Organizational agility requires ongoing change management, but the constant flux is not well accepted by all company members, which can lead to tensions. Specifically, for non-agile mindsets, “change” may be problematic; consequently, it might also be “change management.” This study explores how the dynamics between opposing agile and non-agile mindsets impact organizational change management and encourages discussion on how to manage the tensions between these mindsets to support ambidexterity and facilitate change adoption.
Based on a sample of 1,871 knowledge workers, divided into subsamples of agile (N = 932) and non-agile mindset professionals (N = 939), four structural equation models (SEMs) were developed to examine, step by step, how internal dynamics evolve when facilitated by an agile culture. Additionally, each step is analyzed from the perspectives of agile and non-agile professionals.
This study finds that professionals with an agile mindset see themselves as key to change management, and that non-agile colleagues view them similarly. Non-agile think they contribute positively, but agile colleagues see them as non-contributing. Perceptions of agile and non-agile mindsets are mutually exclusive, hindering collaboration. An agile culture benefits both, especially those with agile mindsets, potentially mitigating tensions.
This study contributes to the theory of organizational ambidexterity by providing empirical evidence that enhancing collaboration between mutually exclusive agile and non-agile mindsets, reflecting exploitative (non-agile mindset) and exploratory (agile-mindset) approaches, improves change management, which can foster organizational ambidexterity and support long-term success.
1. Introduction
Table A1, A2 and A3Organizations that struggle with change often feel unprepared, see change as a disruption that causes chaos, and, as a result, fail to manage it (Gigliotti et al., 2019). As organizations increasingly face a changing business landscape, continuous change becomes a “new normal” (Kotter et al., 2021). This “new normal” creates constant pressure to adjust strategies, operations, and technologies to remain competitively positioned in the market. As a consequence, change management becomes a continuous process embedded in agile organizations routines (AlManei, 2018; Gibbons, 2015; Meglio et al., 2025). Organizations that implement changes “accidentally” often fail in their management; agile ones, in which change is routine, can achieve perfection in wise and prompt adaptation. Mastery of change management practice allows organizations to implement changes efficiently, while agility improves proactive responses to change by recognizing opportunities and threats (Appelbaum et al., 2017). Moreover, according to these authors, agile organizations are characterized by a deep-rooted commitment to continuous change embedded in organizational DNA. In this study, it is assumed that the agile culture is specifically the source of such a commitment that shapes employees' mindsets. Therefore, this study focuses on the internal dynamics of employees with agile and non-agile mindsets and their impact on change management within an agile culture.
These dynamics matter for two reasons. First, an agile mindset is defined as a growth- and change-oriented mindset (Gannod et al., 2018), suggesting that it is critical for detecting change needs and their implementation (Dutta and Chaudhry, 2021). Second, the recently revealed paradox of the agile mindset leader (Kucharska et al., 2026) shows that agile- and non-agile-minded staff cannot cooperate without friction. Tensions between agile and non-agile mindsets may hinder change management, a crucial organizational skill for smooth adaptation.
Specifically, these mindsets differ markedly, thereby contributing to workplace conflict. The given authors termed the tension an “agile leader mindset paradox” because it forces agile leaders to constantly switch between caring for the well-being of non-agile employees—since they, simplifying, need stability and control to perform—and motivating them toward change adaptability, as without it, organizations today cannot grow and compete. However, the agility required for growth can be a significant stressor for non-agile-minded staff, who cannot perform effectively in ambiguous, uncertain conditions they perceive as chaotic. The constant need to balance between stability and change in agile teams was also stressed by Lindskog and Netz (2021). The authors suggested that implementing an agile culture may be an effective solution. Agile culture facilitates continuous change in organizations (Gibbons, 2015; Holbeche, 2019), including constant adjustments to processes, cooperation, conventions, mindset, behaviors, and business units in response to new contexts (Holbeche, 2019; Uwasomba, 2025). Therefore, this study examines whether agile culture indeed facilitates the tension between agile and non-agile mindsets to support change management. On the one hand, the adaptive power of agile culture constitutes a remarkable intangible asset supporting organizational adaptability, survival, and growth (Holbreche, 2019; Siakas and Siakas, 2007). On the other hand, cultural approaches often clash, and organizational resistance to change, often rooted in mindsets, is a significant barrier to the adoption of agile approaches (Kuchel et al., 2023; Neumann et al., 2024). From this perspective, the study aims to clarify how the internal dynamics of employees with agile and non-agile mindsets, shaped by agile culture, impact organizational change management. Ambidexterity theory provides a robust framework for this research, suggesting that reflecting on both exploitative (non-agile mindset) and exploratory (agile mindset) approaches is equally important for long-term organizational success. So, both mutually exclusive approaches (agile and non-agile) are critical for making organizational adoption as prompt as wise.
2. Ambidexterity theory framework
Organizational ambidexterity refers to a company's ability to pursue two strategies simultaneously: exploitation and exploration. Exploitation emphasizes improving efficiency and maximizing performance in current operations, relying on stability and control. Conversely, exploration entails seeking new business ideas and opportunities, particularly in response to market and technological change. This dual approach enables organizations to adapt and thrive in a dynamic environment (Dunca, 1976; Chakma et al., 2021; Papachroni and Heracleous, 2020; O’Reilly et al., 2009). Inspired by this theory, I conclude that tensions in perceptions of ambidexterity are associated with both agile and non-agile mindsets. “Perception is reality” (Caulfield and Senger, 2017). These mindsets face two mutually exclusive realities: exploitative and exploratory. Therefore, developing a firm's ability to engage in both exploitative and exploratory actions, however undoubtedly beneficial, remains challenging. This difficulty is likely because focusing on existing competencies provides stability and control (a non-agile mindset domain), which can create a success trap (we are good at what we do, so let us do it as we used to)—organizational inertia that, from an agile perspective, may hinder adaptation to changing environmental conditions and result in poor performance and failure. Conversely, prioritizing exploratory innovation, continuous learning, and experimentation to identify and implement profitable changes (the agile mindset domain) is often perceived by non-agile mindsets as risky and a sign of failure. In short, ambidexterity, however challenging and tension-causing, is very beneficial for organizations in the long run (Chakma et al., 2021). Therefore, by improving collaboration between agile and non-agile teams—starting with clarifying the root causes of tensions stemming from conflicting mindsets—and fostering mutual understanding, organizations can enhance organizational ambidexterity and support their long-term success.
In summary, this study seeks to advance organizational ambidexterity theory by linking exploitative approaches to a non-agile mindset and exploratory approaches to an agile mindset, and to empirically demonstrate that friction between opposing agile and non-agile mindsets is a fundamental cause of challenges in change management and organizational ambidexterity, ultimately hindering long-term growth.
3. Literature review and hypotheses development
It remains largely unknown how agile culture supports change management by encouraging both agile and non-agile mindsets among staff. To address this gap, we conducted a systematic literature review to gain insights into these relationships; the following query was entered into the Scopus database.
TITLE-ABS-KEY (( “agile culture”) ) AND (LIMIT-TO ( EXACTKEYWORD, “change management”) OR LIMIT-TO (EXACTKEYWORD, “agile mindset”) OR LIMIT-TO (EXACTKEYWORD, “non-agile mindset”) AND LIMIT-TO (EXACTKEYWORD, “non agile mindset”) ).
The Scopus engine returned no publications for this query. A simplified search on “agile mindset” (excluding non-agile mindset studies) also yielded no publications. Therefore, this study aims to address this gap.
3.1 Agile mindset and change management
While agility has proven effective in fast-changing fields like software development and manufacturing, they face significant hurdles in highly regulated, risk-averse environments (Hedges, 2025). It suggests that higher-level agility and low-level change management might be problematic. Agility and change-management misalignment can lead to instability, operational disruptions, and complex coordination challenges (Singha, 2025). Therefore, strong organizational change management skills are vital to avoid chaos.
Achieving mastery in organizational change management requires developing change management competency (Vakola et al., 2007). Gibbons (2015) suggested that “change management” should be replaced with agile management, in which agile leaders are described as change experts with extraordinary change-management skills. Such an idea underscores the urgent need to understand the dynamics of employees with agile and non-agile mindsets.
An agile mindset is a mindset focused on growth and change (Gannod et al., 2018). Simply, it is a mind that remains open. Recent studies assert that organizational agility depends more on employees' mindsets than on technological and methodological management approaches (Eilers et al., 2022; Ozkan et al., 2023; Sathe and Panse, 2023). Tolerance of ambiguity, curiosity, resilience, humility, relationship-building, and perspective-taking characterize an agile workforce, which positively impacts decision-making, innovation, collaboration, and leadership in increasingly complex, uncertain contexts (Caligiuri, 2025). Employees with an agile mindset can then be considered as high-potential assets (Miliani et al., 2024; Roy, 2025). Agile mindset employees (AMe), as defined by Kucharska et al. (2026), are open-minded individuals who effectively use their agile mindset skills to adapt to and embrace change. They view change positively, seeing it as both a challenge and an opportunity. Additionally, they easily adjust to new circumstances to maximize their advantages. These employees are flexible and willing to adapt their approaches to situational and contextual demands. They respond to environmental changes by adapting rather than resisting, and they react positively, constructively, and openly to new tasks.
In contrast, simplifying, non-agile mindset employees (nAMe) require routines, stability, and control to feel safe. Therefore, they struggle with ambiguity and uncertainty, refuse to adapt to such conditions that constitute the “new normal”, and they do not accept changes easily. The only changes they accept are probably those they can fully control (Vardaman et al., 2012). In extreme cases, they may even panic in response to changes (Kucharska et al., 2026). Consequently, responses to the agile culture and its impact on agile organizations can vary between agile and non-agile perspectives. Therefore, the hypothesis is proposed as follows.
Agile mindset employees positively impact change management
Non-agile mindset employees negatively impact change management
3.2 Agile and non-agile mindsets dynamics
As noted in the introduction, the constant need to balance stability and change in agile teams, as stressed by Lindskog and Netz (2021), stems from colliding mindsets, as clarified by the agile leader mindset paradox (Kucharska et al., 2026). The tension between employees with agile and non-agile mindsets creates a constant need for leaders to balance them for organizational purposes. The root of the tension is that the agility required for growth is a significant stressor for non-agile-minded staff, who cannot perform effectively in ambiguous and uncertain conditions. Since agility is a core component of change management and adaptability, the constant balancing between these opposing directions is challenging. Consequently, following these authors' hypothesis was developed as follows:
The mutual impact of employees with an agile and non-agile mindset is negative.
3.3 Agile culture impact on agile and non-agile-minded staff
Successfully implementing and sustaining agile practices is difficult; the main challenge is convincing staff to adopt agile practices in a non-agile culture workplace (Gregory et al., 2016; Nicklich et al., 2021). Therefore, company culture is an important factor supporting the change adoption in companies oriented towards agility (Rigby et al., 2020). Furthermore, agile culture implementation is viewed as a remedy for tensions between members of agile and non-agile mindsets within the company (Kucharska et al., 2026); therefore, it is included in the study. Agile culture is a relatively new concept, and researchers are actively working to develop a comprehensive understanding of it (Kuchel et al., 2023; Magistretti and Trabucchi, 2025). Recently, the Agile Business Consortium (2024) introduced the Agile Culture Matrix (ACM), which is based on the work of Gregory and Taylor (2019). They define agile culture as a work environment rooted in values, behaviors, and practices that enable organizations, teams, and individuals to be more adaptable, flexible, and resilient in the face of complexity, uncertainty, and change. The ACM includes an assessment framework that identifies five levels of development, ranging from survival to transformational excellence. Additionally, it outlines seven focus areas: purpose and results, agile leadership, employee well-being and fulfillment, collaboration and autonomy, trust and transparency, adaptability to change, and innovation and learning.
Agile culture strongly concerns employees and impacts their agile way of thinking and acting (Gregory and Taylor, 2019; Petermann and Zacher, 2021; Ranjeeth, 2025; Uwasomba et al., 2025). Agile culture promotes a sense of ownership and accountability among team members, who are empowered to make decisions and take responsibility for their work (Bastiaansen and Wilderom, 2022). Agile culture also requires a high degree of openness among team members, as well as a willingness to embrace change and adapt to new situations (Bastiaansen and Wilderom, 2022; Vaishnavi et al., 2019). This includes developing a shared vision and values, fostering an environment of trust and psychological safety, and aligning incentives and rewards to encourage desired behaviors (Bastiaansen and Wilderom, 2022; Temitope, 2022). Furthermore, Temitope (2022) highlighted that in an agile environment, employees often feel more engaged and motivated when they have autonomy and can see the immediate results of their work. Empowerment increases job satisfaction, which leads to higher retention rates and improved team morale.
According to Goncalves et al. (2020), Prange (2021), Tolfo et al. (2011), and Vaishnavi et al. (2019), an agile culture fosters an agile way of working by promoting collaboration, transparency, and continuous improvement, which means continuous change. It encourages experimentation and learning through feedback loops and iterative processes. An agile culture prioritizes customer satisfaction and responsiveness to change over strict adherence to plans and processes (Nejatian et al., 2018). Furthermore, Temitope (2022) emphasized that while implementing agile tools and ceremonies is relatively straightforward, the real challenge lies in developing an agile mindset. Organizations that do not invest in comprehensive training programs focused on agile culture often struggle to fully adopt agile principles. Agile culture, as described by Kucharska et al. (2026), is defined as “the culture of an open mind” (mind open to new possibilities), thereby fostering adaptability. Since all the above literature suggests that an agile culture supports the development of an agile mindset, the following hypotheses are formulated:
Agile culture positively impacts employees with an agile mindset.
Agile culture positively impacts employees with non-agile mindsets.
4. Methodology
4.1 Sampling process, measures, and sample quality
The sample comprises knowledge workers, including male and female specialists and managers in the IT sector and in sectors other than IT (50/50 mix). The external agency collected the ordered quota sample in March 2025, in accordance with the highest ethical standards. All participants provided informed consent, contributed voluntarily, and had their anonymity fully protected throughout the research process. Participants come from different organizations (research panel members), so they present a sectoral view rather than a particular company's view. Since many sectors operate mainly online, we chose the Computer-Assisted Web Interview (CAWI) as the most practical data-collection method for professionals who work online. However, one drawback is that this method may miss the views of knowledge workers who do not work online, although this likely involves a small minority. Ultimately, we found that the CAWI method effectively supports high-quality data collection due to its most convenient format for engaging target groups. Participants first responded to two statements: one describing an agile mindset and the other its opposite, by selecting the one they felt was closer to their self-view. From the study's purpose, it was important to maintain a balanced distribution of agile and non-agile self-responses in the sample. The total sample (1871 cases; gender balance maintained) was then divided into two subsamples: agile (AMe, N = 932) and non-agile (nAMe, N = 939). Appendix 1 shows the structure of the subsamples. Next, all respondents answered the same statements ( Appendix 2) that assess their subjective views of their agile and non-agile co-workers, the agile culture, and change management in their organizations, using a 7-point Likert scale (1 = totally disagree; 7 = completely agree). Data were collected in a way that allowed us to analyze all variables of interest from both agile and non-agile perspectives. Therefore, based on the total sample and both subsamples (representing AMe and nAMe views), structural models (Figure 1a–d) were developed (dataset availability: Kucharska, 2026).
Figure 1 illustrates the investigation steps using SEM models, depicting the relationships between employees with an agile mindset and change management (a), employees with a non-agile mindset and change management (b), the interactions between agile and non-agile employees in relation to change management (c), and the impact of an agile culture on change management through both agile and non-agile employees (d). These models were created to provide a comprehensive view of the entire sample, as well as the perspectives of agile-minded and non-agile-minded participants. Overall, the findings reveal significant differences in how agile and non-agile mindsets perceive the same relationships.Visualization of the investigation steps (SEM models). Note: Total/AMe/nAMe samples (N = 1871/932/939) AC: agile culture; nAMe: non-agile mindset employee; AMe: agile mindset employee; CHM: change management; ML-maximum likelihood, standardized estimates; *p < 0.05; **p < 0.01; ***p < 0.001; ns—not significant result, (a) AMe impact on CHM model TOTAL sample Cmin/df = 3.26 CFI = 0.982 TLI = 0.979 RMSEA = 0.035; AMe view Cmin/df = 2.23 CFI = 0.978 TLI = 0.974 RMSEA = 0.035; nAMe view Cmin/df = 2.66 CFI = 0.975 TLI = 0.970 RMSEA = 0.042, (b) nAMe impact on CHM model TOTAL sample Cmin/df = 7.5 CFI = 0.976 TLI = 0.972 RMSEA = 0.057; AMe view Cmin/df = 4.2 CFI = 0.977 TLI = 0.974 RMSEA = 0.059; nAMe view Cmin/df = 4.5 CFI = 0.972 TLI = 0.968 RMSEA = 0.061, (c) AMe and nAMe relations model TOTAL sample Cmin/df = 4.55 CFI = 0.972 TLI = 0.969 RMSEA = 0.044; AMe view Cmin/df = 2.88 CFI = 0.972 TLI = 0.969 RMSEA = 0.045; nAMe view Cmin/df = 2.3 CFI = 0.943 TLI = 0.932 RMSEA = 0.073, (d) AC impact on AMe and nAMe model TOTAL sample Cmin/df = 5.77 CFI = 0.946 TLI = 0.942 RMSEA = 0.051; AMe view Cmin/df = 3.21 CFI = 0.950 TLI = 0.947 RMSEA = 0.049; nAMe view Cmin/df = 3.58 CFI = 0.944 TLI = 0.939 RMSEA = 0.052
Figure 1 illustrates the investigation steps using SEM models, depicting the relationships between employees with an agile mindset and change management (a), employees with a non-agile mindset and change management (b), the interactions between agile and non-agile employees in relation to change management (c), and the impact of an agile culture on change management through both agile and non-agile employees (d). These models were created to provide a comprehensive view of the entire sample, as well as the perspectives of agile-minded and non-agile-minded participants. Overall, the findings reveal significant differences in how agile and non-agile mindsets perceive the same relationships.Visualization of the investigation steps (SEM models). Note: Total/AMe/nAMe samples (N = 1871/932/939) AC: agile culture; nAMe: non-agile mindset employee; AMe: agile mindset employee; CHM: change management; ML-maximum likelihood, standardized estimates; *p < 0.05; **p < 0.01; ***p < 0.001; ns—not significant result, (a) AMe impact on CHM model TOTAL sample Cmin/df = 3.26 CFI = 0.982 TLI = 0.979 RMSEA = 0.035; AMe view Cmin/df = 2.23 CFI = 0.978 TLI = 0.974 RMSEA = 0.035; nAMe view Cmin/df = 2.66 CFI = 0.975 TLI = 0.970 RMSEA = 0.042, (b) nAMe impact on CHM model TOTAL sample Cmin/df = 7.5 CFI = 0.976 TLI = 0.972 RMSEA = 0.057; AMe view Cmin/df = 4.2 CFI = 0.977 TLI = 0.974 RMSEA = 0.059; nAMe view Cmin/df = 4.5 CFI = 0.972 TLI = 0.968 RMSEA = 0.061, (c) AMe and nAMe relations model TOTAL sample Cmin/df = 4.55 CFI = 0.972 TLI = 0.969 RMSEA = 0.044; AMe view Cmin/df = 2.88 CFI = 0.972 TLI = 0.969 RMSEA = 0.045; nAMe view Cmin/df = 2.3 CFI = 0.943 TLI = 0.932 RMSEA = 0.073, (d) AC impact on AMe and nAMe model TOTAL sample Cmin/df = 5.77 CFI = 0.946 TLI = 0.942 RMSEA = 0.051; AMe view Cmin/df = 3.21 CFI = 0.950 TLI = 0.947 RMSEA = 0.049; nAMe view Cmin/df = 3.58 CFI = 0.944 TLI = 0.939 RMSEA = 0.052
Regarding the quality of the total sample, the cross-loading matrix ( Appendix 3) demonstrates the soundness of the measures taken (loadings exceed 0.5 and scales do not overlap). Moreover, the KMO (Kaiser-Meyer-Olkin) test of adequacy (total sample/AMe/nAME) yielded values of 0.970/0.960/0.956, respectively, and the total variance explained was 62%/64%/64%, indicating a good sample adequacy (Byrne, 2016). Harman's single-factor test (31%/27%/26%) and the CMB-common latent factor test (CMB = 28%/25%/30%) indicate no measurement bias (Podsakoff et al., 2012). Table 1 below presents basic statistics, correlations, and square-root AVEs.
Basic statistics, correlations, and the square root of AVE
| Construct | Mean | SD | AVE | CR | Cronbach alpha | AC | AMe | nAMe | CHM |
|---|---|---|---|---|---|---|---|---|---|
| a) Total sample | |||||||||
| AC | 5.51 | 1.29 | 0.55 | 0.90 | 0.89 | 0.741 | |||
| AMe | 5.72 | 1.14 | 0.50 | 0.87 | 0.89 | 0.623 | 0.704 | ||
| nAMe | 3.37 | 1.9 | 0.79 | 0.96 | 0.97 | 0.062 | 0.039 | 0.888 | |
| CHM | 5.18 | 1.00 | 0.52 | 0.85 | 0.85 | 0.332 | 0.527 | 0.109 | 0.724 |
| b) AMe sample | |||||||||
| AC | 5.62 | 1.24 | 0.53 | 0.89 | 0.85 | 0.729 | |||
| AMe | 5.79 | 1.12 | 0.49 | 0.87 | 0.87 | 0.705 | 0.702 | ||
| nAMe | 3.05 | 1.98 | 0.81 | 0.97 | 0.97 | 0.036 | 0.025 | 0.900 | |
| CHM | 5.44 | 0.92 | 0.50 | 0.83 | 0.85 | 0.443 | 0.627 | 0.052 | 0.707 |
| c) nAMe sample | |||||||||
| AC | 5.41 | 1.32 | 0.57 | 0.90 | 0.90 | 0.758 | |||
| AMe | 6.63 | 1.17 | 0.50 | 0.87 | 0.90 | 0.56 | 0.704 | ||
| nAMe | 3.68 | 1.81 | 0.76 | 0.96 | 0.97 | 0.107 | 0.06 | 0.872 | |
| CHM | 4.9 | 1.02 | 0.54 | 0.86 | 0.86 | 0.266 | 0.455 | 0.175 | 0.737 |
| Construct | Mean | SD | AVE | CR | Cronbach alpha | AC | AMe | nAMe | CHM |
|---|---|---|---|---|---|---|---|---|---|
| a) Total sample | |||||||||
| AC | 5.51 | 1.29 | 0.55 | 0.90 | 0.89 | 0.741 | |||
| AMe | 5.72 | 1.14 | 0.50 | 0.87 | 0.89 | 0.623 | 0.704 | ||
| nAMe | 3.37 | 1.9 | 0.79 | 0.96 | 0.97 | 0.062 | 0.039 | 0.888 | |
| CHM | 5.18 | 1.00 | 0.52 | 0.85 | 0.85 | 0.332 | 0.527 | 0.109 | 0.724 |
| b) AMe sample | |||||||||
| AC | 5.62 | 1.24 | 0.53 | 0.89 | 0.85 | 0.729 | |||
| AMe | 5.79 | 1.12 | 0.49 | 0.87 | 0.87 | 0.705 | 0.702 | ||
| nAMe | 3.05 | 1.98 | 0.81 | 0.97 | 0.97 | 0.036 | 0.025 | 0.900 | |
| CHM | 5.44 | 0.92 | 0.50 | 0.83 | 0.85 | 0.443 | 0.627 | 0.052 | 0.707 |
| c) nAMe sample | |||||||||
| AC | 5.41 | 1.32 | 0.57 | 0.90 | 0.90 | 0.758 | |||
| AMe | 6.63 | 1.17 | 0.50 | 0.87 | 0.90 | 0.56 | 0.704 | ||
| nAMe | 3.68 | 1.81 | 0.76 | 0.96 | 0.97 | 0.107 | 0.06 | 0.872 | |
| CHM | 4.9 | 1.02 | 0.54 | 0.86 | 0.86 | 0.266 | 0.455 | 0.175 | 0.737 |
Note(s): Italic font illustrates the square root of AVE
AC: agile culture; nAMe: non-agile mindset employee; AMe: agile mindset employee; CHM: change management
Italic font indicates the square root of AVE; red font indicates that the AVE level is below the accepted minimum of 0.5 (Forner and Larcker, 1981). An AVE of 0.50 or higher is generally considered acceptable, indicating that the construct accounts for at least 50% of the variance in its indicators. AMe AVE <0.50 suggests potential issues with the AMe construct's convergent validity (the construct's meaning may not be reflected in the scale). It is confusing because it occurs only in the AMe sample. The IT sector dominates the AMe sample ( Appendix 1); It may be that their specific perception of agility, rooted in agile software development methods (ASD) rather than in mindset, caused this problem. Moreover, the AVE, measured as a separate CFA of the construct ( Appendix 2), is 0.64, which is correct
4.2 Invariance
Since the sample comprises responses from professionals with agile and non-agile mindsets (knowledge workers), the measurement invariance procedure was applied to assess whether respondents across groups interpreted the measurement instrument similarly (Byrne, 2016). The obtained ΔCFI, ΔTLI, and ΔRMSEA do not exceed 0.01 and accurately measure our constructs of interest across sectoral sub-samples (Byrne, 2016; Putnick and Bornstein, 2016; Raudenska, 2020). As a result, we continued with SEM analysis. Table 2 below presents the details of the invariance measures.
Invariance measurement agile mindsets group (AMe) professionals group (N = 932) vs. non-agile mindsets (nAMe) professionals group (N = 939)
| MCFA models | CFI | IFI | TLI | RMSEA |
|---|---|---|---|---|
| Unconstrained model | 0.958 | 0.958 | 0.955 | 0.032 |
| Loading measurement equality, measurement model (Δ) | 0.958 (0.000) | 0.958 (0.000) | 0.956 (−0.001) | 0.031 (0.001) |
| Factor covariances equality, structural model (Δ) | 0.956 (0.002) | 0.956 (0.002) | 0.955 (0.001) | 0.032 (−0.001) |
| MCFA models | CFI | IFI | TLI | RMSEA |
|---|---|---|---|---|
| Unconstrained model | 0.958 | 0.958 | 0.955 | 0.032 |
| Loading measurement equality, measurement model (Δ) | 0.958 | 0.958 | 0.956 | 0.031 |
| Factor covariances equality, structural model (Δ) | 0.956 (0.002) | 0.956 (0.002) | 0.955 (0.001) | 0.032 (−0.001) |
Note(s): MCFA analyses were performed following the procedure of multigroup CFA analysis (Byrne, 2016)
The next section presents the study results in the following order:
STEP 1. Agile mindset (AMe) impact on change management (CHM) - Figure 1a
STEP 2. Non-Agile mindset (nAMe) impact on change management (CHM) - Figure 1b
STEP 3. Agile and non-agile mindsets employees' interrelation - Figure 1c
STEP 4. Agile culture (AC) impact on change management through agile and non-agile mindset staff - Figure 1d
Comparing these models from opposing perspectives clarifies the dynamics of agile and non-agile mindsets.
5. Results
This study aimed to bring understanding of how the internal dynamics of employees with agile and non-agile mindsets, shaped by agile culture, impact organizational change management. STEP 1 (Figure 1a) revealed that the perceived impact of agile-minded professionals on change management is perceived as highest by this group (H1a, β = 0.56***). Non-agile employees also perceive their agile colleagues' impact on change management as strong and positive, but not as high as agile mindset professionals perceive their own impact. Conversely, STEP 2 (Figure 1b) revealed that only non-agile-mindset professionals view their contributions to change management positively (H1b, β = 0.13***). Agile-minded employees perceive their non-agile colleagues' contributions to change management negatively (H1b, β = −0.07***); the same pattern is observed in the total sample (H1b, β = −0.27***). The lesson from the two steps is that each group (agile and non-agile mindsets) sees itself more favorably than the opposing group does. However, it must be noted that professionals with an agile mindset view non-agile partners harshly. STEP 3 (Figure 1c) corroborates this conclusion by revealing friction between co-workers with agile and non-agile mindsets. Agile mindset professionals view it more harshly than non-agile colleagues (H1c, β = −0.10***/ns). The total sample (H1c, β = −0.30***) indicates the extent of this friction. Still, this step confirms all conclusions regarding the impact of agile and non-agile mindsets on change management. STEP 4 (Figure 1d) illustrates how agile culture can transform organizations. This step exposed that agile culture, enhancing non-agile mindsets (H2b, β = 0.11***), fosters change management (H1b, β = 0.15***), and this transformation is noted by agile minds who perceive non-agile mindset professionals' impact on change management positively (H1b, β = 0.09***), in contrast to their negative perception observed in models without agile culture. Regarding professionals' positive self-view of their contributions to change management, this effect is even stronger in the model that includes an agile culture (H2a, β = 0.64 ***). The lesson from the last step is that an agile culture can mitigate friction between colleagues with agile and non-agile mindsets. Table 3 and Figure 1 below present details of the elaborated results.
Hypotheses verification
| a) AMe impact on CHM (Figure 1a) | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view Cmin/df = 3.26 CFI = 0.982 TLI = 0.979 RMSEA = 0.035 N = 1871 R2 = 25% | AMe professionals view Cmin/df = 2.23 CFI = 0.978 TLI = 0.974 RMSEA = 0.035 N = 932 R2 = 32% | nAMe Professionals view Cmin/df = 2.66 CFI = 0.975 TLI = 0.970 RMSEA = 0.042 N = 939 R2 = 17% |
| H1a | 0.50*** (sustained) | 0.56*** (sustained) | 0.41*** (sustained) |
| a) AMe impact on CHM ( | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view | AMe professionals view | nAMe |
| 0.50*** (sustained) | 0.56*** (sustained) | 0.41*** (sustained) | |
| b) nAMe impact on CHM (Figure 1b) | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view Cmin/df = 7.5 CFI = 0.976 TLI = 0.972 RMSEA = 0.057 N = 1871 R2 = 7% | AMe professionals view Cmin/df = 4.2 CFI = 0.977 TLI = 0.974 RMSEA = 0.059 N = 932 R2 = 00% | nAMe Professionals view Cmin/df = 4.5 CFI = 0.972 TLI = 0.968 RMSEA = 0.061 N = 939 R2 = 2% |
| H1b | −0.27*** (sustained) | −0.07(.**) (sustained) | 0.13*** (rejected) |
| b) nAMe impact on CHM ( | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view | AMe professionals view | nAMe |
| −0.27*** (sustained) | −0.07(.**) | 0.13*** (rejected) | |
| c) AMe and nAMe employee relations model (Figure 1c) | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view Cmin/df = 4.55 CFI = 0.972 TLI = 0.969 RMSEA = 0.044 N = 1871 R2 = 27% | AMe professionals view Cmin/df = 2.88 CFI = 0.972 TLI = 0.969 RMSEA = 0.045 N = 932 R2 = 31% | nAMe Professionals view Cmin/df = 2.3 CFI = 0.943 TLI = 0.932 RMSEA = 0.073 N = 939 R2 = 64% |
| H1a | 0.46*** (sustained) | 0.56*** (sustained) | 0.40*** (sustained) |
| H1b | −0.14*** (sustained) | −0.07(.**)(sustained) | 0.15*** (rejected) |
| H1c | −0.30*** (sustained) | −0.10(.***)(sustained) | −0.02(0.547)(rejected) |
| c) AMe and nAMe employee relations model ( | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view | AMe professionals view | nAMe |
| 0.46*** (sustained) | 0.56*** (sustained) | 0.40*** (sustained) | |
| −0.14*** (sustained) | −0.07(.**)(sustained) | 0.15*** (rejected) | |
| −0.30*** (sustained) | −0.10(.***)(sustained) | −0.02(0.547)(rejected) | |
| d) AC impact on AMe and nAMe employees (Figure 1d) | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view Cmin/df = 5.77 CFI = 0.946 TLI = 0.942 RMSEA = 0.051 N = 1871 R2 = 31% | AMe professionals view Cmin/df = 3.21 CFI = 0.950 TLI = 0.947 RMSEA = 0.049 N = 932 R2 = 39% | nAMe Professionals view Cmin/df = 3.58 CFI = 0.944 TLI = 0.939 RMSEA = 0.052 N = 939 R2 = 23% |
| H1a | 0.64*** (sustained) | 0.70*** (sustained) | 0.45*** (sustained) |
| H1b | 0.06* (sustained) | 0.04(0.249)(rejected) | 0.15*** (rejected) |
| H2a | 0.52*** (sustained) | 0.63*** (sustained) | 0.56*** (sustained) |
| H2b | 0.09*** (sustained) | 0.04(0.315)(rejected) | 0.11** (rejected) |
| d) AC impact on AMe and nAMe employees ( | |||
|---|---|---|---|
| Hypothesis | TOTAL professionals view | AMe professionals view | nAMe |
| 0.64*** (sustained) | 0.70*** (sustained) | 0.45*** (sustained) | |
| 0.06* (sustained) | 0.04(0.249)(rejected) | 0.15*** (rejected) | |
| 0.52*** (sustained) | 0.63*** (sustained) | 0.56*** (sustained) | |
| 0.09*** (sustained) | 0.04(0.315)(rejected) | 0.11** (rejected) | |
Note(s): N = 1871, ML, standardized results *p < 0.05; **p < 0.01; ***p < 0.001; ns—not significant result
Results summary:
Agile mindset participants see themselves and are seen by others as contributing to change management (Figure 1a)
Non-agile mindset participants see themselves as positively contributing to change management. However, others view them oppositely (Figure 1b).
The dynamics (cooperation) between agile and non-agile mindset colleagues is problematic and may cause friction (Figure 1c).
Agile culture positively impacts agile and non-agile mindset members of an organization (Figure 1d). However, its impact on members of the agile mindset is substantially greater.
Agile culture fosters change management in an organization.
6. Discussion
Gren and Lindman (2020) argued that team maturity and composition, along with “culture and mindset,” are key challenging categories related to group dynamics that affect agile work practices. This study seems to confirm this. The present study provides empirical evidence that perceptions of friction between agile and non-agile mindsets depend on the perspective taken about these mindsets. It also shows that agile mindsets tend to view this friction more negatively. Furthermore, cultivating an agile culture shows potential to reduce such friction, as it helps shift negative perceptions of colleagues with agile mindsets toward their non-agile counterparts; however, this change remains subconscious. The self-view of non-agile professionals stays unchanged after exposure to an agile culture. The findings suggest that an agile culture benefits both non-agile professionals and organizations by turning negative attitudes toward non-agile colleagues into more positive perceptions, which could be beneficial in the long run. So, discussing these results through the lens of ambidexterity theory highlights the need for a balanced integration of agile and non-agile mindsets to manage change efficiently.
Managing tensions and paradoxes (the core of ambidexterity), along with change management, should be a key competency of agile organizations, seen as highly adaptive, comprehensive systems (Kucharska et al., 2026). The systematic literature review on organizational agility by Vaszkun and Sziraki (2023) revealed that agile organizations must align their structures, cultures, and leadership to achieve true agility. They identified agile culture as a key enabler of this alignment. Additionally, Jivan et al. (2020), Kuchel et al. (2023), Neuman et al. (2024), Kavitha and Suresh (2019), and Rebentisch et al. (2018) highlighted that many failures of agile management methods stem from cultural issues, underscoring the importance of studies on agile culture. In summary, understanding how to implement and manage an agile culture across organizational levels remains limited, particularly when these levels exhibit diverse agile cultural characteristics. Therefore, further research is required to deepen the knowledge of agile cultural practices.
Kucharska et al. (2026), building on Ambituuni et al. (2021), suggest that agile mindsets are rare yet crucial for change and growth and should be protected. This study uses a purposive sample, with nearly equal subsamples of participants who self-assess as agile and non-agile. Further research is needed to confirm whether agile mindsets are truly a minority or equally common. Additionally, some individuals' awareness of their mindset, agility or not, may be limited. Therefore, further research is necessary to verify the accuracy of the self-assessment of agility.
7. Limitations, further research directions
The previous sections highlighted the importance of the agile mindset and culture. However, there is a lack of understanding of how to effectively implement and manage agile culture and foster an agile mindset at different organizational levels, especially in diverse cultural contexts. Many agile failures stem from cultural challenges, underscoring the need for deeper examination of how a shared agile mindset happens. A significant gap exists in knowledge about successfully applying and overseeing agile culture, indicating a need for further research.
Moreover, the final model (Figure 1d) reached an R-squared of 39%, indicating that it explains only 39% of the variation in how agile culture, staff with agile and non-agile mindsets, and change management function within organizations. This indicates that the remaining 61% is unexplained. So, this topic requires further research.
Furthermore, the study results indicate that a solution to mitigate friction between agile and non-agile mindsets is needed, but we lack understanding of how these mindsets are formed or whether they can be trained; such knowledge would help identify such a solution. Therefore, more in-depth psychological studies are needed to gain this understanding.
As noted earlier, using CAWI for data collection is a limitation, but it was the most convenient way to reach remote participants, especially IT professionals knowledgeable about agility. A drawback is that it may exclude non-online knowledge workers, though likely a small minority. Future studies could use other methods to compare results. Overall, the CAWI method may be the best option for data collection in an increasingly digital work and life environment, not just among IT participants.
Furthermore, as reported in the previous sections, this study uses a purposive sample with nearly equal subsamples of participants with self-assessed agile and non-agile mindsets. Further research is needed to determine how frequent agile mindsets are in populations; similarly, awareness of one's mindset may be limited. Additional studies are then required to verify the accuracy of the agile mindset self-assessment.
8. Practical guidelines
In short, the study broadens ambidexterity theory by emphasizing that enhancing collaboration between agile and non-agile teams—beginning with identifying the root causes of tensions caused by conflicting mindsets—and fostering mutual understanding can strengthen organizational ambidexterity and promote long-term success.
Several practical tips emerge from the study results:
Shape the company's culture into an agile one. Use it to support ongoing changes by embedding an agile mindset into strategies, tactics, operations, and training to benefit everyone at all levels of the company involved and ensure everyone is committed to making change management a routine.
Create a work environment where employees with different mindsets are more engaged and motivated. Agile employees thrive with autonomy and immediate results; non-agile ones excel with clear goals, rules, and procedures. Customizing empowerment can boost satisfaction, retention, and morale for everyone.
Leverage individuals with an agile mindset as change agents: since they see themselves and are seen by others as drivers of change, organizations can assign them roles that support transformation.
Address perception gaps among members with a non-agile mindset: although these individuals believe they are contributing positively to change, others may not see it the same way. Targeted feedback, communication, and training can help align perceptions and foster mutual understanding.
Facilitate cooperation between agile and non-agile mindsets: challenging dynamics require interventions like team workshops, open dialog, group coaching, or mentoring to reduce friction and support collaboration.
9. Conclusions
This study advances the theory of organizational ambidexterity by linking exploitative approaches with a non-agile mindset and exploratory approaches with an agile mindset. It provides empirical evidence that improving understanding between agile and non-agile perspectives enhances change management, fosters organizational ambidexterity, and supports long-term development. Additionally, the study demonstrates that an agile culture can subtly reduce friction between these opposing mindsets. In summary, the research emphasizes the urgent need for solutions that encourage collaboration between agile and non-agile mindsets, thereby enabling more effective change management.
AI usage declaration
Human wrote this manuscript, and it does not contain any AI-generated content.
The presented research results from the project Agile learning culture influence on dynamic capabilities of knowledge-based organizations: Polish-Finnish cross country analysis No. UMO-2022/47/B/HS4/00597, which is financed by the National Science Center (NCN) Poland.
Appendix 1
Subsamples structure
| Agile mindset (AMe) sample N = 932 | Non-agile mindset (nAMe) sample N = 939 | ||
|---|---|---|---|
| gender | F | 437 | 499 |
| M | 495 | 440 | |
| position | specialists | 489 | 745 |
| leaders | 443 | 194 | |
| sector | IT | 599 | 344 |
| other | 333 | 595 |
| Agile mindset (AMe) sample | Non-agile mindset (nAMe) sample | ||
|---|---|---|---|
| gender | F | 437 | 499 |
| M | 495 | 440 | |
| position | specialists | 489 | 745 |
| leaders | 443 | 194 | |
| sector | IT | 599 | 344 |
| other | 333 | 595 |
Appendix 2
Scales, their sources, and reliabilities
| Scale | Statements | Reliabilities based on CFA models |
|---|---|---|
| Agile mindset employee [AMe]/Non-agile mindset employee [nAMe] Kucharska et al. (2026) | About workmates perceived by you as an agile mindset: a. they quickly adapt to new conditions b. they usually achieve their aims c. they never give up d. they learn, unlearn, and relearn smoothly e. they can work in unpredictable environments (they accept uncertainty) f. they act dynamically g. they see change as an opportunity h. they are passionate, enthusiastic, and engaged at work (all conditions met) i. they can follow schemes but also can act creatively beyond schemes (both conditions met) j. they accept the challenge k. they are open to change l. they accept risk m. they learn from mistakes ATTENTION: Regarding non-agile mindsets, participants responded to the same statements as they did for agile mindsets, but usually responded negatively or oppositely (disagreed with statements given). The same method (reverse coding) was applied by Dweck et al. (1995) to measure growth and fixed mindsets | Cronbach alpha = 0.90 AVE = 0.64 CR = 0.92 Cmin/df = 4.6 CFI = 0.960 TLI = 0.952 RMSEA = 0.062/Cronbach alpha = 0.97 AVE = 0.76 CR = 0.96 Cmin/df = 4.4 CFI = 0.979 TLI = 0.972 RMSEA = 0.078 |
| Agile culture [AC] Authors' scale based on qualitative codes identified by Kucharska et al. (2026) | About company's culture orientation towards agility: a. We have common goals, vision, and values concerning constant development b. We have a common willingness to learn and improve our work methods so that we can achieve more and more together c. We are inclusive (we care that everyone feels part of the team) d. We communicate openly and solve problems on an ongoing basis e. together we are very efficient f. We constantly search for the best solutions, so we are ready to be wrong g. Change is a norm h. Nothing is set-up forever i.we accept ambiguity (not everything looks as it seems – so, we stay open-minded) | Cronbach alpha = 0.87 AVE = 0.55 CR = 0.80 Cmin/df = 4.0 CFI = 0.979 TLI = 0.972 RMSEA = 0.058 |
| Change management Adapted from: Grover and Jeong (1995), Al‐Mashari and Zairi (1999), Xiang et al. (2014) | a.we are informed in advance about the reasons for upcoming changes b.the company communicates the change implementation plan (step by step) c.the company cares about employees' attitudes toward the change d.the organization provides adequate training for employees to facilitate change e.the organization delivers all resources and tools needed to implement the change f.managers support employees with change adoption | Cronbach alpha = 0.86 AVE = 0.55 CR = 0.87 Cmin/df = 4.0 CFI = 0.960 TLI = 0.919 RMSEA = 0.068 |
| Scale | Statements | Reliabilities based on CFA models |
|---|---|---|
| Agile mindset employee [AMe]/Non-agile mindset employee [nAMe] | About workmates perceived by you as an agile mindset: | Cronbach alpha = 0.90 |
| Agile culture [AC] | About company's culture orientation towards agility: a. We have common goals, vision, and values concerning constant development | Cronbach alpha = 0.87 |
| Change management | a.we are informed in advance about the reasons for upcoming changes | Cronbach alpha = 0.86 |
Note(s): TOTAL sample N = 1871
Appendix 3
Cross-loadings matrix
| Factors | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| AMe_1 | 0.727 | |||
| AMe_2 | 0.741 | |||
| AMe_3 | 0.643 | 0.105 | ||
| AMe_4 | 0.738 | |||
| AMe_5 | 0.710 | |||
| AMe_6 | 0.700 | |||
| AMe_7 | 0.718 | |||
| AMe_8 | 0.683 | |||
| AMe_9 | 0.647 | |||
| AMe_10 | 0.749 | |||
| AMe_11 | 0.775 | |||
| AMe_12 | 0.641 | |||
| AMe_13 | 0.620 | |||
| nAMe_1 | 0.852 | |||
| nAMe_2 | 0.905 | |||
| nAMe_3 | 0.892 | |||
| nAMe_4 | 0.882 | |||
| nAMe_5 | 0.886 | |||
| nAMe_6 | 0.876 | |||
| nAMe_7 | 0.894 | |||
| nAMe_8 | 0.902 | |||
| nAMe_9 | 0.893 | |||
| nAMe_10 | 0.907 | |||
| nAMe_11 | 0.891 | |||
| nAMe_12 | 0.882 | |||
| nAMe_13 | 0.859 | 0.258 | ||
| AC_1 | 0.701 | |||
| AC_2 | 0.743 | |||
| AC_3 | 0.699 | |||
| AC_4 | 0.718 | |||
| AC_5 | 0.734 | |||
| AC_6 | 0.635 | 0.133 | ||
| AC_7 | 0.582 | 0.145 | −0.153 | |
| AC_8 | 0.718 | |||
| AC_9 | −0.154 | 0.820 | ||
| CHM_1 | 0.130 | 0.735 | ||
| CHM_2 | 0.765 | |||
| CHM_3 | 0.799 | |||
| CHM_4 | 0.784 | |||
| CHM_5 | 0.788 | |||
| CHM_6 | −0.138 | 0.745 | ||
| Factors | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| AMe_1 | 0.727 | |||
| AMe_2 | 0.741 | |||
| AMe_3 | 0.643 | 0.105 | ||
| AMe_4 | 0.738 | |||
| AMe_5 | 0.710 | |||
| AMe_6 | 0.700 | |||
| AMe_7 | 0.718 | |||
| AMe_8 | 0.683 | |||
| AMe_9 | 0.647 | |||
| AMe_10 | 0.749 | |||
| AMe_11 | 0.775 | |||
| AMe_12 | 0.641 | |||
| AMe_13 | 0.620 | |||
| nAMe_1 | 0.852 | |||
| nAMe_2 | 0.905 | |||
| nAMe_3 | 0.892 | |||
| nAMe_4 | 0.882 | |||
| nAMe_5 | 0.886 | |||
| nAMe_6 | 0.876 | |||
| nAMe_7 | 0.894 | |||
| nAMe_8 | 0.902 | |||
| nAMe_9 | 0.893 | |||
| nAMe_10 | 0.907 | |||
| nAMe_11 | 0.891 | |||
| nAMe_12 | 0.882 | |||
| nAMe_13 | 0.859 | 0.258 | ||
| AC_1 | 0.701 | |||
| AC_2 | 0.743 | |||
| AC_3 | 0.699 | |||
| AC_4 | 0.718 | |||
| AC_5 | 0.734 | |||
| AC_6 | 0.635 | 0.133 | ||
| AC_7 | 0.582 | 0.145 | −0.153 | |
| AC_8 | 0.718 | |||
| AC_9 | −0.154 | 0.820 | ||
| CHM_1 | 0.130 | 0.735 | ||
| CHM_2 | 0.765 | |||
| CHM_3 | 0.799 | |||
| CHM_4 | 0.784 | |||
| CHM_5 | 0.788 | |||
| CHM_6 | −0.138 | 0.745 | ||
Note(s): Factor extraction method - principal components. Rotation method - Promax with Kaiser normalization
The matrix reached convergence in 5 iterations

