From voluntary adoption to exogenous shocks in management accounting research

1 Introduction

Management accounting research has produced a substantial body of evidence on how management control systems (MCS) are designed, implemented, and used in organizations (Chenhall, 2003; Malmi & Brown, 2008). A wide range of studies - particularly those using laboratory and field experiments - have documented the behavioral consequences of specific control mechanisms, such as performance-based pay, feedback systems, and target setting (Margolin et al., 2024; Matejka et al., 2024; Bouwens & Kroos, 2017; Casas-Arce et al., 2017; Lourenço, 2016; Hannan et al., 2013; Casas-Arce & Martínez-Jerez, 2009). This literature has contributed to a greater understanding of how MCS influence decision-making, coordination, and performance.

In contrast, considerably less is known about how MCS are adopted when their implementation is driven by pressures outside the firm’s control. The majority of empirical studies in the field examine settings in which firms voluntarily choose control systems in response to internal conditions, such as strategy, size, or perceived uncertainty (Osma et al., 2018; Malagueño et al., 2018; Lisi, 2015; Rodrigue et al., 2013; Gond et al., 2012; Perego & Hartmann, 2009; Sandino, 2007; Slagmulder, 1997). This emphasis on endogenous adoption, while theoretically grounded in contingency-based approaches (Luft & Shields, 2014; van der Stede, 2011; Jermias, 2008), poses challenges for causal inference. When MCS are tailored to firm-specific needs, it becomes difficult to determine whether observed outcomes - such as innovation, efficiency, or sustainability performance - are caused by the system or by the underlying conditions that led to its adoption.

I argue that management accounting research should pay greater attention to exogenous sources of variation in MCS adoption. Regulatory mandates, environmental shocks, and institutional reforms frequently force firms to implement control systems they might not otherwise choose. These contexts create opportunities to study how firms respond to external pressures - settings in which identification is stronger, and where causal claims are more credible. By leveraging natural experiments and quasi-experimental designs, we can examine not only whether control systems “work,” but also how and under what conditions they are adopted, implemented, and ultimately effective. Recent studies highlight the promise of this approach. Labro and Stice-Lawrence (2020) use instrumental variables to compare accounting system updates in U.S. hospitals. They show that economically motivated updates improve performance, whereas coercively imposed updates do not. These findings challenge the implicit assumption that all MCS implementations are equally effective, regardless of the conditions under which they occur. Similarly, in Antonini and Gomez-Conde (2024), we exploit the staggered implementation of the EU (European Union) Directive 2014/95/EU to examine regulatory-driven adoption of environmental MCS. Results show that the directive induced widespread EMCS adoption, but also had short-term negative effects on environmental innovation - effects not typically observed in studies of voluntary adoption.

The argument is not that causal inference should replace other research traditions in management accounting, but that it should play a more central role - particularly in studies of MCS adoption. Understanding how firms respond to exogenous shocks is essential for evaluating the effectiveness of control systems, for informing regulation, and for extending theories of organizational change and adaptation. Moreover, regulators, standard setters, and practitioners increasingly demand evidence not only of association, but of cause and effect (Christ, 2013; Bonner & Sprinkle, 2002; Libby & Lipe, 1992).

There is no shortage of identification opportunities. Regulatory shifts - such as the EU Corporate Sustainability Reporting Directive (CSRD), new tax transparency obligations, whistleblower protections, or changes in labor or environmental regulation - introduce variation that can be leveraged for causal inference. Environmental shocks, such as floods, wildfires, and droughts, offer further possibilities. These contexts allow researchers to address long-standing questions with greater internal validity and direct relevance to policy and practice. As Christensen (2020) argues, narrow-sample studies that exploit institutional detail often enable stronger identification, more precise mapping between empirical analysis and conceptual arguments, and ultimately, greater generalizability across related settings - particularly when broad-sample designs hide the mechanisms at play. Comparable developments can be observed in Brazil, where the ESG (Environmental, Social, and Governance) agenda and CVM/IBGC (Comissão de Valores Mobiliários/Instituto Brasileiro de Governança Corporativa - Brazilian Securities and Exchange Commission / Brazilian Institute of Corporate Governance) resolutions introduced from 2020 onwards have heightened disclosure requirements on sustainability, diversity, and governance. These measures have encouraged the incorporation of non-financial indicators into management control systems, aligning them with sustainability and social responsibility metrics and influencing CFO (Chief Financial Officers) practices and transparency debates in the Brazilian context. More recently, business transparency itself has emerged as a global governance and strategic factor. In Brazil, the Equal Pay Law No. 14,611 (2023) requires companies with more than 100 employees to submit salary and remuneration transparency reports - most recently by August 31, 2025. These reports, publicly released by the Ministry of Labor and Employment, aim to expose gender wage gaps and reinforce accountability. Beyond HR (Human Resource) compliance, salary transparency now directly affects corporate reputation, talent retention, and CFO decision-making, highlighting how governance, disclosure, and financial strategy are increasingly interrelated.

2 Evidence on the Effects of MCS

Management accounting research has generated a substantial body of evidence on the behavioral effects of management control systems (MCS), particularly through experimental designs. Laboratory studies have provided insights into how individual control elements - such as incentive schemes, target setting, or feedback - affect effort, cooperation, and performance. For example, Libby and Lipe (1992) showed that bonus structures shape risk-taking behavior; Sprinkle (2003) reviewed a wide array of experiments on incentives and decision-making; and Christ (2013) examined how intentions and reciprocity interact with control mechanisms.

Field and quasi-field experiments have extended this work into applied contexts. Friebel et al. (2017), using a field experiment in a retail chain, found that team-based performance incentives can significantly increase productivity. Hannan et al. (2019) showed that the provision of relative performance information affects effort allocation in multitask environments. Frimanson et al. (2021), in a one-year field experiment, documented that the frequency of performance evaluations can affect physiological stress markers, with longer evaluation cycles linked to elevated cortisol and thyrotropin levels. These studies benefit from strong internal validity and offer important causal insights into how specific MCS features influence behavior - once the systems are in place.

Recent contributions have reinforced and diversified these insights. For instance, meeting or exceeding externally communicated financial expectations has been shown to enhance employee effort and commitment by signaling performance norms (Hilary et al., 2025). Other studies explore the motivational effects of competitive reward structures, showing that exposure to relative performance feedback can alter perceptions of fairness and personal belonging within organizations (Nieken & Ressi, 2025; Jiang & Nair, 2025). Feedback salience and reporting structure also influence task engagement and interpersonal coordination, particularly when designed to align with team-level goals (Casas-Arce et al., 2017). Work in the field has further investigated how managerial control interacts with strategic uncertainty and organizational learning (Langfield-Smith, 1997). For example, examining how control systems affect strategic responsiveness (Gomez-Conde et al., 2023; Sandino, 2007), while others show that over-tightened controls under conditions of ambiguity can undermine learning and intrinsic motivation (Brown et al., 2022).

Collectively, these findings demonstrate that MCS influence behavior in systematic and often predictable ways. However, most of this research presumes that control systems are already in place and were adopted voluntarily. The causal mechanisms that drive their initial adoption - particularly in settings shaped by external mandates - remain less well understood. This observation motivates the next section.

3 Strengthening the Causal Agenda in Management Accounting

Advancing a causal research agenda in management accounting requires more than identifying whether control systems are associated with desired outcomes. It demands an explicit focus on the conditions under which those systems are adopted, the forces that shape their implementation, and the mechanisms through which they affect behavior and performance. While causal identification has gained traction in the field - particularly through laboratory and field experiments studying MCS effects - research has not kept pace in examining the antecedents of control system adoption when firms are exposed to external, often non-discretionary, pressures.

To date, much of the causal evidence in management accounting has focused on what happens after control systems are in place. A growing stream of experimental and quasi-experimental research has examined how feedback design, goal setting, and incentive structures shape motivation and performance. For example, recent evidence shows that meeting or exceeding externally communicated financial expectations can lead to greater employee effort and organizational commitment, as firms use performance targets to implicitly coordinate behavior (Bouwens et al., 2024). Similarly, reward systems that highlight relative performance benchmarks have been found to trigger competitive responses and shape perceptions of fairness and hierarchy (Nieken & Ressi, 2025; Jiang & Nair, 2025).

This line of research also highlights how control mechanisms operate through psychological and social pathways. Performance comparisons affect not only outcomes but also employee identity and engagement - especially when individuals anchor their expectations on the performance of relevant peers. Evidence from both lab and field settings underscores how the salience, frequency, and transparency of feedback can influence attention allocation, risk-taking, and cooperation. In team settings, for instance, tailored dashboards and feedback tools have been shown to improve information sharing and coordination (Casas-Arce et al., 2017; Friebel et al., 2017).

Beyond incentive design, field studies have explored how shifts in monitoring intensity and reporting frequency affect discretionary effort, learning, and prosocial behavior. Experiments embedded in organizational environments reveal that tight budgetary controls, while often effective in aligning behavior, may backfire under high uncertainty or when perceived as intrusive (Brown et al., 2017). Others have shown that introducing new systems in response to strategic uncertainty can enhance performance when complemented by appropriate accountability structures (Abernethy & Mundy, 2014).

Together, these findings offer rigorous insights into how MCS influence behavior, but they often take system adoption as given. Less attention has been paid to why and how firms adopt these systems, particularly when the trigger is external. It is precisely in those settings - where control implementation is not discretionary - that causal designs can illuminate variation in adoption patterns, internal resistance, and eventual system efficacy.

Methodologically, researchers have access to a rich toolkit to pursue such questions. Difference-in-differences designs are especially effective when adoption is staggered across jurisdictions or firm types. Regression discontinuity approaches are applicable in threshold-based regulatory contexts, and instrumental variables can leverage sources of plausibly exogenous variation - such as geographic exposure to climate risk or shocks to enforcement intensity. For example, Antonini and Gómez-Conde (2024) show how environmental control systems adopted under EU regulation affected innovation outcomes, revealing internal trade-offs that might remain hidden in studies of voluntary adoption.

More broadly, causal designs focused on exogenous adoption enable researchers to test when and why MCS deliver their intended outcomes - or fail to. Such insights are especially relevant in the context of emerging regulatory regimes in sustainability, ESG reporting, and digital compliance, where adoption is externally triggered, but effectiveness depends on internal integration. This line of research responds directly to longstanding calls for greater policy relevance and theoretical depth in management accounting research (Luft & Shields, 2014; van der Stede, 2011).

To extend this agenda, scholars must integrate causal analysis of adoption with the behavioral insights from the broader MCS literature. Hybrid designs - linking natural experiments on adoption with experimental variation in implementation - can help uncover which combinations of context and control design deliver the most robust outcomes. This approach opens new possibilities for explaining when and how control systems can adapt organizations to rapidly changing environments, and for articulating a more dynamic theory of MCS that accounts for institutional complexity, temporal trade-offs, and power asymmetries. At the same time, it is important to recognize that methodological traditions shape how causality is addressed. In some regions, such as Brazil, management accounting research has largely relied on qualitative case studies or survey-based approaches (often using PLS - Partial Least Squares), with experiments relatively rare and archival research less common. Each of these approaches provides distinctive insights but also imposes limits on causal inference. A more balanced methodological portfolio would allow the field to triangulate evidence across designs and produce more robust conclusions about both the adoption and effects of MCS.

The theoretical implications are significant. If adoption mechanisms affect how systems are used, then models that treat MCS as static design choices risk overlooking how power, legitimacy, and internal resistance shape control outcomes. More fundamentally, the field must revisit the assumption that control systems are primarily strategic responses to internal needs. In many settings - especially under new ESG and climate mandates - they are no longer fully discretionary. Causal research on exogenous adoption provides the tools to engage with this reality and to develop more robust, policy-relevant theories of control in complex environments.

4 Concluding Remarks

The study of management control systems has benefited greatly from causal approaches focused on system design and behavioral consequences. However, the conditions under which these systems are adopted - especially when adoption is externally mandated - remain underexplored. As regulatory, environmental, and institutional pressures increasingly shape firms' control environments, management accounting research must expand its focus to understand how organizations respond to such forces. Leveraging exogenous variation for causal identification offers a powerful path forward, one that can strengthen both the practical relevance and theoretical foundations of the field.

Incorporating causal approaches into management accounting research not only broadens the analytical scope but also demands specific capabilities. Researchers must be equipped to handle empirical designs that rely on regulatory variation, institutional detail, or external shocks - contexts that often require familiarity with econometric tools, archival methods, and experimental logic. These skills are not uniformly developed across the field and may require deliberate investment in training and collaboration. Moreover, studies of this nature face practical constraints: limited access to granular data, uneven enforcement environments, and the risk of misinterpreting institutional effects. Acknowledging these challenges is essential - not to discourage such work, but to ensure that its execution is grounded, its assumptions transparent, and its conclusions robust.

Taken together, these considerations suggest that a more deliberate and methodologically diverse approach to studying control system adoption can help the field better engage with the complexities of contemporary organizational environments.

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  This is a bilingual text. This article has also been translated into Portuguese, published under the DOI https://doi.org/10.1590/1808-057x20259051.pt

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• Academic Editor-in-Chief:
  Andson Braga de Aguiar https://orcid.org/0000-0003-4034-4134

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Publication Dates

• Publication in this collection
  03 Nov 2025
• Date of issue
  2025