The effect of organizational inertia on the adoption of big data analytics

Information is now a strategic resource that generates value in private and social spheres (UNCTAD, 2021). Adopting digital technologies is critical to narrowing productivity gaps between Latin American countries and developed nations by creating new sources of growth and quality jobs (Vilgis et al., 2023). Latin American countries continue to face structural challenges that hinder the adoption of data-intensive technologies, including big data analytics (BDA) (ECLAC, 2022).

Despite the importance of data use and BDA, the main obstacles to becoming a data-driven rather than a technological organization are human factors, such as people, culture, processes and organization (Davenport and Bean, 2023). Establishing a data-driven culture poses both an objective and a challenge, as data executives concentrate on initiatives to modify organizational behaviors and attitudes (Brown, 2023).

BDA differs from traditional IT technologies in that it operates as a service rather than an integrated operational process. Unlike enterprise resource planning systems that connect activities across departments and directly affect financial outcomes (Babu and Sastry, 2014), BDA enhances specific operations, such as customer identification and improved offerings (Hung et al., 2020), and fosters satisfaction and loyalty (Gopal et al., 2022). This service orientation engenders resistance to change because organizations perceive BDA as separate from core processes (Mikalef et al., 2021).

Research on resistance to BDA adoption primarily addresses individual-level factors, while studies at the organizational-level remain limited. For instance, Shahbaz et al. (2019) examine the gap between intention to use and actual BDA use, while Mikalef et al. (2021) explore inertial forces during BDA deployment. Recent studies have examined process-level drivers of BDA adoption success from a value-chain perspective, highlighting differences between internal and external value-chains (El-Haddadeh et al., 2025). Further research has underscored the need for data-driven dynamic capabilities in emerging markets to enable digital transformation through knowledge-sharing and integration mechanisms (Anning-Dorson et al., 2025). In addition, in BDA adoption, cultural factors influence the extent to which organizations resist or support digital transformation (Orero-Blat et al., 2025). Organizational inertia (OI) refers to resistance to environmental changes and comprises perceptual, action and psychological dimensions (Godkin and Allcorn, 2008). Much of the existing literature on BDA adoption and OI remains qualitative, limiting generalizability and cross-contextual insights. Prior literature seldom integrates these perspectives on BDA adoption with an organizational-level conceptualization of OI as a multi-dimensional construct, particularly in the Latin American context. Addressing this gap can provide strategies to overcome internal resistance and advance digital transformation.

The objective of this research is to examine the impact of OI on BDA adoption, treating OI as a second-order formative construct composed of perceptual, action and psychological dimensions. The study also explores the moderating role of department leader power on this relationship, contributing to a deeper understanding of organizational resistance to innovation.

This research contributes to the literature in several ways. First, it attempts to explain, from an organizational viewpoint, the low BDA adoption rates and associated difficulties, as BDA is different from other information technologies (ITs). Second, the use of OI based on evolutionary theory explains this resistance to adoption and expands the boundaries of this theory to the topic of advanced IT. Third, this research introduces a new model linking OI and BDA adoption, with the novel inclusion of the archetype of the department leader’s power as a moderator, expanding the literature on BDA, IT and fundamental organizational theories. Fourth, it validates the measurement model for OI, establishing its dimensions as a formative second-order construct.

A literature review on BDA adoption (Aboelmaged and Mouakket, 2020) indicates that prior research on firm-level BDA adoption examines individual behaviors and perceptions while neglecting structural and strategic mechanisms. This focus reveals a gap in understanding organizational-level determinants and their integration within broader adoption frameworks. Prior research identifies organizational determinants such as organizational encouragement, expectations and size (Yu et al., 2022). Other studies report factors including knowledge use and sharing, collaboration, human capital, change management, managerial, infrastructure and data capabilities, networks, user-technology interactions and task and technology characteristics (Aboelmaged and Mouakket, 2020). Additional factors include tangible resources and workforce skills (Chen et al., 2024), top management support, organizational readiness and data-driven culture (Babalghaith and Aljarallah, 2024).

Relevant literature highlights the influence of OI on IT constructs. OI affects corporate digital entrepreneurship (Li et al., 2023), impacts service member creativity (Alkayid et al., 2022), shapes business model innovation and open innovation (Moradi et al., 2021), influences organizational agility and IT ambidexterity (Zhen et al., 2021) and determines business model innovation and organizational learning (Huang et al., 2020).

Some studies use OI as a moderator in relationships, including the dynamic capabilities of SMEs and organizational performance (Nedzinskas et al., 2013). Research identifies a relationship between behavioral intentions to use BDA and actual BDA use, highlighting the moderating role of resistance to change (Shahbaz et al., 2019), which can be considered a predecessor of psychological inertia. A qualitative study reports that certain inertia forces can impede the assimilation of BDA as dynamic capabilities emerge (Mikalef et al., 2021). Recent studies on BDA adoption examine value chain process-level drivers (El-Haddadeh et al., 2025), cultural influences on BDA capabilities (Orero-Blat et al., 2025), and dynamic capabilities in emerging markets (Anning-Dorson et al., 2025). These studies are presented in Table 1.

In sum, existing research has examined OI’s influence on IT constructs; however, it has not demonstrated its impact on BDA or the moderating effect of departmental leader power. This review underscores the originality of this study.

Traditionally, the concept of inertia appears in two distinct theoretical perspectives. The adaptationist view frames inertia as the capacity to adjust to significant environmental changes, where continuous transformation enhances long-term performance. Inertia functions as a prerequisite for change rather than a consequence (Nedzinskas et al., 2013). The ecological view emphasizes the temporality of change, stating that organizational survival depends on whether learning and adaptation progress faster than environmental shifts. This study applies the ecological perspective as the theoretical lens for conceptualizing OI.

Within this framework, organizations integrate environmental uncertainty into their capabilities, strategies and structures (Hannan and Freeman, 1984). Effective adaptation requires synchronizing learning and response with the pace of external changes. Strategic readiness depends on the organization’s ability to address uncertainty through the dimensions of OI, which shape direction and performance (Hedberg and Ericson, 1997).

Prior studies have used resource-based theory, dynamic capabilities theory and TAM-TTF to examine BDA adoption (El-Haddadeh et al., 2025; Anning-Dorson et al., 2025; Shahbaz et al., 2019). These conceptual frameworks do not examine mechanisms for addressing organizational resistance, which supports the analytical relevance of OI as a complementary lens. This study operationalizes OI through the three dimensions: perceptual, action and psychological (Godkin and Allcorn, 2008).

Perceptual inertia arises when a discrepancy exists between critical environmental changes and the organization’s recognition of these changes (Godkin and Allcorn, 2008). It reflects a limited comprehension of shifts in the organizational environment. Management lacks an adequate interpretation of internal and external signals necessary to adapt behaviors in response to change (Hedberg and Ericson, 1997). Organization members demonstrate insufficient understanding of the nature and causes of environmental developments. This inertia constrains the organization’s learning processes (Huang et al., 2013).

Action inertia arises after managerial awareness of environmental change, when the response is delayed, and outcomes remain misaligned with current conditions (Godkin and Allcorn, 2008). It follows environmental analysis and reflects ineffective implementation despite the recognized need (Hedberg and Ericson, 1997). Limited role learning contributes to this inertia; staff may receive training but fail to act on the knowledge acquired (Godkin and Allcorn, 2008). Audience learning occurs when individuals adjust their behavior based on knowledge but cannot influence others to do the same (Huang et al., 2013).

Psychological inertia arises when the organization exhibits stress, anxiety and defensive attitudes that resist change, resulting in individual and group dysfunctions. A lack of psychological motivation reinforces a preference for the status quo, undermining organizational performance (Godkin and Allcorn, 2008).

OI is modelled as a second-order formative construct defined by three distinct dimensions: perceptual, action and psychological. Perceptual inertia captures gaps in recognizing environmental change; action inertia reflects delays in managerial response; psychological inertia denotes resistance to change linked to stress or defensiveness (Godkin and Allcorn, 2008). These IO dimensions are non-interchangeable and causally define OI. In the case of OI, the formative structure reflects the multidimensional and independent contributions of each component, in contrast to a reflective measurement structure where indicators result from the latent factor. This formative approach aligns with the theoretical complexity of OI and its role in organizational adaptation and performance.

BDA adoption refers to the organizational process of integrating advanced data-driven technologies and practices to enhance decision-making, innovation and performance by leveraging data as a strategic resource (Aboelmaged and Mouakket, 2020; Yu et al., 2022; Grover et al., 2018). BDA adoption requires significant organizational change, integrating advanced data-driven technologies to enhance decision-making and performance (Aboelmaged and Mouakket, 2020). Organizational support, resource readiness and leadership commitment drive successful adoption (Yu et al., 2022; Babalghaith and Aljarallah, 2024). Resistance to change, resource constraints and cultural barriers often delay BDA implementation (Mikalef et al., 2021). Effective leadership and resource mobilization are critical for overcoming inertia during the adoption process (Grover et al., 2018).

Therefore, organizational members must understand what BDA is to adopt it. However, a global talent shortage has peaked, with 77% of companies reporting difficulties filling IT vacancies (ManpowerGroup, 2023). This talent shortage indicates a general lack of specialist knowledge, translating into a lack of awareness of the subject; previous research shows that the lack of skills of technical employees is a barrier to BDA adoption (Mikalef et al., 2021). Similarly, previous studies have shown that for companies to adopt BDA, they must be aware of the benefits it offers (Grover et al., 2018). In prior studies, the relative advantage of this technology has been identified as a precursor to BDA adoption (Aboelmaged and Mouakket, 2020). Without knowledge of these benefits, BDA adoption is not possible. Consequently, when management remains isolated due to a lack of awareness of environmental changes, it is in a state of perceptual inertia, a dimension of OI (Godkin and Allcorn, 2008), and it cannot adopt BDA. As a result, the following hypothesis is proposed:

BDA adoption often encounters action inertia, characterized by slow responses from management after environmental analyses, leading to minimal change outcomes (Godkin and Allcorn, 2008). Top executives frequently exhibit low adoption rates of new technology systems (Youssef et al., 2022), while organizational factors such as attitudes toward technology can further delay adoption (Jahanmir and Cavadas, 2018). Senior management support (Lutfi et al., 2023), including resource allocation, plays a critical role in facilitating BDA adoption (Aboelmaged and Mouakket, 2020).

Empirical evidence highlights management as a key driver of BDA adoption (Babalghaith and Aljarallah, 2024). Consequently, delays in management action represent a form of action inertia, contributing to slower organizational responses to technological changes. Thus:

BDA adoption is influenced by psychological inertia, a component of OI, manifesting as stress, anxiety and defensiveness against change within organizations. These reactions lead to rigid commitments and dysfunctions that negatively impact firm performance (Godkin and Allcorn, 2008). While innovation is a critical strategy for profitable growth, psychological inertia diminishes creativity, a key driver of innovation (Li et al., 2007). In addition, OI obstructs innovation, as even highly innovative companies encounter internal resistance, making it challenging to adopt new business methods (Huang et al., 2013). Overcoming these barriers requires cultural transformation, strong leadership and the active involvement of senior management to initiate and sustain BDA adoption (Barlette and Baillette, 2022). Furthermore, change management often precedes BDA adoption (Aboelmaged and Mouakket, 2020).

Psychological inertia, as part of OI, hampers organizational creativity, cultural transformation and adaptation to innovations such as BDA. This dimension of OI directly affects the organization’s ability to adapt and innovate. Thus:

The dimensions of OI insight inertia, action inertia and psychological inertia, as theoretically proposed by Godkin and Allcorn (2008), are complementary and inherently suggest that BDA, like all innovations, should be subject to OI, delaying its implementation. OI, which involves technology that is not part of operational processes but aimed at improving decision-making, is somewhat intangible and can wait, giving preference to day-to-day operations. As a result, just as each dimension affects the adoption of BDA, these dimensions are part of a more holistic concept, namely OI. It is thus proposed that OI should influence the implementation of BDA, therefore:

The Archetype of the Department Leader’s Power refers to how a firm coordinates its departments by granting varying power levels to department heads. The firm selects an archetype that defines its approach to coordination and power distribution among its leaders (Siggelkow and Rivkin, 2005).

Thus, executives exert significant influence on innovation, particularly when decision-making power is centralized, as they shape organizational values and culture supporting innovation (Damanpour and Schneider, 2006). Executive power can either facilitate or hinder the adoption of innovations, including BDA, depending on the decisions and resource allocation. Managers often resist adopting new IT systems (Youssef et al., 2022). Organizational silos and fears of loss of control have also been identified as barriers to BDA adoption (Mikalef et al., 2021). Organizational support, encompassing technological and human resources, plays a critical role in adoption processes, with leadership resource allocation being pivotal (Yu et al., 2022). Top management support and organizational readiness significantly influence BDA adoption, with leadership’s control over resources affecting the process’s speed and success (Babalghaith and Aljarallah, 2024). Concentrated or misdirected management power may exacerbate OI and delay technological adoption.

According to OI (Hannan and Freeman, 1984), resource mobilization is essential for structural changes, including BDA adoption. When department leaders control human, financial and accounting resources, their inertial tendencies can delay resource mobilization, slowing BDA adoption. Authority forms may influence adoption speed and outcomes. Thus:

This study adopts a mixed-method approach; the quantitative phase identifies statistical relationships, while the qualitative phase interprets these findings by exploring underlying organizational dynamics. This combination complements and strengthens the study’s explanatory power by integrating broad patterns with practitioner insights.

The quantitative study took place in Peru, using the database of the country’s most prominent business school as the source of informants. This database includes professionals with department head and management positions from the country’s leading companies. One thousand five hundred and three invitations were sent out to complete the survey online, with a response rate of 26.6%. Of these, 287 (19.1%) remained after a filter question confirmed that the professionals were involved in or could influence the adoption of emerging technologies. An a priori power analysis using G*Power for a multiple regression with four predictors, effect size f²=0.05, α = 0.05 and power = 0.95 indicated a minimum required sample size of 263 observations. The study sample of 287 responses exceeds this threshold.

The informants from the companies were screened with an initial question about their influence on the decision to adopt ITs; their mean age was 38.6 years, 59.2% were men, and they had an average of 6.0 years working in the company. The working positions distribution was 69.3% in IT, 21.3% in sales and marketing, 5.9% in operations, 5.2% in R&D, 4.5% in administration and 3.9% in finance. Their positions were department heads (49.1%), managers (42.5%) and independent professionals (8.4%). The represented firms have been operating for an average of 30.1 years, with an average of 287 employees, and represent a variety of sectors: 30.0% in IT and telecommunications, 25.1% in retail, 12.5% in manufacturing and logistics, 12.5% in construction and mining, 10.1% in finance, 3.5% in health, 3.1% in consulting and 3.1% in other services. To ensure nonresponse bias, the mean difference test between early and late responders (Armstrong and Overton, 1977) shows no significant differences in the number of employees (Diff. = 42, t = 0.838, p = 0.799), firm’s age (Diff. = 2.54, t = 0.191, p = 0.057), industrial sector composition (Manufacture-Diff. = 0.001, t = 0.02, p = 0.982), informant age (Diff. = 0.748, t = 0.851, p = 0.198) and gender (Diff. = 0.019, t = 0.334, p = 0.631).

The construct measurements were adapted from prior studies on OI dimensions (Godkin and Allcorn, 2008; Liao et al., 2008; Sull, 1999; Gal, 2006) and BDA adoption (Tu, 2018; Verma et al., 2018), using a 7-point Likert scale (see  Appendix 1). OI was modelled as a Type II second-order construct, first-order reflective and second-order formative, consistent with its conceptual definition (Godkin and Allcorn, 2008). This formative specification reflects the assumption that OI is causally formed by distinct, non-interchangeable dimensions. The archetype of department leader power followed Siggelkow and Rivkin’s (2005) five organizational configurations, based on autonomy, pre-screening authority, agenda control and veto power, and was measured using a five-point semantic differential scale (see  Appendix 1). All instruments underwent face validity assessment by two experts and were tested through a pilot study to refine wording, ensure clarity and relevance and evaluate initial construct reliability. The questionnaire was translated into Spanish using the back-translation method. Construct validity was assessed with the partial least squares structural equation modelling (PLS-SEM) algorithm in SmartPLS (Ringle et al., 2022). The moderation was tested using the product indicator approach, which is appropriate for scale-based moderators such as the archetype of department leader power.

Following the recommendations of Podsakoff et al. (2003), independent and dependent variables were separated, and marker variable items were inserted as task distractions to reduce mental associations. A pilot test with 34 participants, demographically similar to the final sample (mean age 31.7 years, 50% male, 4.6 years of tenure), ensured question clarity. Respondents were informed of anonymity and confidentiality to reduce sensitivity bias. Harman’s single-factor test showed that the largest variance explained by a single factor was 23.38%, below the 50% threshold. The highest variance inflation factor was 2.339, below the 3.3 threshold, indicating common method bias (Kock and Lynn, 2012). The marker variable technique was applied using a theoretically unrelated construct (Lindell and Whitney, 2001; Simmering et al., 2015). After re-estimating the model with the marker variable, no significant changes appeared in path estimates, confirming that common method bias was not a concern.

First, the measurements were validated for composite reliability, with a minimum CR of 0.791, which surpasses the accepted threshold of 0.7 (Hair et al., 2017). Likewise, to assure convergence validity, the average variance extracted (AVE) values are between 0.521 and 0.752, which are over the desirable 0.5 (Hair et al., 2017).

To access discriminant validity, the cross-loading estimations (see Table 2) show that each loading item construct exceeds the loading on the other construct and surpasses the recommended 0.7 loading, or as Hair et al. (2017) suggest, loadings between 0.4 and 0.7 may be acceptable if they do not enhance CR and AVE values appreciably, as in this study.

Furthermore, discriminant validity is assured with the Fornell–Larcker criterion, where the square root of the AVE exceeds the correlation of each construct with any other construct. In the same way, the HTMT criteria show values from 0.024–0.684, which are below the 0.9 threshold (Hair et al., 2017) (see Table 3).

To test the model, PLS-SEM and bootstrap path estimations (5,000 subsamples) were conducted using SmartPLS software version 4.1.0.0 (Ringle et al., 2022). Prior research recommends PLS-SEM for small samples and complex models that include moderators and second-order constructs (Hair et al., 2017).

The model defines OI as a second-order formative construct consisting of insight, action and psychological inertia, based on Godkin and Allcorn (2008). Insight inertia significantly contributes (β = 0.3954, t = 22.85, p < 0.001), supporting Hypothesis H1. Action inertia also contributes significantly (β = 0.4150, t = 21.46, p < 0.001), supporting Hypothesis H2. Psychological inertia demonstrates the strongest effect (β = 0.4241, t = 23.64, p < 0.001), confirming Hypothesis H3 (see Table 3).

H4 posits that the OI second-order construct relates to adopting BDA. The estimated results reveal that the association between OI and BDA adoption is negative and significant (β = −0.2137, t = 2.99, p < 0.01), thereby supporting H4 (see Table 4). In addition, it was proposed that the department leader’s power positively moderates the relationship between OI and BDA adoption. This interaction was positive and significant (β = 0.1343, t = 2.13, p < 0.05), supporting H5 (see Table 4).

Figure 1 illustrates all the proposed model relationships with the standardized coefficients and significance, demonstrating that the conceptual model is fully supported.

This study complements the quantitative results through semi-structured interviews with IT professionals, applying the Theories-in-Use approach to generate practitioner-informed insights that support theoretical hypotheses (Zeithaml et al., 2020). The interview guide includes open-ended questions aligned with the study constructs. Interviewers explain the objectives at the beginning and maintain neutrality throughout the sessions. The research team applied purposeful sampling to include professionals with diverse managerial experience. The sample consists of 12 IT professionals (83% male), with a mean age of 39.7 years, 17 years of professional experience and 13.7 years in IT roles. All participants work in global operations across the USA and Latin America (see  Appendix 2. Participant characteristics). Each interview took place remotely and individually, with durations ranging from 15 to 40 min, during the third quarter of 2024. Thematic analysis guided the interpretation. Co-authors conducted independent coding of the transcripts and resolved discrepancies through structured comparison.

Insight inertia in our model refers to the time lag between technological changes and management awareness. Managers often become aware of the need to implement new technologies like BDA after significant delays, sometimes only when external pressures or internal failures highlight the necessity for change:

Competitive pressures often drive awareness and BDA adoption. When organizations face competition or market changes, they are more likely to adopt new technologies to maintain their competitive edge:

As argued in this study, there is a superficial understanding of BDA. Many managers are vaguely aware of BDA but lack a deep understanding of its concepts and practical applications:

One explanation for the superficial understanding is that participants recognize that implementing BDA requires specific skills often lacking within organizations. This gap requires hiring new talent or training existing employees to develop the required expertise. It often requires creating specialized roles or departments focused on data analytics to bridge the skills gap:

Although managers recognize the benefits of BDA, their superficial understanding creates a need to justify investing in it, which requires strong business cases. Without clear, demonstrable benefits, it is difficult for managers to make decisions about such investments:

The model proposes that action inertia refers to the speed with which management responds to BDA adoption. Many participants mentioned that BDA adoption is usually slow and bureaucratic, often requiring multiple levels of approval, and that day-to-day operations are a priority, which stifles innovation:

Another characteristic of action inertia is the influence of the firm′s culture. Participants highlighted that long-standing hierarchical structures and traditional mindsets significantly impede the adoption of new technologies. Bureaucracy, compensation systems and a lack of willingness to invest create a substantial barrier to adopting BDA:

Management leadership’s role can be a source of inertia; management with reactive leadership only responds to immediate needs or pressures, slowing the adoption process:

On the contrary, proactive leadership can overcome inertia and accelerate BDA adoption. It involves a proactive stance, guiding and supporting BDA projects, ensuring alignment with company goals and fostering an environment conducive to innovation:

The responses show a consensus that there is always a degree of psychological inertia when implementing any technology, indicating the presence of this form of OI. This is specifically because of a strong preference for existing tools and methods, leading to resistance to adopting new technologies. Participants are more comfortable with traditional tools and exhibit territorial behavior over their data:

Individuals’ resistance to new technologies is due to the fear that they may expose inefficiencies or lead to job losses. Perceived threats to job security cause fear of transparency and accountability, making individuals reluctant to adopt BDA:

The responses agreed that centralizing power at the CEO or top executive level is necessary to drive BDA adoption. This would ensure a unified vision, quicker implementation and consistent support across departments. This suggests the moderating effect of power centralization at the CEO′s level, reducing OI:

However, centralization can pose challenges because it can drive initial implementation. Still, it can also lead to resistance and inefficiency if lower management and departments are not adequately involved:

Departmental leaders are also determinants of effective BDA adoption. Their influence on their teams and their understanding of technology can significantly impact the success of such projects:

The power strategy of centralization or decentralization at the CEO level and department leaders may need to evolve over time. Initially, centralizing is needed to establish a strong foundation, followed by gradual decentralization to adapt to departmental requirements for adopting BDA, which is seen as effective:

Finally, the conceptual model was presented to the participants to gather their opinions. The responses indicate unanimous agreement with the conceptual model, and participants affirm the model’s validity, expressing their concurrence with its core principles and structure:

OI is a significant theme, and recognizing inertia is crucial for the model and aligns with the hypotheses:

Participants used their practical experiences to support their views on the model, and they provided examples to illustrate the model’s relevance and applicability in real-world scenarios:

To gain more insights, participants were asked, “Is there anything you would change and/or add?” Some suggested nothing else:

Other participants suggested additional factors that could lead to further studies, like variations in the firm structure, egos, managers’ experience in technology and interconnection between inertia dimensions that confirm the second-order dimension of OI proposed in the model:

One interesting insight is that adoption is seen as an ongoing process rather than a one-time event; respondents recognize that even after initial adoption, organizations might revert to inertia and need to re-engage with the model to maintain progress:

In conclusion, this qualitative study provides valuable insights into the relevance of the model and its relationships, offering a meaningful explanation for BDA adoption in real-world practice. Finally, as recommended in qualitative research, this study ensured rigor through trustworthiness checks, including credibility, transferability, dependability and confirmability (Zeithaml et al., 2020). Credibility is established as participants consistently validate and accept the proposed model, confirming its accuracy in representing their experiences. Transferability is addressed by including a wide range of firms from different countries and contexts across 12 Latin American countries, the US, and other regions globally. Dependability is demonstrated through participants’ stories, which relate to current firm experiences and past experiences in other firms. Confirmability is addressed by including exact quotes from participants in the text and providing independent analysis by the co-authors.

This study contributes to theory by introducing a validated model that links OI to BDA adoption. Unlike prior research focused on individual-level factors (Yu et al., 2022; Shahbaz et al., 2019), this research conceptualizes OI as a second-order formative construct comprising perceptual, action and psychological dimensions (Godkin and Allcorn, 2008). This multidimensional structure extends the inertia perspective by identifying internal barriers that delay strategic alignment with BDA capabilities (Mikalef et al., 2021; Grover et al., 2018).

Second, including departmental leadership power as a moderator clarifies how authority structures condition the relationship between inertia and innovation adoption. Higher departmental power intensifies the negative effect of OI on BDA adoption, aligning with leadership and organizational change frameworks (Siggelkow and Rivkin, 2005; Damanpour and Schneider, 2006; Alkayid et al., 2022).

Third, the analysis distinguishes BDA from traditional IT by emphasizing its role as a service-oriented technology that generates resistance when perceived as disconnected from core operations (Mikalef et al., 2021; Hung et al., 2020). Qualitative findings indicate that action inertia emerges from reactive management and performance-driven decision-making, reinforcing challenges that service-based technologies pose’ to organizational routines and structures (Gopal et al., 2022; Brown, 2023; Davenport and Bean, 2023).

This research identifies the practical implications of its findings. To overcome OI, it is necessary to mitigate each of its dimensions. To address perceptual inertia, the study proposes training for executives and other staff to understand the benefits and applications of BDA. Similarly, action inertia manifests as delays in decision-making. Therefore, companies should incorporate BDA implementation into their strategic planning.

Furthermore, psychological inertia is evident, particularly associated with fears of sharing information that could be judged based on performance and the change of processes. The research suggests that managers should be transparent about the effects of not sharing information, as opposed to acknowledging that this information may differ from the past, without fear of reprisals. In this context, the department leader’s power is crucial to overcoming OI, making their cooperation essential. Hence, from the moment of their hiring, leaders should possess flexibility to adapt to changes, as well as IT experience and knowledge.

This study has a societal impact by addressing organizational barriers to digital transformation in Latin America, where productivity gaps and inefficient services persist due to low technological adoption. It identifies OI as a barrier to BDA adoption and offers strategies to overcome internal resistance in firms. Societies that build data-driven capabilities improve decision-making, efficiency and innovation. Enhancing organizational efficiency and productivity strengthens competitiveness and raises the overall quality of life.

Improved BDA adoption in Latin American firms supports Sustainable Development Goal (SDG) 9 by strengthening innovation and infrastructure in under-digitized sectors. It also advances SDG 8 by encouraging data-driven practices that enhance competitiveness and job quality.

This research has no significant limitations, and the results are consistent. First, participants in the qualitative study highlighted other factors, such as IT experience and the department leader’s knowledge, that can influence overcoming OI and should be further studied.

Second, the focus of this study on the organizational level as the unit of analysis, along with its specific theoretical framework, limits the exploration of leader attributes, such as directly influencing or mediating dimensions of OI, that future research could address through alternative theoretical perspectives and levels of analysis.

Third, participants described a dynamic interaction between OI and BDA adoption, involving iterative transitions between inertia types and partial adoption. Further research could explore whether this process is iterative or resolved in a single stage.

Fourth, the qualitative study found few differences across countries, but further research should examine cultural influences to strengthen external validity.

Fifth, the qualitative section involves a small, non-representative sample, limiting its ability to support statistical generalization. However, the findings contribute to a deeper contextual understanding of the quantitative result.

Sixth, the study examines BDA adoption at the firm level, which limits understanding of how personal-level factors, such as user attitudes, interact with OI. Future research could integrate individual-level frameworks, such as TAM or UTAUT, to provide a more comprehensive view.

Erratum: It has come to the attention of the publisher that the peer‑review history dates for the article Palomino-Tamayo W, Romero Capcha E (2026), “The effect of organizational inertia on the adoption of big data analytics”. RAE: Revista de Administracao de Empresas, Vol. 66 No. 1 pp. 43–65, doi: Link to The effect of organizational inertia on the adoption of big data analyticsLink to the cited article were incorrectly published.

At publication, the Received and Accepted dates reflected the manuscript’s submission history within the publisher submission system rather than the article’s full peer‑review history prior to transfer. The correct dates are Received 23^rd July 2024 and Accepted 24^th September 2025.

These errors were introduced during the publication process, for which the publisher apologises.

The authors gratefully acknowledge the reviewers and participants of the 2024 BALAS Conference, which strengthened the development of this manuscript. This study received the BALAS Presidents’ Award for Best Academic Paper (2024) from the Business Association of Latin American Studies (BALAS).