organisationnelle [Organizational Memory]. Rev. Française Gest. 1995, 105, 30–42. [Google Scholar]
  77. Yan, M.; Yu, Y.; Dong, X. Contributive roles of multilevel organizational learning for the evolution of organizational ambidexterity. Inf. Technol. People 2016, 29, 647–667. [Google Scholar] [CrossRef]
  78. Raoofian, A.; Rajabzadeh Ghatari, A.; Fakhar Manesh, M.; Palumbo, R. Digging into the drivers of strategic renewal: A systematic literature review. Int. J. Organ. Anal. 2025, 33, 75–104. [Google Scholar] [CrossRef]
  79. Schmitt, A.; Raisch, S.; Volberda, H.W. Strategic renewal: Past research, theoretical tensions and future challenges. Int. J. Manag. Rev. 2018, 20, 81–98. [Google Scholar] [CrossRef]
  80. Sievinen, H.M.; Ikäheimonen, T.; Pihkala, T. Strategic renewal in a mature family-owned company—A resource role of the owners. Long Range Plan. 2020, 53, 101864. [Google Scholar] [CrossRef]
  81. Schmitt, A.; Barker, V.L., III; Raisch, S.; Whetten, D. Strategic renewal in times of environmental scarcity. Long Range Plan. 2016, 49, 361–376. [Google Scholar] [CrossRef]
  82. Albert, D.; Kreutzer, M.; Lechner, C. Resolving the paradox of interdependency and strategic renewal in activity systems. Acad. Manag. Rev. 2015, 40, 210–234. [Google Scholar] [CrossRef]
  83. Hortovanyi, L.; Szabo, R.Z.; Fuzes, P. Extension of the strategic renewal journey framework: The changing role of middle management. Technol. Soc. 2021, 65, 101540. [Google Scholar] [CrossRef]
  84. Nguyen, K.; Peltoniemi, M.; Lamberg, J.A. Strategic renewal: Can it be done profitably? Long Range Plan. 2022, 55, 102179. [Google Scholar] [CrossRef]
  85. Floyd, S.W.; Lane, P.J. Strategizing throughout the organization: Managing role conflict in strategic renewal. Acad. Manag. Rev. 2000, 25, 154–177. [Google Scholar] [CrossRef]
  86. Huff, J.O.; Huff, A.S.; Thomas, H. Strategic renewal and the interaction of cumulative stress and inertia. Strateg. Manag. J. 1992, 13 (Suppl. S1), 55–75. [Google Scholar] [CrossRef]
  87. Jantunen, A.; Tuppura, A.; Pätäri, S. Dominant logic–Cognitive and practiced facets and their relationships to strategic renewal and performance. Eur. Manag. J. 2024, 42, 108–118. [Google Scholar] [CrossRef]
  88. 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]
  89. Sussan, F.; Acs, Z.J. The digital entrepreneurial ecosystem. Small Bus. Econ. 2017, 49, 55–73. [Google Scholar] [CrossRef]
  90. Chen, H.; Tian, Z. Environmental uncertainty, resource orchestration and digital transformation: A fuzzy-set QCA approach. J. Bus. Res. 2022, 139, 184–193. [Google Scholar] [CrossRef]
  91. Jia, J.; Xu, Y.; Li, W. A study on the strategic momentum of SMEs’ digital transformation: Evidence from China. Technol. Forecast. Soc. Change 2024, 200, 123038. [Google Scholar] [CrossRef]
  92. Chen, Y.; Wang, Y.; Zhao, C. From riches to digitalization: The role of AMC in overcoming challenges of digital transformation in resource-rich regions. Technol. Forecast. Soc. Change 2024, 200, 123153. [Google Scholar] [CrossRef]
  93. Walsh, J.P.; Ungson, G.R. Organizational memory. Acad. Manag. Rev. 1991, 16, 57–91. [Google Scholar] [CrossRef]
  94. Rosen, B.; Furst, S.; Blackburn, R. Overcoming barriers to knowledge sharing in virtual teams. Organ. Dyn. 2007, 36, 259–273. [Google Scholar] [CrossRef]
  95. Anwar, R.; Rehman, M.; Wang, K.S.; Hashmani, M.A. Systematic literature review of knowledge sharing barriers and facilitators in global software development organizations using concept maps. IEEE Access 2019, 7, 24231–24247. [Google Scholar] [CrossRef]
  96. Disterer, G. Individual and social barriers to knowledge transfer. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences, Maui, HI, USA, 6 January 2001; pp. 1–17. [Google Scholar]
Sustainability 17 07055 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
Sustainability 17 07055 g001
Sustainability 17 07055 g002
Figure 2. SEM analysis of the research model. *** p < 0.001.
Figure 2. SEM analysis of the research model. *** p < 0.001.
Sustainability 17 07055 g002
Table 1. Demographic characteristics of samples.
Table 1. Demographic characteristics of samples.
MeasureItemsFrequencyPercentage
GendersMale12640.2%
Female18759.8%
AgeLess than 25 years old9530.2%
25–34 years old16552.7%
35–44 years old3812.0%
45–54 years old134.0%
55 years old and above31.1%
EducationJunior college10.3%
College185.7%
Bachelor’s degree17355.3%
Master’s degree and above12138.7%
Work experienceLess than 3 years15148.4%
3–5 years10533.6%
6–10 years3611.4%
10 years and above216.6%
Table 2. Measure scales and convergent validity.
Table 2. Measure scales and convergent validity.
ConstructMeasureLoad FactorAVECR
Knowledge acquisition (KA)I understand the department’s work philosophy0.7120.51460.8091
I was encouraged to gather information about departmental change0.704
our department constantly evaluate the need to adapt to the business0.719
Our department uses a formal procedure to evaluate results and compare them with other departments0.734
Knowledge distribution (KD)Our department has established a meeting schedule with other departments to consolidate available information0.7120.59490.811
Our department will spend some time discussing future development needs0.931
Our department will internally communicate the overall goals of the department0.641
Shared interpretation (SI)Our department will promptly update our perspective about the external environment0.8030.50330.7505
Our department will prepare a brief report0.626
Our department conducts a thorough analysis of the different options to choose the best one0.688
Organizational memory (OM)Turnover in our department does not affect the department’s ability to create new knowledge0.7390.56620.7965
Our department knows each member’s specialty and experience0.748
When our department faces new opportunities or problems, we can promptly contact key personnel0.77
Exploitative renewal (ER)Our department improves the efficiency of public services0.8150.63870.8413
Our department increases economies of scale in existing services0.805
Our department expands its services to existing users0.777
Explorative renewal (EE)Our department will be launching new services0.7360.55140.8308
Our department experiment with new services0.791
Our department will invent new services0.712
Our department embraces the need to go beyond existing services0.729
Government performance (GP)Our department adopts a data-driven model0.7990.66060.8536
Our department can restructure the department by data analysis0.776
Our department has established a decision-making by data analysis0.861
Table 3. Discriminative validity.
Table 3. Discriminative validity.
ConstructAVEFactor Correlation
KAKDSIOMEREEGP
KA0.5150.809
KD0.5950.2420.811
SI0.5030.1010.0890.751
OM0.5660.4840.1410.0820.797
ER0.6390.5110.1410.1100.4980.841
EE0.5510.4370.1560.1260.4850.5780.831
GP0.6610.4120.1860.1040.3620.4830.5200.854
Table 4. Model-fit test.
Table 4. Model-fit test.
Model-Fit IndicesResultsRecommended Value
Chi-square statistic χ2/df1.477(χ2 = 316.143/ df = 214) ≤3
GFI0.920 ≥0.9
CFI0.966 ≥0.9
NFI0.904 ≥0.9
RMSEA0.039 <0.1
Table 5. Research hypothesis testing results.
Table 5. Research hypothesis testing results.
HypothesesPath CoefficientsResult
KA→ER0.291 ***Supported
KA→EE0.402 ***Supported
KD→ER−0.005Not support
KD→EE−0.043Not support
SI→ER0.085Not support
SI→EE0.07Not support
OM→ER0.448 ***Supported
OM→EE0.407 ***Supported
ER→GP0.411 ***Supported
EE→GP0.326 ***Supported
*** p < 0.001.
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

Xiao, J.; Lu, Y.; Zhang, H. The Antecedents and Consequences of Strategic Renewal in Digital Transformation in the Context of Sustainability: An Empirical Analysis. Sustainability 2025, 17, 7055. https://doi.org/10.3390/su17157055

AMA Style

Xiao J, Lu Y, Zhang H. The Antecedents and Consequences of Strategic Renewal in Digital Transformation in the Context of Sustainability: An Empirical Analysis. Sustainability. 2025; 17(15):7055. https://doi.org/10.3390/su17157055

Chicago/Turabian Style

Xiao, Jianying, Yitong Lu, and Hui Zhang. 2025. "The Antecedents and Consequences of Strategic Renewal in Digital Transformation in the Context of Sustainability: An Empirical Analysis" Sustainability 17, no. 15: 7055. https://doi.org/10.3390/su17157055

APA Style

Xiao, J., Lu, Y., & Zhang, H. (2025). The Antecedents and Consequences of Strategic Renewal in Digital Transformation in the Context of Sustainability: An Empirical Analysis. Sustainability, 17(15), 7055. https://doi.org/10.3390/su17157055

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

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop