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Article

Digital Red Tape in Public Organizations: Challenges to Sustainable Digital Transformation

by
Juan Liu
1,
Shuigen Hu
1,*,
Yantong Jin
1 and
Lieen Weng
2,*
1
School of Public Affairs, Zhejiang University, Hangzhou 310058, China
2
School of Public Administration, Zhejiang University of Technology, Hangzhou 310023, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(23), 10681; https://doi.org/10.3390/su172310681
Submission received: 29 October 2025 / Revised: 20 November 2025 / Accepted: 25 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Enterprise Performance, Teamwork, and Team Management: A Perspective of Sustainable Enterprise and Public Organizations Development)
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Abstract

Digital transformation is expected to improve the sustainability, efficiency, and transparency of public organizations. Yet, it also entails unintended consequences by generating digital red tape, defined as dysfunctional rules that impose compliance burdens through their integration with digital technologies. This study examines how organizational structure shapes the emergence of digital red tape and how these patterns affect the sustainability of digital transformation. Using two-wave survey data from public employees, digital red tape was measured as digital compliance burden and digital functionality deficiency, while formalization and centralization captured key structural dimensions. Group comparisons were conducted to assess differences in digital red tape and its two dimensions across demographic and organizational categories, followed by robust OLS regressions estimated for upper, middle, and lower bureaucratic echelons. The results show that younger employees and those in lower-echelon organizations perceive higher levels of digital red tape. Across the full sample, both formalization and centralization are positively and significantly associated with digital red tape, with centralization displaying the strongest and most consistent relationships. Echelon-specific regressions further indicate that these structural associations vary in magnitude across hierarchical levels. Centralization remains positively related to digital red tape in all echelons, while the association between formalization and digital red tape appears most pronounced in the middle echelon. Ultimately, sustainable digital transformation requires recognizing both the existence of digital red tape and the ways in which organizational structures shape its emergence and distribution, potentially constraining organizational innovation and diminishing public value.

1. Introduction

Digital transformation has become a central component of contemporary public sector reform and is often presented as a path toward a more efficient, transparent, and citizen-oriented administration [1]. Digitalization of public organizations involves redesigning processes, procedures, structures, and services so that new technologies become fully institutionalized within organizational routines [2,3,4]. Research shows that digital transformation reshapes work practices, alters communication patterns, transforms organizational structures and cultures, and redefines how public value is created and delivered [5]. For this reason, digital transformation is increasingly understood as a long-term organizational and institutional process that requires continuous learning, flexibility, and sustained governance capacity.
At the same time, public administrations face a dual pressure. Societies expect innovative and responsive digital services, while fiscal and managerial constraints push organizations toward efficiency, cost reduction, and performance [6]. Under these conditions, sustaining digital transformation over time becomes challenging. The sustainability agenda reflected in SDG 16 emphasizes the need for effective, accountable, and inclusive public institutions, highlighting the importance of maintaining public value and organizational capacity throughout digital reform processes [7]. Achieving sustainable digital transformation requires institutions capable of adaptation, innovation, and coordinated governance [8,9,10].
However, as organizations undergo digital transformation, a new form of organizational pathology has emerged: digital red tape, which refers to dysfunctional rules and regulations that create compliance burdens through their integration with digital technology [11]. Compared to private entities, public organizations face greater legal and public accountability, often have conflicting goals, and operate under more bureaucratic structures [12]. These characteristics may amplify the complexities introduced by digital transformation. Evidence from traditional red tape research shows that dysfunctional rules undermine managerial support, restrict autonomy, suppress innovation cultures, and generate frustration, stress, and exhaustion [13,14,15,16]. When such dynamics become embedded in digital infrastructures, their effects may be intensified as digital procedures are far less flexible and difficult to adapt or circumvent. These pressures weaken organizational learning, experimentation, and adaptability, which are essential for sustaining digital transformation over time.
Research on digital red tape has grown in recent years, mostly within WEIRD (western, educated, industrialized, rich, and democratic) contexts. Existing studies show that digital tools can generate new rule burdens and exacerbate rule dysfunction; for example, those arising from digital HR systems [17,18], university research robotic administration platforms [19], or digital accounting technologies [20]. Yet the empirical base remains concentrated in WEIRD contexts, and systematic evidence from non-WEIRD administrative systems is still limited, particularly regarding internal digital red tape as experienced by public employees within organizations. In addition, the role of hierarchical position in shaping perceptions of digital red tape remains insufficiently understood. Classic studies of red tape have long shown that organizational position shapes perceptions of rules [21,22]. Senior managers tend to view rules as instruments of coordination and accountability, whereas frontline officials often experience them as obstacles to effective service delivery. However, it remains unclear whether these hierarchical differences persist, or perhaps even deepen, in the digital era, particularly within administrative systems characterized by steep bureaucratic hierarchies such as China’s. To address this gap, this study investigates four questions using a two-wave survey design in China: (1) Do systematic differences in perceived digital red tape persist? (2) How do structural features, particularly formalization and centralization, relate to perceived digital red tape? (3) Do these structural effects vary across different bureaucratic echelons? (4) What are the implications of these patterns for the sustainability of digital transformation in public organizations? While the two-wave design helps reduce common method concerns, the analysis focuses on associative and predictive relationships rather than establishing strong causal effects.
This study makes four main contributions by addressing the research questions outlined above. First, it extends the organizational echelon perspective to the context of digital governance by examining hierarchical differences in perceived digital red tape. Second, it provides empirical evidence from China on how formalization and centralization relate to digital red tape. Third, it demonstrates that these structural effects vary across bureaucratic echelons, clarifying how bureaucratic echelons shape the implementation and consequences of digital governance. Finally, by situating the analysis within China’s bureaucratic system, the study contributes a non-WEIRD perspective that broadens comparative debates on digital transformation and highlights the implications of these patterns for the long-term sustainability of digital transformation in public organizations.
The remainder of the article is organized as follows. Section 2 reviews the relevant literature and develops the conceptual hypotheses. Section 3 describes the data and research methods. Section 4 presents the empirical results. Section 5 discusses the implications of the findings, outlines the study’s limitations, and suggests avenues for future research.

2. Theoretical Background

2.1. From Red Tape to Digital Red Tape

The term red tape originated from the medieval English practice of binding official documents with red ribbons and later evolved into a metaphor for bureaucratic constraints. In public administration research, it gained theoretical prominence in the 1990s to describe rules that impose compliance burdens without serving legitimate purposes [23]. Over time, two major perspectives have shaped the conceptual development of red tape: the economic benefit–cost view, which focuses on organizational consequences, and the psychological process view, which emphasizes individual perceptions [24]. Contemporary definitions converge on the idea that red tape consists of rules and procedures that create excessive compliance burdens relative to achieving legitimate objectives [25,26]. A commonly conflated concept is administrative burden, which refers to the learning, compliance, and psychological costs individuals face when interacting with the state. In contrast, administrative burden research focuses on the frictions encountered by citizens, whereas red tape concerns to dysfunctional organizational rules themselves [27,28,29].
The emergence of digital red tape is intrinsically linked to the digital transformation of the public sector. Traditional red tape and digital red tape belong to the same family of red tape yet arise in different bureaucratic environments. Traditional red tape developed within traditional Weberian bureaucracy, which is characterized by formalized, hierarchical, and routine-based rule systems, whereas digital red tape emerges within newer forms of algorithmic and robotic bureaucracy, marked by AI-mediated, automated, and highly structured digital administrative processes [19,30,31,32]. Although digital technologies were initially introduced as tools to eliminate traditional red tape and improve administrative efficiency [33,34,35], the effects of computer-based systems are highly context dependent, and the outcomes of technological reforms intended to cut red tape have often proven unpredictable [19,36,37,38]. Subsequent research has shown that digitalization can also generate new forms of red tape. In many cases, digital systems have failed to reduce workloads [39], fostered informal formalization, and turned government agencies into “late-bureaucratic” organizations rather than post-bureaucratic ones [40]. Digital red tape arises when dysfunctional rules become embedded in information and communication technology (ICT) systems [11]. As Peled (2001) noted, technology replacement can render existing rules dysfunctional [41], while digitalization may alter the rule ecology, transforming once-functional rules into sources of red tape [25].
Digital red tape affects the long-term sustainability of digital transformation. Human resource management research underscores that sustainable organizational performance depends on skilled, committed, and adaptive personnel [42]. Digital red tape that erodes autonomy, reduces discretion, or creates psychological strain can weaken these human capital foundations. Traditional red tape research shows consistent negative effects on managerial support, innovation cultures, and employee well-being [14,43], and these patterns are likely to intensify when burdens are embedded within digital systems. At a more systemic level, the broader sustainability agenda reinforces the significance of these risks. The SDG literature conceptualizes the SDGs themselves as wicked problems, meaning challenges that are systemic, dynamic, and highly interdependent, requiring long-term capability building and coordinated action among multiple actors [44]. When burdensome and dysfunctional rules become embedded in digital infrastructures, they restrict exactly the adaptive capacity needed to address these complex governance demands. Understanding how these digitally embedded constraints are shaped by organizational structure, and how their effects vary across hierarchical levels, therefore represents an important and still underexplored question in digital governance research.

2.2. Organizational Structure and Digital Red Tape

Organizational structure captures patterned relationships of authority, communication, and control and is commonly represented by dimensions such as formalization and centralization [45,46]. Early work linking structure to red tape focused on formalization, largely because red tape was operationalized as rules and procedures that impose compliance burdens without functional value [23,25]. More recent research argues that red tape is a multifaceted perception of organizational structure rather than only the result of pathological formalization. Empirical evidence points to three core structural dimensions that shape perceived red tape; namely, formalization, centralization, and hierarchy [47]. Taken together, this multidimensional view suggests that structure is not a static set of rules but a set of patterned constraints and enablers enacted in practice. When organizations digitalize their processes, these structural features become embedded in information systems. In this way, technology not only reproduces existing rules but also generates new ones, shaping how formal structures are enacted and perceived [48,49].

2.2.1. Formalization and Digital Red Tape

Formalization is the extent to which rules and procedures are written and standardized [46]. Its consequences are context dependent, sometimes increasing alienation and stress and sometimes supporting efficiency and accountability [45,47,50]. Classical theory implies that a larger stock of controlling rules raises the probability that some become dysfunctional, which is the core logic behind traditional red tape [23]. Later work emphasizes the perceptual nature of red tape, reflecting how employees experience formalized rules in practice [51,52]. When administrative routines become embedded in digital systems, written rules are transformed into software specifications and transaction logic. Once routines are hard coded through validation checks, mandatory fields, audit trails, and other material features, local discretion narrows and dysfunctional rules can be reproduced or amplified [11,53]. Technology replacement can also render formerly functional rules obsolete, altering the rule ecology in ways that increase perceived burden, including functionality deficiency when information architectures exclude or misfit user groups [25,41,54]. Accordingly, the following hypothesis is proposed:
H1. 
Organizational formalization is positively associated with digital red tape.

2.2.2. Centralization and Digital Red Tape

Centralization is the concentration of decision authority at higher organizational levels [45]. It strengthens managerial control but limits discretion and participation, slows information flows, and is associated with lower satisfaction, whereas decentralization facilitates local adaptation and quicker responsiveness [55,56,57]. Digitalization accelerates information flows and shortens decision windows, which increases the value of locally responsive structures [58]. In digital workflows, centralized authority is often embedded in platforms via role-based permissions, escalation rules, and multi-step approvals. As authorizations move upward by design, cycle times expand and frontline discretion contracts, consistent with the shift from street-level to system-level bureaucracy and with the rise of algorithmic bureaucracy, where design choices channel discretion to system designers [31,59]. Evidence on robotic bureaucracy further shows that administrative burden depends on system design and the characteristics of senders and receivers, implying that centralized digital workflows are especially prone to perceived red tape when users cannot adapt procedures [19,60]. This leads to the second hypothesis:
H2. 
Organizational centralization is positively associated with digital red tape.

2.3. Bureaucratic Echelon and the Chinese Administrative Context

Research on organizational echelons suggests that hierarchical position shapes how public employees perceive and interpret bureaucratic constraints. Walker and Brewer (2008) conceptualize organizational echelons as levels or strata within organizations [21], building on earlier studies that distinguished senior, middle, and frontline managerial groups [61,62,63]. This line of work reveals a clear gradient: officials at higher levels, operating with greater discretion and a broader strategic vantage point, tend to perceive fewer procedural constraints, whereas frontline staff encounter denser and more intrusive rules [21,22]. These findings suggest that the experience of red tape depends on positional distance from decision-making centers and the degree of discretionary authority, and this logic can be extended from internal managerial strata to broader administrative hierarchies.
This perspective is particularly relevant in China, where digitalization is embedded in a steeply stratified administrative hierarchy. Since the launch of the Internet Plus Government Services reform in 2015 [64], China has rapidly expanded digital governance, with more than ninety percent of government services and most provincial administrative permits now processed through digital platforms [65]. Zhou and Lian (2020) conceptualize Chinese bureaucracy as a three-level control system comprising a principal, supervisor, and agent, roughly corresponding to upper, middle, and lower-tier authorities [66]. The principal formulates policy and performance incentives, the supervisor monitors implementation, and the agent executes directives. This tripartite model mirrors the echelon logic in Western studies and provides a structural bridge for comparative analysis. Building on this, Zhou (2021) highlights that China’s bureaucratic system intertwines vertical functional lines (ministries and agencies) with horizontal territorial administrations (provincial, municipal, county, and township governments), creating a dual-authority structure that shapes how rules are interpreted and enforced across levels [67].
Given these characteristics of Chinese governance, it is reasonable to expect that the structural antecedents of digital red tape are not uniform across hierarchical levels. Within this complex hierarchy, each echelon faces distinct incentives, accountability mechanisms, and digital regulatory environments. These dynamics are further reinforced by China’s target-based performance system, in which objectives cascade downward through a top-down allocation process [68]. As performance targets cascade from central to local levels, a “target pyramid” emerges in which each echelon functions simultaneously as both evaluator and evaluated. This cascading system intensifies hierarchical accountability and transmits administrative pressure downward through the bureaucratic chain [69], creating conditions under which digital technologies may either alleviate or exacerbate bureaucratic burdens.
Building on this echelon perspective, we argue that employees in upper echelon organizations, who are closer to policy design and enjoy greater discretion and resources, are more likely to experience digital systems as tools for coordination, oversight, and performance monitoring. Middle echelon organizations, positioned between higher level directives and frontline implementation, must both interpret digital requirements from above and enforce them below, which exposes them to dual pressures from target setting and compliance. In contrast, employees in lower echelon organizations, bound by detailed procedures and performance mandates and directly responsible for data entry and reporting, are most likely to encounter digital systems as additional administrative burdens. In this context, structural features such as formalization and centralization do not operate in a vacuum but interact with echelon specific roles and pressures to shape the extent of digital red tape. Conceptually, this implies a pathway in which organizational structure and bureaucratic echelon jointly influence the level of digital red tape, which in turn has important implications for the sustainability of digital transformation by constraining learning, experimentation, and adaptive capacity. Accordingly, this study expects that the strength of the structural effects of formalization and centralization will vary across bureaucratic echelons, reflecting hierarchical heterogeneity in the generation of digital red tape. This leads to the third hypothesis:
H3. 
The influence of organizational formalization and centralization on digital red tape exhibits hierarchical heterogeneity across bureaucratic echelons.
In summary, the theoretical model developed in this study links organizational structure, bureaucratic echelon, and digital red tape. Formalization and centralization function as structural antecedents of digital red tape, but their influence is expected to vary across hierarchical positions due to echelon-specific roles and responsibilities. In this study, the bureaucratic echelon therefore operates as an analytical lens for detecting such variation. This pathway clarifies how organizational structure shapes digital red tape differently across echelons and why these patterns matter for the sustainability of digital transformation.

3. Methods

3.1. Sample and Data Collection

The survey targeted employees in Chinese public organizations across different areas. Data were collected through the Credamo online platform, which allows stratified sampling by occupation and demographics. To ensure data quality, only participants with credibility and approval scores above 60% could join, each IP address was restricted to one submission, and responses were monitored for reliability. To reduce potential common method bias, both procedural and statistical remedies were applied. Following Podsakoff et al. (2003) [70], the study adopted a temporal separation and response anonymity design in which independent and dependent variables were measured at two points separated by about two months, both completed anonymously. Although organizational structure was measured at Wave 2 and digital red tape at Wave 1, both constructs are expected to be stable over short periods, so this sequencing functions as a procedural remedy for common method bias rather than implying temporal causality. Attention-check items were embedded in both waves, and those failing the checks or responding unrealistically fast were removed. The anonymous and non-evaluative design further reduced evaluation apprehension and social desirability bias [71]. Data collection took place from November 2024 to March 2025. All participants were informed about confidentiality and voluntarily consented to follow-up participation. At Time 1, 565 questionnaires were received; after screening, 550 valid responses remained. At Time 2, 456 responses were collected, and after excluding four invalid cases, a total of 452 matched questionnaires were retained for analysis. China’s public administration operates within a five-tier hierarchy consisting of the central, provincial, municipal, county, and township levels.

3.2. Measures

Table 1 summarizes the demographic and organizational characteristics of the 452 valid respondents, and Table 2 provides an overview of the constructs, sources, and sample items (the full questionnaire appears in Appendix A). All scales were adapted from well-established instruments and demonstrate satisfactory to excellent reliability and validity, and the variance inflation factors show no meaningful multicollinearity among the predictors (see Appendix B, Table A1 and Table A2). For context, China’s public administration follows a five-tier hierarchy comprising the central, provincial, municipal, county, and township levels. This structure underpins the bureaucratic echelon variable used in the analyses and informs the stratified comparisons reported later.
Digital red tape. Digital red tape was measured using a validated 12-item Digital Red Tape Scale [72]. The instrument builds on Van Loon et al. (2016)’s Job-Centered Red Tape Scale [73] and includes two dimensions: digital compliance burden (7 items) and digital functionality deficiency (5 items). The scale has been validated through standard scale-development procedures in multiple samples from Chinese public organizations. Cronbach’s alpha in this study was 0.92 for the full scale, 0.91 for digital compliance burden, and 0.84 for digital functionality deficiency.
Formalization. Organizational formalization was measured using items adapted from DeHart-Davis et al. (2013, 2014) [74,75] and Kaufmann et al. (2019) [47]: “In my organization, how many of the rules can be described as written rules?” Response options ranged from None to All (None, Very few, Some, Many, All). Although concise, this item has been repeatedly validated as an effective proxy for rule codification in related studies. The item was translated and back-translated following standard procedures.
Centralization. Organizational centralization was measured with three items adapted from Aiken and Hage (1968) [45], Kaufmann et al. (2019) [47] (e.g., “Even for small matters, I must seek my supervisor’s approval”), rated on a seven-point scale (Cronbach’s α = 0.87), also translated and back-translated for use in the Chinese context.
Control variables. Gender, age, education, job level, years in service, region, and bureaucratic echelon were included as control variables. In particular, the bureaucratic echelon was coded into five ordered categories (township, county, municipal, provincial, and central) for descriptive statistics. For stratified analyses and coefficient plots, these categories were collapsed into three administrative tiers: lower (township and county), middle (municipal), and upper (provincial and central) to ensure adequate cell sizes and interpretability.
Table 1. Sample characteristics (n = 452).
Table 1. Sample characteristics (n = 452).
VariableCategorynPercent
GenderFemale26759.10%
Male18540.90%
Age21–3024654.40%
31–4014933%
41–50388.40%
51–60184%
>6010.20%
EducationJunior college or below235.10%
Undergraduate28162.20%
Postgraduate14832.70%
Bureaucratic echelonTownship level7115.70%
County level17739.20%
Municipal level16035.40%
Provincial level368%
Central level81.80%
Job levelPrincipal leader40.90%
Middle manager5311.70%
Frontline manager12627.90%
Frontline staff26959.50%
Years in service<1 years368%
1–3 years14732.50%
4–6 years8819.50%
7–10 years8017.70%
11–15 years4910.80%
16–20 years163.50%
>21 years368%
RegionEastern region23451.80%
Central region10022.10%
Western region8619%
Northeastern region327.10%
Source: Authors’ two-wave survey of public employees in China.
Table 2. Measurements.
Table 2. Measurements.
ConstructSourceSubdimensionSample Item *Wave
Digital red tapeVan Loon et al. (2016) [73]Digital compliance burdenImplementation of new digital systems consumes excessive work time1
Digital functionality deficiencyTechnical support is timely and effective (R)
FormalizationDeHart-Davis et al. (2013) [74], DeHart-Davis et al. (2014) [75], Kaufmann et al. (2019) [47] In my organization, how many of the rules can be described as written rules?
(Response options: None, Very few, Some, Many, All)		2
CentralizationAiken and Hage (1968) [45], Kaufmann et al. (2019) [47] In my organization, before doing anything, I almost always have to ask my supervisor for instructions.2
* Please see Appendix A for the full list of items.

3.3. Analytical Approach

The analysis proceeded in two main stages to examine both overall patterns and administrative tier variations in digital red tape. First, one-way ANOVA and Tukey–Kramer post hoc tests were conducted to assess group differences in digital red tape and its two dimensions, digital compliance burden and digital functionality deficiency, across demographic and organizational categories. Second, ordinary least squares (OLS) regression models with HC3 robust standard errors were estimated to evaluate how formalization and centralization are associated with digital red tape, controlling for gender, age, education, years in service, bureaucratic echelon, job level, and region. To assess hierarchical heterogeneity, the models were re-estimated separately for upper-echelon (central and provincial), middle-echelon (municipal), and lower-echelon (county and township) organizations. This comparison will illustrate how the relationships between organizational structure and digital red tape vary across hierarchical levels within China’s multilevel administrative system. It is important to note that we were not able to collect organizational identifiers, which prevents us from formally modeling potential clustering of respondents within the same organizations. Additionally, because the upper-echelon subsample is small (n = 44), we were unable to conduct multi-group CFA to assess measurement invariance across echelons. All OLS models use HC3 robust standard errors to mitigate small-sample bias in variance estimation, and these methodological constraints are further discussed in the limitations.

4. Results

The reliability and validity of all multi-item constructs were examined prior to hypothesis testing. Cronbach’s alpha coefficients ranged from 0.84 to 0.92, exceeding the 0.70 benchmark, indicating satisfactory internal consistency. Composite reliability (CR) values ranged from 0.84 to 0.91, and average variance extracted (AVE) values ranged from 0.48 to 0.71, meeting the recommended cut-off values for convergent validity [76]. A confirmatory factor analysis (CFA) further demonstrated good measurement properties for the three-factor model of digital compliance burden, digital functionality deficiency, and centralization (χ2/df = 2.16, CFI = 0.974, TLI = 0.969, RMSEA = 0.051, SRMR = 0.032). These results confirm that the measurement model achieved satisfactory reliability and validity. The detailed reliability and validity results (Cronbach’s α, AVE, and CR) are provided in Appendix B.
Table 3 presents the means, standard deviations, and correlations among the study variables (n = 452). Digital red tape (Mean = 2.91, SD = 0.83) is positively correlated with both digital compliance burden (r = 0.78, p < 0.001) and digital functionality deficiency (r = 0.52, p < 0.001), confirming their conceptual relatedness yet empirical distinctiveness. Centralization (r = 0.37, p < 0.001) and formalization (r = 0.21, p < 0.001) are also positively correlated with digital red tape, while correlations among the control variables remain moderate, suggesting no multicollinearity concerns.
Table 3. Means, standard deviations, minimum, maximum, and correlations (n = 452).
Table 3. Means, standard deviations, minimum, maximum, and correlations (n = 452).
VariableMeanSDMinMax123456789101112
1Digital red tape2.910.8331.1674.751
2Digital compliance burden2.7330.90314.750.780 ***1
3Digital functionality deficiency2.110.66114.50.524 ***0.502 ***1
4Gender0.4090.49201−0.049−0.028−0.0061
5Age1.6260.81815−0.171 ***−0.189 ***−0.161 ***0.139 **1
6Education2.2770.55130.0680.0770.114 *−0.100 *−0.169 ***1
7Years in service3.3341.65617−0.152 **−0.171 ***−0.151 **0.115 *0.867 ***−0.201 ***1
8Bureaucratic echelon2.4090.90715−0.206 ***−0.145 **−0.081 +0.0310.0030.213 ***0.0391
9Job level3.460.733140.114 *0.138 **0.145 **−0.161 ***−0.471 ***−0.003−0.476 ***−0.110 *1
10Region1.8140.979140.0180.0260.019−0.0210.051−0.0440.0370.033−0.011
11Centralization2.9571.06150.371 ***0.323 ***0.266 ***−0.02−0.088 +0.067−0.135 **−0.109 *0.125 **−0.0561
12Formalization3.5730.73150.207 ***0.155 ***0.155 ***0.0180.0180.0350.0010.084 +0.0410.0280.312 ***1
Notes: Below-diagonal cells show Pearson correlations with significance (+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001). Source: Authors’ two-wave survey of public employees in China.

4.1. Group Differences in Digital Red Tape

The ANOVA (see Appendix C) and Tukey–Kramer post hoc tests revealed significant group differences in digital red tape and its two dimensions (Table 4). Regarding overall digital red tape, employees aged 31–40 and 41–50 reported significantly lower perceptions than those aged 21–30, suggesting that younger public servants tend to feel more constrained by digital procedures. Moreover, significant differences appeared across bureaucratic echelon. Digital red tape was higher among township and county governments than at the municipal and provincial levels, indicating that bureaucratic obstacles are more salient in lower administrative tiers.
For digital compliance burden, similar age-related patterns emerged. Employees in their thirties and forties experienced lower compliance burdens than the youngest group. Additionally, those with 7–10 years of service reported lower burdens than newcomers, implying that organizational experience helps employees adapt to digital procedures. Job position also mattered: frontline staff perceived higher digital compliance burdens than middle managers, with a gap of around 0.35 points that reflects meaningful positional disparities in exposure to digital administrative requirements.
Finally, for digital functionality deficiency, significant differences appeared by both age and job level. Middle managers perceived fewer digital functionality problems than frontline staff, with an average gap of about 0.31 points, consistent with their greater access to organizational support and technical resources
Overall, the significant group differences cluster around a few core characteristics. Younger employees consistently report higher levels of digital red tape and its subdimensions. Staff in township and county governments also perceive markedly higher burdens than those in municipal and provincial units, reflecting stratification across the bureaucratic echelon. Frontline employees show a similar pattern, reporting more compliance and functionality problems than middle managers. Other characteristics, including education, region, and years in service, display only limited or context-specific differences. Taken together, these patterns suggest that age, bureaucratic echelon, and job position are the categories in which perceptions of digital red tape diverge most clearly in our data.

4.2. Structural Effects on Digital Red Tape

Across the full sample, both formalization and centralization exerted positive and significant effects on digital red tape (Table 5). Higher levels of rule codification were associated with stronger perceptions of procedural burden and inefficiency, indicating that rigidly written rules tend to amplify digital constraints. Formalization showed significant effects on the overall index (B = 0.150, p < 0.01) and on digital functionality deficiency (B = 0.082, p < 0.05), suggesting that excessive procedural specification may reduce the adaptability of digital systems. These results support H1, which posited a positive relationship between organizational formalization and digital red tape. Centralization displayed an even more robust and consistent pattern across all models (p < 0.001), confirming that hierarchical decision concentration increases employees’ sense of digital red tape by limiting discretion in digital workflows. This finding strongly supports H2, demonstrating that centralization functions as a dominant structural driver of digital red tape. Among the control variables, younger and less educated employees reported higher levels of digital red tape, while those working in lower bureaucratic echelons tended to experience greater digital compliance burden.
To assess whether the structural effects vary across bureaucratic echelons in H3, we first estimated multiplicative interaction models, but the interaction terms were not statistically significant (Appendix D, Table A3). A coefficient plot is also provided for transparency (Appendix D, Figure A1). However, some methodological work cautions that such non-significance should not be taken as evidence of no heterogeneity, because interaction estimates are often fragile under strong functional-form assumptions, limited common support, and sensitivity to model specification [77,78]. In our data, the upper echelon contains only 44 observations, further limiting the power to detect interaction effects. Following these cautions, we therefore supplement the interaction models with separate OLS regressions estimated for upper, middle, and lower echelons (Table 6).
The echelon-specific regressions indicate different patterns across bureaucratic echelons. In upper-echelon organizations (n = 44), formalization is not associated with digital red tape, whereas centralization shows a positive and statistically significant coefficient for the overall index. In middle-echelon organizations (n = 160), both formalization and centralization are positively associated with digital red tape across all three outcomes, with centralization exhibiting comparatively larger standardized coefficients. In lower-echelon organizations (n = 248), centralization remains positive and significant, while formalization shows a modest association for the overall index but is not statistically significant for the two subdimensions. These patterns, which are also reflected in Figure 1 and Figure 2, indicate variation in the estimated magnitude of structural effects across bureaucratic echelons even though the formal interaction test did not reach statistical significance. Taken together, the results provide descriptive rather than confirmatory evidence of heterogeneous structural effects and offer partial support for H3.
Overall, the results offer clear evidence that both formalization and centralization are positively associated with digital red tape in Chinese public organizations, providing full support for H1 and H2. The additional heterogeneity analysis yields a more nuanced picture. Although the formal interaction test does not yield statistical significance, the echelon-specific regressions and coefficient plots indicate differences in the estimated magnitudes of structural effects. In middle-echelon organizations, both formalization and centralization are positively associated with all three outcomes, with centralization showing comparatively larger standardized coefficients. In lower-echelon organizations, centralization remains positive and significant across outcomes, whereas formalization shows only a modest association with the overall index. These descriptive patterns point to possible variation across echelons. Thus, the findings offer descriptive rather than statistical support for H3, indicating only partial support for the proposed heterogeneity.

5. Discussion

This study makes three theoretical contributions. First, ANOVA and Tukey–Kramer post hoc tests reveal that significant differences in perceived digital red tape are concentrated among younger employees, frontline staff, and those working in lower-level governments, rather than dispersed across many background characteristics. This indicates a patterned rather than scattered structure of variation in how employees experience digital constraints. Younger and less experienced staff reported significantly higher perceptions of digital constraints, which perhaps reflects their limited tenure and position in the organizational hierarchy, making them more sensitive to procedural rigidity. Employees in lower job positions also perceived stronger digital compliance burden and digital functionality deficiency than those in managerial roles. Such disproportionate burdens on frontline and early-career employees may undermine two foundations of sustainable digital development: employee well-being and the capacity for continuous innovation [79,80,81]. These findings are consistent with prior evidence that red tape perceptions vary across organizational echelons [21,22]. Extending this logic to a non-WEIRD (western, educated, industrialized, rich, and democratic) setting [82], the results indicate that hierarchical variation in bureaucratic experience persists in the digital era. Such intra-organizational differences provide a micro-level foundation for understanding how bureaucratic hierarchy more broadly shapes digital red tape.
Second, significant differences also emerge across bureaucratic echelons within China’s administrative hierarchy. Employees in lower-echelon organizations reported higher levels of digital red tape than those in upper echelons. This pattern reflects the cascading nature of control in the Chinese bureaucracy, where lower-level organizations face tighter supervision, heavier reporting requirements, and fewer opportunities for discretion. This result provides a context-specific contribution by showing that perceptions of digital red tape in China are shaped by a vertically stratified bureaucratic system in which hierarchical distance from decision-making centers amplifies feelings of rigidity and burden. This pattern is consistent with Zhou and Lian’s (2020) [66] control-rights model of Chinese bureaucracy, which depicts authority cascading downward through principal-supervisor-agent relationships. Within such a system, middle and lower levels face dual pressures of implementing upper-level mandates and enduring intensified digital supervision, making them most susceptible to the burdens of digital red tape. Digital procedures that exhibit functionality deficiencies or impose excessive compliance burdens can reduce service responsiveness and erode public trust, thereby undermining the long-term sustainability of digital transformation [4,83]. Taken together, these results demonstrate that digital red tape arises from the interaction between hierarchical control and technological embeddedness, reflecting a socio-technical tension in which the distribution of authority shapes how digital systems are perceived and experienced.
Third, the structural analysis identifies centralization as the primary organizational source of digital red tape, whereas the influence of formalization is weaker and more context-dependent. Across all models, higher levels of centralization consistently heighten perceptions of both compliance burden and functionality deficiency. The echelon-specific regression models also provide descriptive indications that these associations may vary across hierarchical levels. Although such patterns should be interpreted cautiously given the non-significant interaction terms and the small upper-echelon subsample. These findings offer theoretical insight into how structural features shape employees’ experiences with digital procedures. When decision-making authority is concentrated at upper levels, frontline and lower-echelon employees have limited discretion to interpret or adapt digital processes, which makes digital oversight feel rigid. The role of formalization is weaker and less consistent, but the descriptive patterns suggest why its effects may be experienced differently across hierarchical layers. Middle echelons often occupy the intersection of upward compliance and downward supervision. This dual position requires them to translate formalized rules from upper levels while also enforcing them on frontline staff, which may make the constraining aspects of formalization more visible in this layer even if the statistical interaction terms are not significant.
Moreover, codified and standardized rules do not inherently generate digital red tape. Their negative effects arise mainly in settings where digital supervision and bureaucratic control intersect most intensely, aligning with Adler and Borys (1996)’s distinction between enabling and coercive bureaucracies [50]. This perspective also helps clarify why digital rules that are essential for safeguarding accountability, cybersecurity and privacy may still feel restrictive when embedded in coercive, highly centralized structures, whereas the same rules can function as supportive coordination mechanisms in more enabling environments. At the same time, structural features such as formalization, centralization and hierarchy also serve essential governance purposes by promoting accountability, transparency and predictability, even when they create procedural burdens [47]. Digital red tape therefore represents only one dimension of organizational functioning and should be viewed within the broader trade-off between control and flexibility.
The findings also offer several practical implications for sustainable digital transformation from the perspective of digital red tape. First, because perceptions of digital red tape, including functionality deficiencies and excessive compliance burdens, are concentrated among frontline and lower-level employees rather than spread evenly across the bureaucracy, public organizations need to monitor how digital procedures affect these groups in order to maintain the long-term sustainability of digital transformation. Second, reducing unnecessary digital constraints is essential for building a more resilient and adaptable digital governance system. Excessive centralization may harden digital procedures and limit the discretion of middle and lower-level units, suggesting the value of greater localized configuration rights, periodic reviews of outdated or duplicative digital rules, and more flexible workflow arrangements. Third, the results indicate that public organizations need to differentiate digital rules that provide essential safeguards for cybersecurity, privacy, and accountability from those that impose avoidable burdens or exhibit functionality deficiencies, including repetitive reporting requirements or systems that are difficult to operate and therefore encourage workarounds. Clearer distinctions between these two types of rules can help managers retain necessary controls while preventing the accumulation of harmful digital red tape. Finally, incorporating user co-creation, frontline participation, and iterative feedback into system design, together with balancing quantitative digital indicators with qualitative assessments, can help public organizations reduce the build-up of coercive digital red tape and maintain a more sustainable trajectory for digital transformation.
Despite these insights, several limitations should be acknowledged. First, although the two-wave design helps reduce simultaneity concerns and provides preliminary temporal leverage, the short interval between waves limits the ability to capture the dynamic evolution of digital red tape. Second, because the data rely on self-reported measures for both independent and dependent variables, common source bias cannot be entirely ruled out [43,84]. Third, the absence of organizational identifiers prevents the use of multilevel modeling to account for between-organization variance; future research with such identifiers would enable random-intercept or random-slope models to more precisely examine structural effects across organizations. Fourth, the interaction terms in the multiplicative interaction models are not statistically significant. Given the small upper-echelon subsample and the sensitivity of interaction models to specification, the heterogeneity we observe should be treated as descriptive rather than definitive. Future research should more directly examine the downstream consequences of digital red tape for innovation, employee well-being, public trust and the long-term sustainability of digital transformation, using multi-source or administrative data and cross-national, multilevel or longitudinal designs to strengthen causal inference and assess the generalizability of these relationships.

Author Contributions

Conceptualization, J.L., S.H. and L.W.; Methodology, J.L., S.H. and L.W.; Formal Analysis, J.L.; Validation, J.L.; Writing, Original Draft Preparation, J.L.; Review and Editing, S.H., Y.J. and L.W.; Supervision, S.H. and L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Project of the National Social Science Fund of China, grant number 23AZD035.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the School of Public Affairs, Zhejiang University.

Informed Consent Statement

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

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Survey Items

English Items
Digital red tape (Van Loon et al., 2016 [73]) (Cronbach’s Alpha = 0.9162)
Based on your experiences in work environment
1Implementation of new digital systems consumes excessive work time
2Multiple digital systems increase my workload
3Mastering new digital systems requires substantial time and effort
4Frequent switching between digital platforms increases my work burden
5Data collection and entry in digital systems require considerable time and effort
6Fragmented push notifications cause information overload and reduce efficiency
7Including digital system-usage frequency in evaluations increases my work pressure
8Technical support is timely and effective (R)
9Digital system interfaces are user-friendly and modern (R)
10Digital system operates reliably without affecting efficiency (R)
11Digital system functions match actual my work needs (R)
12Digital system provides quality data with effective processing capabilities (R)
Formalization ( DeHart-Davis et al., 2013 [74]; DeHart-Davis et al., 2014 [75]; Kaufmann et al., 2019 [47])
1In my organization, how many of the rules can be described as written rules?
(Response options: None, Very few, Some, Many, All)
Centralization ( Aiken and Hage, 1968 [45]; Kaufmann et al., 2019 [47]) (Cronbach’s Alpha = 0.8728 )
In my organization.
1Before doing anything, I almost always have to ask my supervisor for instructions.
2Even for minor matters, I must seek my supervisor’s approval before a final decision can be made.
3In general, employees who try to make decisions on their own are quickly discouraged.

Appendix B. Reliability, Validity and Multicollinearity

Table A1. Reliability and validity.
Table A1. Reliability and validity.
ConstructItemsAlphaAVECR
Digital red tape120.9160.4780.914
Digital compliance burden70.9130.6020.913
Digital functionality deficiency50.8410.5210.844
Centralization30.8730.7070.877
Notes: Alpha is Cronbach’s alpha; AVE = average variance extracted; CR = composite reliability. Source: Authors’ two-wave survey of public employees in China.
Table A2. Variance Inflation Factors (VIF).
Table A2. Variance Inflation Factors (VIF).
VariableVIF
Age4.159
Years in service4.259
Formalization1.132
Centralization1.167
Gender1.045
Education1.128
Bureaucratic echelon1.101
Job level1.374
Region1.012

Appendix C. Group Differences (t/ANOVA)

Dependent VariableGrouping VariableMethodContrastStatisticF ValueMean Diffp-ValueSigGroups
Digital red tapeGendert-testMale-Female1.04 −0.0830.299 2
AgeANOVA 4.915 0.0007***5
EducationANOVA 1.103 0.3327 3
Years in serviceANOVA 2.286 0.0348*7
Bureaucratic echelonANOVA 6.308 0.0001***5
Job levelANOVA 2.01 0.1117 4
RegionANOVA 0.426 0.7347 4
Digital compliance burdenGendert-testMale-Female0.588 −0.0510.557 2
AgeANOVA 5.576 0.0002***5
EducationANOVA 1.555 0.2124 3
Years in serviceANOVA 2.862 0.0096**7
Bureaucratic echelonANOVA 3.89 0.0041**5
Job levelANOVA 3.118 0.0259*4
RegionANOVA 0.476 0.699 4
Digital functionality deficiencyGendert-testMale-Female0.122 −0.0080.9033 2
AgeANOVA 3.909 0.0039**5
EducationANOVA 3.01 0.0503+3
Years in serviceANOVA 1.945 0.0722+7
Bureaucratic echelonANOVA 0.741 0.5642 5
Job levelANOVA 3.689 0.012*4
RegionANOVA 0.113 0.9525 4
Notes: t-test for two groups; one-way ANOVA for ≥3 groups. Significance levels: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: Authors’ two-wave survey of public employees in China.

Appendix D. Interaction Models and Diagnostic Plots for Structural Effects Across Bureaucratic Echelons

Table A3. Interaction models for structural effects across bureaucratic echelons.
Table A3. Interaction models for structural effects across bureaucratic echelons.
PredictorDigital Red TapeDigital Compliance BurdenDigital Functionality Deficiency
Formalization (lower echelons)0.139 * (0.069)0.064 (0.078)0.047 (0.059)
Formalization × middle echelons0.058 (0.108)0.128 (0.121)0.102 (0.091)
Formalization × upper echelons−0.165 (0.190)−0.134 (0.213)−0.074 (0.160)
Centralization (lower echelons)0.182 *** (0.048)0.188 *** (0.054)0.083 * (0.041)
Centralization × middle echelons0.103 (0.076)0.097 (0.085)0.111 + (0.064)
Centralization × upper echelons0.153 (0.124)0.034 (0.139)0.096 (0.105)
Joint test: Formalization × echelonsF = 0.669, p = 0.5129F = 0.959, p = 0.3840F = 0.903, p = 0.4063
Joint test: Centralization × echelonsF = 1.349, p = 0.2606F = 0.650, p = 0.5225F = 1.619, p = 0.1992
ControlsYesYesYes
R20.2290.1760.128
Adjusted R20.2040.150.1
N452452452
Notes: Reference group is lower tier. Controls include gender, age, education, years in service, job level, and region. Significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: Authors’ two-wave survey of public employees in China.
Sustainability 17 10681 g0a1
Figure A1. Visual diagnostics from interaction models across bureaucratic echelons.
Figure A1. Visual diagnostics from interaction models across bureaucratic echelons.
Sustainability 17 10681 g0a1

References

  1. Lips, M. Digital Government: Managing Public Sector Reform in the Digital Era; Routledge: London, UK, 2019; pp. 4–5. [Google Scholar]
  2. Tassabehji, R.; Hackney, R.; Popovič, A. Emergent digital era governance: Enacting the role of the ‘institutional entrepreneur’ in transformational change. Gov. Inf. Q. 2016, 33, 223–236. [Google Scholar] [CrossRef]
  3. Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst. 2019, 28, 118–144. [Google Scholar] [CrossRef]
  4. 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]
  5. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2019, 36, 101385. [Google Scholar] [CrossRef]
  6. Palmi, P.; Corallo, A.; Prete, M.I.; Harris, P. Balancing exploration and exploitation in public management: Proposal for an organizational model. J. Public Aff. 2021, 21, e2245. [Google Scholar] [CrossRef]
  7. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015. Available online: https://sdgs.un.org/2030agenda (accessed on 13 November 2025).
  8. European Commission. Europe’s Digital Decade: Digital Targets for 2030. Available online: https://digital-strategy.ec.europa.eu/en/library/2023-report-state-digital-decade (accessed on 13 November 2025).
  9. Marques, I.; Leitão, J.; Carvalho, A.; Pereira, D. Public administration and values oriented to sustainability: A systematic approach to the literature. Sustainability 2021, 13, 2566. [Google Scholar] [CrossRef]
  10. Sigurjonsson, T.O.; Jónsson, E.; Gudmundsdottir, S. Sustainability of digital initiatives in public services in digital transformation of local government: Insights and Implications. Sustainability 2024, 16, 10827. [Google Scholar] [CrossRef]
  11. Bauwens, R.; Meyfroodt, K. Debate: Towards a more comprehensive understanding of ritualized bureaucracy in digitalized public organizations. Public Money Manag. 2021, 41, 281–282. [Google Scholar] [CrossRef]
  12. Boyne, G.A. Public and private management: What’s the difference? J. Manag. Stud. 2002, 39, 97–122. [Google Scholar] [CrossRef]
  13. Blom, R.; Borst, R.T.; Voorn, B. Pathology or inconvenience? A meta-analysis of the impact of red tape on people and organizations. Rev. Public Pers. Adm. 2021, 41, 623–650. [Google Scholar] [CrossRef]
  14. DeHart-Davis, L. Red tape and public employees: Does perceived rule dysfunction alienate managers? J. Public Adm. Res. Theory 2005, 15, 133–148. [Google Scholar] [CrossRef]
  15. Chen, C.-A.; Bozeman, B. Organizational risk aversion: Comparing the public and non-profit sectors. Public Manag. Rev. 2012, 14, 377–402. [Google Scholar] [CrossRef]
  16. Hattke, F.; Hensel, D.; Kalucza, J. Emotional responses to bureaucratic red tape. Public Adm. Rev. 2020, 80, 53–63. [Google Scholar] [CrossRef]
  17. Muylaert, J.; Decramer, A.; Audenaert, M. Linking red tape originating from digital tools to affective commitment: The mediating roles of role ambiguity and work engagement. Public Manag. Rev. 2023, 25, 2402–2427. [Google Scholar] [CrossRef]
  18. Muylaert, J.; Decramer, A.; Audenaert, M. How leader’s red tape interacts with employees’ red tape from the lens of the job demands-resources model. Rev. Public Pers. Adm. 2023, 43, 430–455. [Google Scholar] [CrossRef]
  19. Bozeman, B.; Youtie, J. Robotic bureaucracy: Administrative burden and red tape in university research. Public Adm. Rev. 2020, 80, 157–162. [Google Scholar] [CrossRef]
  20. Rajala, T. New development: Red tape and digital accounting technology in the public sector. Public Money Manag. 2025, 1–6. [Google Scholar] [CrossRef]
  21. Walker, R.M.; Brewer, G.A. An organizational echelon analysis of the determinants of red tape in public organizations. Public Adm. Rev. 2008, 68, 1112–1127. [Google Scholar] [CrossRef]
  22. Yang, Y. Beyond Red tape: An organizational echelon analysis of necessary bureaucracy. Public Perform. Manag. Rev. 2023, 46, 1145–1179. [Google Scholar] [CrossRef]
  23. Bozeman, B. A theory of government “red tape”. J. Public Adm. Res. Theory 1993, 3, 3273–3304. [Google Scholar] [CrossRef]
  24. Pandey, S.K.; Pandey, S.; Van Ryzin, G.G. Prospects for Experimental Approaches to Research on Bureaucratic Red Tape. In Experiments in Public Management Research: Challenges and Opportunities; OJames, O., Jilke, S., Van Ryzin, G., Eds.; Cambridge University Press: New York, NY, USA, 2017; pp. 219–243. [Google Scholar]
  25. Bozeman, B.; Feeney, M.K. Rules and Red Tape: A Prism for Public Administration Theory and Research; Routledge: London, UK, 2015. [Google Scholar]
  26. Zahradnik, S. Red tape: Redefinition and reconceptualization based on production theory. Int. Public Manag. J. 2022, 27, 343–362. [Google Scholar] [CrossRef]
  27. Herd, P.; DeLeire, T.; Harvey, H.; Moynihan, D.P. Shifting administrative burden to the state: The case of medicaid take-up. Public Adm. Rev. 2013, 73, S69–S81. [Google Scholar] [CrossRef]
  28. Moynihan, D.; Herd, P.; Harvey, H. Administrative burden: Learning, psychological, and compliance costs in citizen-state interactions. J. Public Adm. Res. Theory 2015, 25, 43–69. [Google Scholar] [CrossRef]
  29. Madsen, J.K.; Mikkelsen, K.S.; Moynihan, D.P. Burdens, sludge, ordeals, red tape, oh my!: A user’s guide to the study of frictions. Public Adm. 2022, 100, 375–393. [Google Scholar] [CrossRef]
  30. Vogl, T.M.; Seidelin, C.; Ganesh, B.; Bright, J. Algorithmic Bureaucracy: Managing Competence, Complexity, and Problem Solving in the Age of Artificial Intelligence. SSRN Electron. J. 2019, 148–153. [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. Bozeman, B.; Youtie, J.; Jung, J. Robotic bureaucracy and administrative burden: What are the effects of universities’ computer automated research grants management systems? Res. Policy 2020, 49, 103980. [Google Scholar] [CrossRef]
  33. Moon, M.J.; Bretschneiber, S. Does the perception of red tape constrain IT innovativeness in organizations? Unexpected results from a simultaneous equation model and implications. J. Public Adm. Res. Theory 2002, 12, 273–292. [Google Scholar] [CrossRef]
  34. Welch, E.W.; Pandey, S.K. E-government and bureaucracy: Toward a better understanding of intranet implementation and its effect on red tape. J. Public Adm. Res. Theory 2007, 17, 379–404. [Google Scholar] [CrossRef]
  35. Kim, S.; Paik, W.; Lee, C. Does bureaucracy facilitate the effect of information technology (IT)? Int. Rev. Public Adm. 2014, 19, 219–237. [Google Scholar] [CrossRef]
  36. Bretschneider, S. Management information systems in public and private organizations: An empirical test. Public Adm. Rev. 1990, 50, 536–545. [Google Scholar] [CrossRef]
  37. Pandey, S.K.; Bretschneider, S.I. The impact of red tape’s administrative delay on public organizations’ interest in new information technologies. J. Public Adm. Res. Theory 1997, 7, 113–130. [Google Scholar] [CrossRef]
  38. Heintze, T.; Bretschneider, S. Information technology and restructuring in public organizations: Does adoption of information technology affect organizational structures, communications, and decision making? J. Public Adm. Res. Theory 2000, 10, 801–830. [Google Scholar] [CrossRef]
  39. Ahn, M.J.; Bretschneider, S. Politics of e-government: E-government and the political control of bureaucracy. Public Adm. Rev. 2011, 71, 414–424. [Google Scholar] [CrossRef]
  40. Meijer, A.J. E-mail in government: Not post-bureaucratic but late-bureaucratic organizations. Gov. Inf. Q. 2008, 25, 429–447. [Google Scholar] [CrossRef]
  41. Peled, A. Do computers cut red tape? Am. Rev. Public Adm. 2001, 31, 414–435. [Google Scholar] [CrossRef]
  42. Armstrong, M.; Taylor, S. Armstrong’s Handbook of Human Resource Management Practice, 13th ed.; Kogan Page: London, UK, 2014. [Google Scholar]
  43. Jacobsen, C.B.; Jakobsen, M.L. Perceived organizational red tape and organizational performance in public services. Public Adm. Rev. 2018, 78, 24–36. [Google Scholar] [CrossRef]
  44. Van Tulder, R.; van Mil, E. Principles of Sustainable Business: Frameworks for Corporate Action on the SDGs, 1st ed.; Routledge: London, UK, 2022. [Google Scholar] [CrossRef]
  45. Aiken, M.; Hage, J. Organizational alienation: A comparative analysis. Am. Sociol. Rev. 1966, 31, 497–507. [Google Scholar] [CrossRef]
  46. Pugh, D.S.; Hickson, D.J.; Hinings, C.R.; Turner, C. Dimensions of organization structure. Adm. Sci. Q. 1968, 13, 65–105. [Google Scholar] [CrossRef]
  47. Kaufmann, W.; Borry, E.L.; DeHart-Davis, L. More than pathological formalization: Understanding organizational structure and red tape. Public Adm. Rev. 2019, 79, 236–245. [Google Scholar] [CrossRef]
  48. Orlikowski, W.J. The duality of technology: Rethinking the concept of technology in organizations. Organ. Sci. 1992, 3, 398–427. [Google Scholar] [CrossRef]
  49. 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. 2014, 31, 545–555. [Google Scholar] [CrossRef]
  50. Adler, P.S.; Borys, B. Two types of bureaucracy: Enabling and coercive. Adm. Sci. Q. 1996, 41, 61–89. [Google Scholar] [CrossRef]
  51. Pandey, S.K.; Kingsley, G.A. Examining red tape in public and private organizations: Alternative explanations from a social psychological model. J. Public Adm. Res. Theory 2000, 10, 779–800. [Google Scholar] [CrossRef]
  52. Kaufmann, W.; Feeney, M.K. Beyond the rules: The effect of outcome favourability on red tape perceptions. Public Adm. 2014, 92, 178–191. [Google Scholar] [CrossRef]
  53. Volkoff, O.; Strong, D.M.; Elmes, M.B. Technological embeddedness and organizational change. Organ. Sci. 2007, 18, 832–848. [Google Scholar] [CrossRef]
  54. Peeters, R.; Widlak, A. The digital cage: Administrative exclusion through information architecture–The case of the Dutch civil registry’s master data management system. Gov. Inf. Q. 2018, 35, 175–183. [Google Scholar] [CrossRef]
  55. Pandey, S.K.; Rainey, H.G. Public managers’ perceptions of organizational goal ambiguity: Analyzing alternative models. Int. Public Manag. J. 2006, 9, 85–112. [Google Scholar] [CrossRef]
  56. Willem, A.; Buelens, M.; De Jonghe, I. Impact of organizational structure on nurses’ job satisfaction: A questionnaire survey. Int. J. Nurs. Stud. 2007, 44, 1011–1020. [Google Scholar] [CrossRef]
  57. Brewer, G.A.; Walker, R.M.; Bozeman, B.; Avellaneda, C.N.; Brewer, G.A., Jr. External control and red tape: The mediating effects of client and organizational feedback. Int. Public Manag. J. 2012, 15, 288–314. [Google Scholar] [CrossRef]
  58. Mustafa, G.; Solli-Sæther, H.; Bodolica, V.; Håvold, J.I.; Ilyas, A. Digitalization trends and organizational structure: Bureaucracy, ambidexterity or post-bureaucracy? Eurasian Bus. Rev. 2022, 12, 671–694. [Google Scholar] [CrossRef]
  59. Bovens, M.; Zouridis, S. From Street-Level to System-Level Bureaucracies: How Information and Communication Technology is Transforming Administrative Discretion and Constitutional Control. Public Adm. Rev. 2002, 62, 174–184. [Google Scholar] [CrossRef]
  60. Buffat, A. Street-level bureaucracy and e-government. Public Manag. Rev. 2015, 17, 149–161. [Google Scholar] [CrossRef]
  61. Payne, R.L.; Mansfield, R. Relationships of perceptions of organizational climate to organizational structure, context, and hierarchical position. Adm. Sci. Q. 1973, 18, 515–526. [Google Scholar] [CrossRef]
  62. Brewer, G.A. In the eye of the storm: Frontline supervisors and federal agency performance. J. Public Adm. Res. Theory 2005, 15, 505–527. [Google Scholar] [CrossRef]
  63. Walker, R.M.; Enticott, G. Using multiple informants in public administration: Revisiting the managerial values and actions debate. J. Public Adm. Res. Theory 2004, 14, 417–434. [Google Scholar] [CrossRef]
  64. State Council of China. Guiding Opinions on Actively Promoting the “Internet Plus” Action Plan. 2015. Available online: https://www.gov.cn/zhengce/zhengceku/2015-07/04/content_10002.htm (accessed on 13 November 2025).
  65. Cyberspace Administration of China. Digital China Development Report. 2023. Available online: https://www.szzg.gov.cn/2024/xwzx/szkx/202406/P020240630600725771219.pdf (accessed on 13 November 2025).
  66. Zhou, X.; Lian, H. Modes of governance in the Chinese bureaucracy: A “control rights” theory. China J. 2020, 84, 51–75. [Google Scholar] [CrossRef]
  67. Zhou, X. Chinese bureaucracy through three lenses: Weberian, Confucian, and Marchian. Manag. Organ. Rev. 2021, 17, 655–682. [Google Scholar] [CrossRef]
  68. Jie, G. Political rationality vs. technical rationality in China’s target-based performance measurement system: The case of social stability maintenance. Policy Soc. 2015, 34, 37–48. [Google Scholar] [CrossRef]
  69. Burns, J.P.; Zhiren, Z. Performance management in the government of the People’s Republic of China: Accountability and control in the implementation of public policy. OECD J. Budg. 2010, 10, 7. [Google Scholar]
  70. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879. [Google Scholar] [CrossRef]
  71. Conway, J.M.; Lance, C.E. What reviewers should expect from authors regarding common method bias in organizational research. J. Bus. Psychol. 2010, 25, 325–334. [Google Scholar] [CrossRef]
  72. Liu, J.; Neumann, O.; Hu, S. Measuring digital red tape: Scale development and validation in Chinese public organizations. Public Perform. Manag. Rev. 2025; accepted. [Google Scholar]
  73. Van Loon, N.M.; Leisink, P.L.; Knies, E.; Brewer, G.A. Red tape: Developing and validating a new job-centered measure. Public Adm. Rev. 2016, 76, 662–673. [Google Scholar] [CrossRef]
  74. DeHart-Davis, L.; Chen, J.; Little, T.D. Written versus unwritten rules: The role of rule formalization in green tape. Int. Public Manag. J. 2013, 16, 331–356. [Google Scholar] [CrossRef]
  75. DeHart-Davis, L.; Davis, R.S.; Mohr, Z. Green tape and job satisfaction: Can organizational rules make employees happy? J. Public Adm. Res. Theory 2014, 25, 849–876. [Google Scholar] [CrossRef]
  76. 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]
  77. Hainmueller, J.; Mummolo, J.; Xu, Y. How much should we trust estimates from multiplicative interaction models? Simple tools to improve empirical practice. Political Anal. 2019, 27, 163–192. [Google Scholar] [CrossRef]
  78. Helm, R.; Mark, A. Analysis and evaluation of moderator effects in regression models: State of art, alternatives and empirical example. Rev. Manag. Sci. 2012, 6, 307–332. [Google Scholar] [CrossRef]
  79. Bican, P.M.; Brem, A. Digital business model, digital transformation, digital entrepreneurship: Is there a sustainable “digital”? Sustainability 2020, 12, 5239. [Google Scholar] [CrossRef]
  80. Guandalini, I. Sustainability through digital transformation: A systematic literature review for research guidance. J. Bus. Res. 2022, 148, 456–471. [Google Scholar] [CrossRef]
  81. Vitálišová, K.; Sýkorová, K.; Koróny, S.; Laco, P.; Vaňová, A.; Borseková, K. Digital transformation in local municipalities: Theory versus practice. In Participatory and Digital Democracy at the Local Level: European Discourses and Practices; Springer International Publishing: Cham, Switzerland, 2023; pp. 207–226. [Google Scholar]
  82. Henrich, J.; Heine, S.J.; Norenzayan, A. Most people are not WEIRD. Nature 2010, 466, 29. [Google Scholar] [CrossRef] [PubMed]
  83. Rupeika-Apoga, R.; Petrovska, K. Barriers to sustainable digital transformation in micro-, small-, and medium-sized enterprises. Sustainability 2022, 14, 13558. [Google Scholar] [CrossRef]
  84. Favero, N.; Bullock, J.B. How (not) to solve the problem: An evaluation of scholarly responses to common source bias. J. Public Adm. Res. Theory 2015, 25, 285–308. [Google Scholar] [CrossRef]
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Figure 1. Structural effects of centralization across bureaucratic echelons. Coefficients (B) with 95% confidence intervals are shown for upper, middle, and lower echelons.
Figure 1. Structural effects of centralization across bureaucratic echelons. Coefficients (B) with 95% confidence intervals are shown for upper, middle, and lower echelons.
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Figure 2. Structural effects of formalization across bureaucratic echelons. Coefficients (B) with 95% confidence intervals are shown for upper, middle, and lower echelons.
Figure 2. Structural effects of formalization across bureaucratic echelons. Coefficients (B) with 95% confidence intervals are shown for upper, middle, and lower echelons.
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Table 4. Tukey–Kramer post hoc tests.
Table 4. Tukey–Kramer post hoc tests.
Dependent VariableGrouping VariableContrastEstimate95% CI (Low)95% CI (High)Adj. pSig
Digital red tapeAge31–40 vs. 21–30−0.296−0.529−0.0640.00486**
41–50 vs. 21–30−0.474−0.864−0.0830.00863**
Bureaucratic echelonMunicipal vs. Township−0.381−0.699−0.0630.00975**
Municipal vs. County−0.366−0.609−0.1220.000435***
Provincial vs. County−0.417−0.824−0.0090.0423*
Digital compliance burdenAge31–40 vs. 21–30−0.32−0.572−0.0680.00492**
41–50 vs. 21–30−0.516−0.938−0.0930.00799**
Years in service7–10 years vs. <1 years−0.546−1.076−0.0150.039*
Bureaucratic echelonMunicipal vs. County−0.332−0.599−0.0660.00624**
Job levelFrontline staff vs. Middle manager0.350.0020.6980.0477*
Digital functionality deficiencyAge41–50 vs. 21–30−0.384−0.696−0.0730.0071**
Job levelFrontline staff vs. Middle manager0.3090.0550.5630.0099**
Notes: Tukey–Kramer (HSD) multiple comparisons after one-way ANOVA. Rows shown are adjusted p < 0.05. Significance: * p < 0.05, ** p < 0.01, *** p < 0.001. Source: Authors’ two-wave survey of public employees in China.
Table 5. Structural effects on digital red tape.
Table 5. Structural effects on digital red tape.
Digital Red TapeDigital Compliance BurdenDigital Functionality Deficiency
BβtBβtBβt
Formalization0.150 ** (0.054)0.1312.7790.101 + (0.059)0.0821.7190.082 * (0.041)0.0912.002
Centralization0.236 *** (0.039)0.3016.120.230 *** (0.044)0.275.2060.132 *** (0.031)0.2114.225
Gender−0.022 (0.074) −0.3040.024 (0.082) 0.2990.049 (0.063) 0.772
Age−0.236 ** (0.083) −2.848−0.0244 −2.431−0.054 −1.731
Education0.105 (0.065) 1.6070.112 (0.074) 1.5110.129 * (0.053) 2.433
Years in service0.053 (0.042) 1.2680.046 (0.051) 0.8980.027 (0.038) 0.703
Bureaucratic echelon−0.188 *** (0.040) −4.693−0.139 ** (0.046) −3.037−0.062 (0.038) −1.633
Job level−0.014 (0.060) −0.2360.029 (0.065) 0.450.062 (0.041) 1.512
Region0.042 (0.034) 1.2260.051 (0.040) 1.2830.029 (0.028) 1.018
R20.2093245 0.1570378 0.1153837
Adjusted R20.1932248 0.1398734 0.0973712
Notes: OLS with HC3 robust SEs. B cells show unstandardized coefficients with stars and SE in parentheses; β cells show standardized coefficients. Significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: Authors’ two-wave survey of public employees in China.
Table 6. Structural effect models across organizational echelons.
Table 6. Structural effect models across organizational echelons.
Upper Echelons (n = 44)Middle Echelons (n = 160)Lower Echelons (n = 248)
BβtBβtBβt
Digital red tape
Formalization−0.030 (0.179)−0.027−0.1680.214 * (0.093)0.1892.3030.139 + (0.075)0.1271.85
Centralization0.256 * (0.116)0.3572.2190.275 *** (0.069)0.3423.9870.189 *** (0.052)0.253.63
Controlscontrolledcontrolledcontrolled
R20.3455317 0.2489091 0.1548864
Adjusted R20.195939 0.2091162 0.1265981
Digital compliance burden
Formalization−0.079 (0.221)−0.067−0.3570.205 + (0.104)0.1661.970.059 (0.079)0.0490.749
Centralization0.236 (0.173)0.3081.3650.270 *** (0.080)0.3083.3840.188 ** (0.060)0.2243.133
Controlscontrolledcontrolledcontrolled
R20.1352538 0.2143538 0.1293911
Adjusted R2−0.0624025 0.1727302 0.1002493
Digital functionality deficiency
Formalization−0.038 (0.109)−0.04−0.3470.164 * (0.072)0.1832.2660.043 (0.058)0.0480.752
Centralization0.130 (0.130)0.2120.9990.185 ** (0.057)0.2923.2680.087 * (0.040)0.1392.157
Controlscontrolledcontrolledcontrolled
R20.1956691 0.2099862 0.0828571
Adjusted R20.011822 0.1681312 0.0521578
Notes: OLS with HC3 robust SEs. B cells show unstandardized coefficients with stars and SE in parentheses; β cells show standardized coefficients. Controls: Gender, age, education, years in service, job level, region. Significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: Authors’ two-wave survey of public employees in China.
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Liu, J.; Hu, S.; Jin, Y.; Weng, L. Digital Red Tape in Public Organizations: Challenges to Sustainable Digital Transformation. Sustainability 2025, 17, 10681. https://doi.org/10.3390/su172310681

AMA Style

Liu J, Hu S, Jin Y, Weng L. Digital Red Tape in Public Organizations: Challenges to Sustainable Digital Transformation. Sustainability. 2025; 17(23):10681. https://doi.org/10.3390/su172310681

Chicago/Turabian Style

Liu, Juan, Shuigen Hu, Yantong Jin, and Lieen Weng. 2025. "Digital Red Tape in Public Organizations: Challenges to Sustainable Digital Transformation" Sustainability 17, no. 23: 10681. https://doi.org/10.3390/su172310681

APA Style

Liu, J., Hu, S., Jin, Y., & Weng, L. (2025). Digital Red Tape in Public Organizations: Challenges to Sustainable Digital Transformation. Sustainability, 17(23), 10681. https://doi.org/10.3390/su172310681

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