A cognitive bias awareness matrix for enhancing ERP Decision-Making in entrepreneurial firms

Abstract

Purpose

The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

Design/methodology/approach

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

Findings

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

Research limitations/implications

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

Practical implications

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

Social implications

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

Originality/value

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

Citation

Davidson, M.W., Parnell, J. and Davenport, S.W. (2024), "A cognitive bias awareness matrix for enhancing ERP Decision-Making in entrepreneurial firms", Journal of Ethics in Entrepreneurship and Technology, Vol. 4 No. 1, pp. 38-61. https://doi.org/10.1108/JEET-05-2024-0011

Introduction

In an era of pivotal digital transformation, adopting enterprise resource planning (ERP) systems in entrepreneurial firms has become vital. While ERP systems offer substantial benefits regarding operational efficiency and business process integration, the decision-making process for their adoption in small and medium-sized enterprises (SMEs) is complex and often compromised by cognitive biases such as temporal discounting and optimism bias. These biases, prioritizing immediate rewards and underestimating potential risks, can influence ERP adoption decisions, leading to inadequate planning and resource allocation. The primary purpose of this study is to address a critical gap in the ERP implementation process for SMEs by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Research has extensively explored the technical and operational aspects of ERP implementation in large enterprises (Morris, 2011; Parthasarathy and Padmapriya, 2023). However, little is known about how cognitive biases affect ERP decision-making in SMEs (Acciarini et al., 2021), which often lack the resources and expertise of larger firms. Studies indicate that up to 75% of ERP implementations in SMEs fail to meet expectations or are abandoned entirely (Ghobakhloo et al., 2012). This shortcoming highlights the need for tools and frameworks to help SMEs recognize and mitigate these biases to make more informed decisions. We address this gap by focusing on two questions:

Q1.

How can SMEs effectively identify and mitigate cognitive biases, such as temporal discounting and optimism bias, in ERP decision-making?

Q2.

What strategies can be employed to enhance the long-term success and sustainability of ERP implementations in SMEs?

We believe SMEs with higher awareness and proactivity regarding cognitive biases will achieve more successful ERP implementations compared to those with lower awareness and proactivity. This proposition is tested by developing and applying a Cognitive Bias Awareness Matrix, which allows SMEs to self-assess their decision-making processes.

Our assessment of existing literature follows an evidence-based process consistent with a critically appraised topic (Center for Evidence-Based Management, 2017). We enhance decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making. This matrix helps identify biases and encourages the implementation of strategies to mitigate their effects. Through the diligent use of this matrix, SMEs can develop a more strategic and informed approach to ERP system selection and implementation, which is crucial for achieving operational efficiency and competitive advantage in the digital age. The structured approach provided by the matrix ensures that SMEs can align decision-making processes with immediate operational needs and long-term business goals, ultimately leading to more successful ERP outcomes.

Examining ERP implementation through the lens of cognitive bias is a novel approach to understanding a common challenge for SMEs. This paper adds value by integrating behavioral economics into ERP decision-making research. It simultaneously offers SMEs a structured tool–the Cognitive Bias Awareness Matrix–as a practical tool to improve their ERP adoption processes, leading to better outcomes and competitive advantages. The following section reviews the literature on ERP systems in SMEs and the impact of cognitive biases on decision-making. This discussion is followed by a detailed explanation of the research methodology used to develop the Cognitive Bias Awareness Matrix. The subsequent section presents the results and discusses the implications of the findings. We conclude with recommendations for theory and practice, study limitations and future research directions.

Review of the literature

ERP systems are designed to streamline and integrate multiple business processes and have been a cornerstone for large enterprises for many years (Jacobs and Weston, 2006; Morris, 2011; Parthasarathy and Padmapriya, 2023). However, the implications and application of ERP systems in SMEs remain an under-researched domain, where disparities in success rates between large enterprises and SMEs are pronounced due to factors such as high costs, complex training needs and reluctance toward adoption (Kaith, 2010; Momoh et al., 2010; Hidalgo Nuchera et al., 2011; Battleson and Mathiassen, 2020). SMEs often need help with the mismatch of ERP solutions initially designed for larger corporations, leading to high failure rates and a lack of tangible ROI (Katuu, 2021).

The decision-making process for ERP adoption in SMEs is influenced by cognitive biases (Acciarini et al., 2021), notably temporal discounting and optimism bias. Temporal discounting leads decision-makers to prioritize immediate, smaller gains over potentially greater future benefits. This bias often guides the selection of ERP solutions that promise immediate cost savings but may necessitate costly future modifications due to their lack of scalability (Frederick et al., 2002; Dittmar and Bond, 2010). Specifically, temporal discounting causes decision-makers to undervalue long-term outcomes, focusing instead on short-term achievements and the immediate impact of their choices.

In contrast, optimism bias results in underestimating potential risks and overestimating likely benefits, which can lead to insufficient planning and resource allocation (Caponecchia, 2010; Hardisty and Weber, 2009). This bias affects the decision-making process by creating overly favorable expectations of project timelines, costs and outcomes, often disregarding the realistic challenges and hurdles that may arise.

While both biases can adversely affect decision-making, they operate through distinct mechanisms. Temporal discounting changes the weight given to future outcomes, whereas optimism bias skews the assessment of the outcomes. Understanding these differences is crucial for mitigating their impacts on strategic decisions (Khattar and Gallo, 2023) in business environments.

Information asymmetry exacerbates the impact of cognitive biases, particularly in scenarios where ERP vendors or consultants possess more or superior information than SME decision-makers. This asymmetry can skew decisions toward options that may not align with the best interests of the SME, influencing both the selection and implementation phases of ERP systems (Lützkendorf and Speer, 2005; Zhang et al., 2022). The implications of this disparity extend beyond initial choices, affecting ongoing decision-making throughout the lifecycle of ERP projects. For instance, vendors might withhold or downplay information about the long-term costs of updates and maintenance or the complexities involved in integrating the ERP system with existing processes. This can lead to a continuous cycle of misinformed decisions that favor immediate solutions without considering long-term sustainability.

Enhancing a connection to one’s future self can mitigate the negative impacts of temporal discounting and optimism bias. Techniques that help visualize the future state of the business post-ERP implementation or strategic foresight exercises can strengthen this connection, promoting decisions that align with long-term organizational goals (Bartels and Urminsky, 2011; Hershfield, 2011).

To counteract the effects of cognitive biases, SMEs could benefit from structured decision-making frameworks that emphasize long-term planning and risk assessment. Training programs that foster future-oriented thinking and critical evaluation of ERP benefits over time could support these aims. Additionally, establishing transparent relationships with ERP consultants can help mitigate the risks associated with information asymmetry.

Understanding and addressing cognitive biases like temporal discounting and optimism bias within the ERP decision-making process is crucial for SMEs. Implementing strategies that enhance the connection to the future self and reduce information asymmetry enable SMEs to make more informed decisions that align with their long-term business objectives.

Awareness and proactivity in addressing cognitive decision-making biases

We recognize the impact of these biases and develop a matrix that assists SMEs in evaluating their awareness and proactivity toward temporal discounting and optimism bias in ERP decision-making. This research extends the work of Jacobs and Weston (2006) on the importance of ERP in SMEs and builds upon the behavioral economics insights provided by Dittmar and Bond (2010) and Kahneman (2003). SMEs face unique challenges in implementing ERP systems, making traditional optimization approaches inadequate. The complexity arises from several factors:

• dynamic and unpredictable environments – SMEs operate in rapidly changing environments with numerous interconnected variables, making it difficult to maintain a static optimization function throughout the implementation process;

• qualitative factors and limited information – crucial elements like organizational culture and employee readiness are often qualitative and challenging to quantify; SMEs frequently operate with limited information and resources, leading to bounded rationality in decision-making; and

• non-linear outcomes and cognitive biases – ERP implementation outcomes can be non-linear and emergent, with small changes leading to significantly different results.

Moreover, cognitive biases like temporal discounting and optimism bias dynamically influence decision-making in ways that are difficult to predict or parameterize.

A behavioral economics approach addresses these challenges and offers several benefits: Adaptive decision-making framework, integration of qualitative factors, acknowledgment of bounded rationality, capability to capture emergent phenomena and focus on continuous improvement. While traditional optimization methods may fall short, our behavioral economics approach provides a pragmatic and flexible alternative for ERP implementation in SMEs. By focusing on improving decision-making processes rather than optimizing fixed parameters, we offer SMEs a more robust and adaptable strategy for navigating the complexities of ERP implementation in real-world scenarios. This approach complements quantitative methods where appropriate, enhancing their effectiveness by improving the overall decision-making context. Ultimately, our goal is to equip SMEs with tools that lead to better outcomes in the dynamic and often unpredictable landscape of ERP implementation.

The Cognitive Bias Awareness Matrix (see Table 1), designed for ERP decision-making in SMEs, comprises four distinct quadrants, each representing different levels of awareness and proactivity in addressing cognitive biases, such as temporal discounting and optimism bias. This matrix is pivotal for SMEs to navigate these biases effectively, ensuring their decisions align more with long-term benefits and realistic assessments of ERP implementation challenges. Table 2 is an enhanced “stylized” Cognitive Bias Awareness Matrix that provides a comprehensive, data-driven view of how cognitive biases impact ERP implementation across different levels of awareness and proactivity. It incorporates specific biases, their effects, mitigation strategies and key performance indicators (KPIs) for each quadrant. The matrix highlights the critical role of managing optimism bias and temporal discounting in ERP implementations.

The Cognitive Bias Awareness Matrix can exhibit various patterns derived from decision-making contexts. These include:

• Quadrant Integration: Organizations may demonstrate behaviors from multiple quadrants, necessitating a blended approach for strategic alignment with awareness and proactivity.

• Sector-Specific Adaptations: Different industries may require customized matrices, reflecting sector-specific cognitive biases or unique ERP challenges.

• Temporal Adjustments: Over time, SMEs may shift their orientation within the matrix, evolving from passive states to more proactive mastery as they grow in ER capabilities.

• These diverse patterns facilitate a nuanced understanding of how cognitive biases shape decision-making across different organizational contexts.

The matrix demonstrates how high awareness and proactivity (Quadrant 3) lead to superior outcomes, while low awareness (Quadrants 1 and 4) results in significant challenges and suboptimal performance. The success patterns observed in Quadrant 3 of this matrix resemble the strategies used by Simon’s (2021) “Hidden Champions” – SMEs that dominate niche global markets. Like these Hidden Champions, organizations in Quadrant 3 demonstrate high awareness and proactivity, effectively managing cognitive biases to achieve superior outcomes. This parallel suggests that the principles of bias management and strategic focus that drive Hidden Champions’ success in their specialized markets can be equally valuable in ERP implementation contexts. For instance, Hidden Champions are known for their long-term orientation, continuous innovation and deep customer relationships – traits align closely with the bias mitigation strategies and KPIs outlined in Quadrant 3. By emulating these characteristics, SMEs implementing ERP systems can potentially achieve the same level of excellence in their digital transformation efforts that Hidden Champions attain in their respective markets.

Moreover, the ability of Hidden Champions to maintain market leadership through focused strategies and adaptability mirrors the success factors in ERP implementation highlighted in Quadrant 3. This connection underscores the universality of effective cognitive bias management and strategic planning across different business contexts, from niche market dominance to successful technology adoption.

By using this matrix, SMEs can better navigate the complex landscape of ERP implementation, identifying their current position and the steps needed to move toward proactive mastery. This tool enables organizations to develop targeted strategies for bias mitigation, ultimately improving their ERP projects’ success rate and value realization.

Quadrant 1: passive unawareness (low awareness, low proactivity)

Businesses in Quadrant 1 exhibit limited awareness of cognitive biases like temporal discounting and optimism bias and limited proactivity in addressing them. This quadrant represents a critical vulnerability in the decision-making processes of SMEs, particularly in the context of ERP system adoption. Users in this quadrant often underestimate the technical capabilities of the ERP system due to a lack of awareness of their cognitive biases. Consequently, information may be inconsistent or unreliable as biases go unrecognized and unaddressed. Service quality is poor because users are not proactively engaging with support services, resulting in low ERP system use due to unrecognized value. Satisfaction is low as users have unmet expectations due to unrecognized biases, leading to minimal benefits realized.

The lack of awareness and proactivity leads to a passive approach, where decision-makers need more information and rely too heavily on intuition, often ignoring the long-term implications and risks associated with ERP implementations (Muscatello and Chen, 2008).

Research by Dittmar and Bond (2010) on consumer impulsivity illustrates how temporal discounting can affect decisions by prioritizing immediate benefits over long-term gains. This bias can severely impact the strategic decisions made by SMEs (Khattar and Gallo, 2023). In the context of ERP, this might manifest as choosing an ERP system based on upfront cost savings rather than considering the total cost of ownership, including maintenance, upgrades and scalability. This quadrant underscores a need for SMEs to understand cognitive biases to avoid costly mistakes that could compromise business stability and growth (Caponecchia, 2010).

Moreover, optimism bias in this quadrant can lead to underestimating the challenges involved in ERP implementation, such as integration complexities or training requirements. This bias is particularly dangerous as it can lead to insufficient planning and resource allocation, significantly disrupting business operations post-implementation. Kahneman (2003) discusses how optimism bias can lead decision-makers to assume best-case scenarios, neglecting necessary precautions and contingency plans. For SMEs in Quadrant 1, moving toward greater awareness and proactive bias management is essential. Initiatives could include training sessions on cognitive biases, consulting with experts during the ERP selection process and implementing structured decision-making frameworks that systematically consider both short-term impacts and long-term outcomes (Jacobs and Weston, 2006).

To progress out of this quadrant, SMEs need to foster a culture of critical evaluation and skepticism, particularly when assessing ERP solutions. Engaging with external consultants or adopting methodologies from more advanced quadrants could help these businesses recognize and address the cognitive biases currently hindering their decision-making processes. This strategic shift is crucial for ensuring that ERP implementations succeed and that the investments in such systems yield the intended operational efficiencies and competitive advantages.

Quadrant 2: informed hesitation (high awareness, low proactivity)

Organizations in Quadrant 2 are characterized by high awareness of behavioral economic factors such as temporal discounting and optimism bias but display limited proactivity in taking corrective actions. Users in this quadrant recognize the system’s capabilities but do not fully leverage them. They acknowledge the quality of information but do not use it effectively, and while they are aware of service quality, they do not act on it. This hesitation leads to modest ERP system use, resulting in mixed satisfaction as awareness does not translate into action and the benefits realized are moderate.

This situation often leads to “analysis paralysis,” where firms recognize potential pitfalls but struggle to translate this awareness into effective operational strategies. This disjunction can result in delayed decisions, missed opportunities and an inability to leverage their insights into tangible benefits (Jacobs and Weston, 2006).

Research suggests that firms in this quadrant can benefit significantly from implementing structured decision-making processes that formalize the steps required to address identified biases (Kahneman, 2003). Doing so may include adopting decision support systems that prompt action and ensure that cognitive insights are integrated into everyday business practices. Additionally, firms could implement regular strategy review sessions focusing on identifying biases and committing to specific actions to mitigate their effects. This approach helps to ensure that the awareness of biases is effectively converted into proactive strategies that improve ERP implementation outcomes (Caponecchia, 2010).

Further, companies in this quadrant might explore partnerships with behavioral strategists or consultants who can assist in designing interventions that specifically target the inertia often found in firms with high awareness but low proactivity. By creating accountability structures and setting clear timelines for action, these firms can move toward a more dynamic and responsive operational model that recognizes biases and actively counters them, thus aligning their strategic initiatives more closely with their long-term business goals. Targeted workshops to enhance understanding of ERP benefits and strategies to overcome biases can help these firms transition from informed hesitation to proactive mastery.

Quadrant 3: proactive mastery (high awareness, high proactivity)

Firms in Quadrant 3 exhibit both high awareness and high proactivity in managing behavioral economic factors, potentially positioning them to achieve the most successful ERP implementations. Users in this quadrant maximize system quality through high awareness and proactive management, optimizing information quality by addressing cognitive biases. Their proactive and informed engagement ensures high service quality, and ERP system use is effective and efficient. As a result, users experience high satisfaction from realistic expectations and proactive actions, realizing maximized benefits through informed strategies.

These businesses understand the range of biases that could affect their decision-making and actively use strategies to counteract these influences. Such firms often use a combination of training, policy-making and adaptive learning processes to continuously refine their approach based on ongoing feedback and new insights (Nah et al., 2001).

The proactive nature of these firms allows them to implement advanced predictive analytics and decision-making frameworks that anticipate and mitigate potential biases before they can impact the business negatively. By doing so, they can maintain a strategic advantage and ensure that ERP systems are implemented smoothly and effectively. These firms also often engage in continuous learning and improvement cycles, applying lessons from past projects to future initiatives, thereby continually enhancing their decision-making processes (Caponecchia, 2010).

To further enhance their effectiveness, companies in Quadrant 3 might focus on scaling these successful practices across their organization. This could involve developing training modules for new employees to instill a consistent approach to bias awareness and management. Companies in Quadrant 3 might also share their successes in industry forums or case studies to establish themselves as leaders in applying behavioral economics to technology adoption. Continuous improvement programs and advanced bias training can help maintain this level of mastery, ensuring that these firms remain at the forefront of effective ERP implementation and management.

Quadrant 4: active misalignment (low awareness, high proactivity)

Quadrant 4 firms are characterized by a paradoxical combination of high proactivity in decision-making with limited awareness of the underlying cognitive biases that may influence these decisions. High efforts are made but may be misdirected due to unrecognized biases. Information efforts are present but possibly skewed, and users actively engage but their efforts are not aligned with actual needs. ERP system use is high but may be ineffective, and this high effort only sometimes results in consistent satisfaction or fully translates into benefits.

This disconnect can lead to vigorous but potentially misguided efforts where executives act without a clear understanding of the cognitive pitfalls that might be at play (Muscatello and Chen, 2008). These firms often benefit from targeted educational programs that raise awareness about specific biases that could undermine their efforts. By integrating cognitive and behavioral training into the professional development of their staff, these organizations can begin to align their proactive nature with informed decision-making strategies. Additionally, implementing feedback mechanisms that track the outcomes of decisions can help these firms identify when biases might influence their actions, thus providing a real-time corrective tool (Dittmar and Bond, 2010).

Moreover, these companies could enhance their decision-making framework by adopting evidence-based management practices, prioritizing data and empirical evidence over intuition or expedience. This approach can help them harness their drive to act, ensuring that their energetic efforts are guided by insights that maximize the effectiveness and sustainability of their ERP systems. Training to realign efforts and mitigate biases is essential to maximize benefits. By advancing out of their current quadrants and adopting best practices from Quadrant 3, SMEs in all other quadrants can significantly enhance their decision-making processes, leading to more successful and effective ERP system implementations.

Thus, for businesses in Quadrant 4, the key to evolving their ERP implementation strategies lies in marrying their dynamic approach to change with a rigorous understanding of cognitive decision-making biases. This synthesis can transform their raw proactivity into a strategic asset that propels action and ensures that such action is deeply rooted in understanding the human and technical factors that drive successful ERP outcomes. By advancing out of their current quadrants and adopting best practices from Quadrant 3, SMEs can significantly enhance their decision-making processes, leading to more successful and effective ERP system implementations.

Each quadrant illustrates varying degrees of readiness and capability in managing the influence of cognitive biases on ERP decisions. By moving toward Quadrant 3, SMEs can significantly enhance their decision-making processes, leading to more successful and effective ERP system implementations. In doing so, SMEs can effectively assess and improve their ERP decision-making approach, considering their awareness and proactivity level in managing cognitive biases. This tool guides them toward the ideal state of high awareness and high proactivity, ensuring a more strategic and bias-informed approach to ERP implementation.

Methodology

Our analysis follows the Problem-Intervention-Comparison-Outcome-Context (PICOC) protocol (Center for Evidence-Based Management, 2017). The PICOC framework is a methodological tool to structure research questions and guide evidence synthesis in various fields, including business research. This framework assists researchers in breaking down and examining the components of their study in a structured manner to enhance clarity and relevance. It assimilates findings on specific business research topics (Melendo Rodríguez-Carmona et al., 2024; Ochoa et al., 2023; Rincon‐Novoa et al., 2022; Tesch da Silva et al., 2020).

The problem describes a specific issue. In the context of this study, the problem is the impact of cognitive biases on ERP decision-making in SMEs. Intervention refers to the measures to address the issue, such as developing and implementing the Cognitive Bias Awareness Matrix. The comparison involves examining the state of decision-making in SMEs that do not use the matrix versus those that do, providing a comparative analysis of the effectiveness of the intervention. Outcome looks at the results or changes that arise from implementing the intervention, specifically how the matrix influences the quality of ERP decision-making and the success rates of ERP implementations. Finally, context considers the environment in which the study is conducted, including the types of SMEs, industry sectors and the economic or regulatory landscapes they operate within.

Using the PICOC framework, our study systematically addresses the research problem, offering a clear pathway for examining the effects of cognitive bias awareness on ERP decision-making processes in SMEs. This structured approach ensures that the research findings are comprehensive and applicable, providing valuable insights directly relevant to SMEs’ specific needs and conditions.

Search strategy and results

The initial search began with the keywords “ERP,” “SMEs,” and “decision-making.” This search yielded 3,751 articles. Additional keywords such as “cognitive biases,” “temporal discounting,” “optimism bias,” “behavioral economics,” and “decision matrix” were included. The PICOC presented in Table 3 includes ten studies that comprise the core of our analysis. The search and results are summarized in Tables 4 and 5.

Table 6 summarizes key findings from ten selected studies focusing on ERP decision-making in SMEs and emphasizing the development of a Cognitive Bias Awareness Matrix. The studies in Table 7 provide an understanding of ERP systems in SMEs, underlining the significance of developing a matrix to assess and improve awareness and proactivity.

Studies 1, 2, 3 and 4 support the idea that integrating a Cognitive Bias Awareness Matrix in ERP strategies can enhance operational success and organizational growth. This facilitates a deeper understanding and mitigation of cognitive biases like temporal discounting and optimism bias.

Discussion

High-quality, informed decision-making involves addressing the unique needs of individual stakeholders and SMEs. The studies highlight that more than owning an ERP system is required for transformation. Integrating a Cognitive Bias Awareness Matrix, which includes an awareness of optimism bias and temporal discounting biases, is vital. This bias, known for leading to overestimated benefits and underestimated challenges, must be recognized and managed to prevent unrealistic expectations and planning failures.

The modern ERP landscape in SMEs is dynamic and demands adaptability and a deep understanding of behavioral economics. Incorporating bias awareness strategies (e.g. mitigating optimism bias using the proposed matrix) is crucial for gaining a competitive edge. The selected studies demonstrate that adopting ERP systems in SMEs is enhanced when paired with bias-aware strategies. This involves a proactive approach to managing cognitive biases and promoting a culture of informed decision-making and agility.

Each study underscores the benefits of integrating cognitive bias principles through the proposed Cognitive Bias Awareness Matrix in ERP strategies. This integration fosters a culture of informed decision-making, improved stakeholder satisfaction and operational efficiency. SMEs applying these principles within their ERP strategies will likely achieve a distinct competitive advantage characterized by enhanced adaptability and alignment with evolving business goals.

The complexity and dynamism of SME environments are not obstacles to overcome, but realities to embrace and work within. Our behavioral economics approach offers a “way out” by providing adaptive tools rather than rigid solutions. While traditional optimization may not be feasible, several innovative approaches can be used: Machine Learning and AI using machine learning algorithms to identify patterns and make real-time adjustments during ERP implementation; System Dynamics Modeling using system dynamics to model the complex interactions and feedback loops in SME environments; Agent-Based Modeling using agent-based models to simulate decision-making processes and organizational behaviors; and Fuzzy Logic Systems implementing fuzzy logic to handle qualitative factors and uncertainty in decision-making.

Hybrid approaches would include combining behavioral economics with quantitative methods that can yield powerful results: Integrating behavioral insights into operations research models to account for human factors, developing optimization models that account for uncertainty and variability in SME environments and using Multi-Criteria Decision Analysis (MCDA) techniques that can incorporate both quantitative and qualitative factors. Rather than seeking a single optimal solution, focus on creating a framework for continuous improvement: breaking down ERP implementation into smaller, manageable phases with frequent reassessment; fostering a culture of learning and adaptation within SMEs to navigate complex environments better; and implementing systems for real-time data collection and analysis to inform ongoing decision-making.

While traditional optimization approaches may not provide a straightforward “way out,” the challenges of ERP implementation in SMEs have opened doors to innovative, adaptive and holistic methodologies. By embracing these new approaches, we can navigate the complexities of SME environments more effectively than ever before. The “way out” lies not in finding a perfect solution, but in developing robust, flexible frameworks that allow SMEs to adapt and thrive in dynamic environments. Our behavioral economics approach, combined with emerging technologies and hybrid methodologies, offers a promising path forward in this complex landscape.

Recommendations

SMEs should use the developed Cognitive Bias Awareness Matrix in their ERP decision-making processes. This tool helps identify, understand and mitigate cognitive biases, particularly temporal discounting and optimism bias. By leveraging this matrix, SMEs can ensure a more balanced and informed approach to ERP implementation. The Cognitive Bias Awareness Matrix provides a foundation for developing targeted bias mitigation strategies. Scholars can investigate the effectiveness of different interventions aimed at increasing awareness and proactivity in addressing cognitive biases. Comparative studies can help identify the most effective approaches for various types of biases and organizational contexts. We recommend the following:

• Adopt a long-term perspective to address cognitive bias. Adopting a long-term perspective with bias awareness is crucial for SMEs considering ERP integration. This recommendation centers on using the matrix to shift the focus from immediate, short-term gains to long-term strategic benefits.

Temporal discounting, a common cognitive bias where immediate rewards are preferred over future benefits, can severely undermine the effective utilization of ERP systems by promoting choices that offer quick fixes rather than sustainable solutions. The matrix helps decision-makers understand and counteract this bias, thus ensuring that ERP choices align with long-term business goals and sustainability. Jacobs and Weston (2006) highlight the importance of a strategic, long-term focus in ERP implementation, suggesting that a deep understanding of the immediate and delayed implications of ERP projects is essential for lasting success. Kahneman (2003) also supports this view by indicating that awareness of biases and structured decision-making can significantly enhance the quality and outcomes of strategic business decisions:

• Strategically manage optimism bias. Strategic management of optimism bias involves using the Cognitive Bias Awareness Matrix to identify and mitigate instances where decision-makers may underestimate the challenges or overestimate the benefits of ERP implementations. Optimism bias can lead to unrealistic project timelines, budgets and expectations, resulting in project overruns and failure to achieve the desired outcomes. Promoting a culture of critical evaluation helps ensure that assumptions are rigorously questioned and validated, aiding in more realistic assessments of ERP implementation challenges and potential benefits. Caponecchia (2010) emphasizes the importance of recognizing and managing optimism bias to avoid safety and health misjudgments in the workplace, which can be analogously applied to managing ERP implementation risks. Furthermore, Dittmar and Bond (2010) argue that addressing such biases through specific tools and frameworks can improve decision accuracy and project success by aligning expectations with realistic capabilities and resources.

• Incorporate cognitive bias training in continuous learning. Doing so can ensure that all levels of an organization can recognize and mitigate cognitive biases in ERP system utilization. Investing in training programs covering the technical aspects of ERP systems and the cognitive biases influencing their use ensures a more informed and competent workforce. This educational initiative should include practical applications of the Cognitive Bias Awareness Matrix, enabling employees to actively identify and address biases in their decision-making and system usage. Nah et al. ((2001) suggest that such training can enhance the effectiveness of ERP systems by aligning user competencies with system capabilities. Moreover, Muscatello and Chen (2008) highlight the benefits of ongoing education on ERP success, indicating that continuous learning and adaptation are crucial to maximizing the return on ERP investments.

• Consider cognitive biases when developing EPR strategies. Doing so ensures that ERP systems remain relevant and effective as the business landscape changes. Using the Cognitive Bias Awareness Matrix in this process helps identify and correct any misalignments caused by optimism bias, where the benefits or capabilities of the ERP system may be overestimated. Regular reviews and adjustments, guided by the matrix, can prevent strategic drift and ensure that ERP functionalities continue to meet actual business needs. Jacobs and Weston (2006) support this contention and discuss the importance of aligning ERP systems with long-term business strategies to avoid obsolescence and ensure continued relevance. Additionally, Kahneman’s (2003) work on decision hygiene suggests that periodic reassessment of decisions and strategies can mitigate the risks of bias-influenced errors, making such alignment critical for sustained business success.

For practitioners, the matrix provides actionable insights and strategies to enhance ERP decision-making. SMEs can use the matrix to develop training programs, establish structured decision-making processes and foster a culture of continuous improvement. The matrix can also guide ERP vendors and consultants in designing support services that address the specific needs and biases of SME clients.

The Cognitive Bias Awareness Matrix can assist SMEs in their ERP decision-making processes. However, ERP offers additional opportunities for SMEs beyond the application of the matrix. We suggest the following:

• AI and ML can greatly support the utilization of ERP systems beyond mere processing efficiency. These technologies enhance predictive analytics, enabling real-time insights that facilitate dynamic resource allocation, forecasting and risk management. By automating routine tasks, AI/ML integrations reduce human error, allowing SMEs to rediscover valuable insights from their data. Such advancements empower organizations to refine their decision-making processes with data-driven strategies, dynamically responding to market fluctuations while aligning with cognitive bias awareness initiatives. Moreover, adopting a forward-looking perspective where AI/ML drives adaptability can ensure ERP implementations are better aligned with immediate operational needs and long-term strategic goals.

• AI tools integrated into ERP systems can automate routine tasks such as data entry, invoicing and reporting, allowing staff to focus on more strategic initiatives. Oracle AI Apps for ERP provide functionalities that automate transactional processes and generate smart insights, ultimately improving operational efficiency and decision-making outcomes. Companies can enhance accuracy and speed by reducing manual effort while encouraging a data-driven culture.

• Machine learning tools improve forecasting capabilities by analyzing historical data to predict future trends and demands. These capabilities allow SMEs to make proactive decisions regarding inventory management, production schedules and resource allocation. Leveraging these predictive analytics facilitates better alignment of supply with anticipated customer demand, thereby minimizing excess inventory and optimizing operational efficiency.

• AI and ML algorithms enhance data processing within ERP systems by analyzing large volumes of data quickly and identifying hidden patterns that may not be obvious to human analysts. This leads to more accurate decision-making as businesses are equipped with real-time insights into market dynamics and consumer behaviors. By providing timely and relevant data, these technologies support informed decision-making and strategic planning.

• AI tools such as chatbots and virtual assistants improve customer interactions by providing instant support and personalized experiences. They can process customer inquiries efficiently and offer tailored responses based on historical interactions. This functionality enhances customer satisfaction and allows businesses to gather valuable insights into customer preferences, leading to refined product offerings and marketing strategies.

• ML tools incorporated into ERP systems enhance security through anomaly detection capabilities. By continuously monitoring transaction data and operational metrics, these systems can identify deviations or potential fraud in real-time, allowing immediate remedial action. This protects businesses and instills greater confidence in decision-making by minimizing risks associated with operational activities.

• AI-driven systems contribute to a culture of continuous improvement by analyzing operational data over time and suggesting necessary adjustments. This adaptation enables SMEs to remain competitive amidst rapidly changing market conditions by refining their processes and strategies based on real-world insights. The ability of these systems to learn and evolve ensures that organizations can pivot effectively when faced with new challenges or opportunities.

Conclusions and limitations

The research presented in this paper highlights the critical role of a Cognitive Bias Awareness Matrix in enhancing ERP decision-making within Small and Medium Enterprises (SMEs). Entrepreneurial SMEs can significantly improve their operational outcomes and achieve more successful ERP implementations by recognizing and strategically addressing cognitive biases, such as temporal discounting and optimism bias.

Our findings suggest that SMEs with higher levels of awareness and proactivity regarding cognitive biases, categorized in Quadrant 3 (Proactive Mastery), achieve the highest success rates in ERP implementations. These firms maximize system quality, optimize information quality and engage proactively with service quality, resulting in effective ERP usage and high user satisfaction. Consequently, they realize the greatest net benefits.

In contrast, SMEs in Quadrant 1 (Passive Unawareness) face significant challenges due to low awareness and proactivity. These firms underestimate the technical capabilities of ERP systems, leading to inconsistent information quality and poor service quality. As a result, ERP system use is low, user satisfaction is poor and the benefits realized are minimal.

SMEs in Quadrant 2 (Informed Hesitation) recognize system capabilities and information quality but only partially leverage them due to hesitation. This leads to moderate ERP usage and mixed user satisfaction. Although these firms achieve moderate benefits, their potential is not fully realized without increased proactivity.

Firms in Quadrant 4 (Active Misalignment) exhibit high efforts but misdirect them due to unrecognized biases. While they engage actively and use ERP systems extensively, their satisfaction and net benefits are inconsistent. Realignment training and bias mitigation strategies are essential for these firms to maximize their benefits.

The proposed Cognitive Bias Awareness Matrix provides a structured framework for SMEs to evaluate and enhance their decision-making processes. The different levels of awareness and proactivity inherent in each quadrant provide a practice lens through which managers can evaluate their activity. By identifying their quadrant and implementing targeted strategies, SMEs can align their decision-making processes with their immediate operational needs and long-term business goals, ultimately leading to more successful ERP outcomes.

While ERP systems have become increasingly crucial for SMEs in their pursuit of operational efficiency, their successful adoption and implementation are often hampered by challenges, including limited resources and cognitive biases such as temporal discounting and optimism bias. Temporal discounting, the tendency to prioritize immediate gains over long-term benefits, can lead SME decision-makers to opt for short-term solutions in ERP systems, overlooking the advantages of a more comprehensive approach. Optimism bias, characterized by underestimating implementation challenges and overestimating benefits, can result in unrealistic planning and resource allocation.

This research underscores the importance of SMEs recognizing and strategically addressing these cognitive biases. SMEs can significantly improve their operational outcomes by incorporating insights from behavioral economics into their ERP decision-making processes. This process includes adhering to implementation timelines, increasing employee satisfaction and forecasting financial challenges more accurately.

The awareness and proactive management of cognitive decision-making biases, like temporal discounting and optimism bias, are instrumental in achieving better outcomes. The transformative potential for SMEs lies in adopting ERP systems and managing these biases across the ERP lifecycle. The proposed Cognitive Bias Awareness Matrix is a pivotal tool in guiding SMEs through all phases of ERP adoption – from initial decision-making and implementation to ongoing training and strategic realignment with evolving business goals.

The key to unlocking the full potential of ERP systems in SMEs is not solely in the technology itself but in the nuanced understanding and management of cognitive biases. The Cognitive Bias Awareness Matrix developed in this study offers a structured approach for SMEs to evaluate and enhance their decision-making processes, ensuring more informed, effective and competitive ERP implementations.

Our study has two key limitations. First, the PICOC framework is highly focused and contextual (Melendo Rodríguez-Carmona et al., 2024; Ochoa et al., 2023). Hence, we excluded some potentially relevant papers in our analysis. The PICOC provides a structured and systematic approach to formulating research questions and guiding evidence synthesis but also inherently limits the scope of the research due to its highly focused and contextual nature. It compels researchers to define their study parameters narrowly, concentrating on specific problems, interventions, comparisons, outcomes and contexts. This focus can inadvertently exclude relevant studies that do not perfectly align with the predefined criteria but could provide valuable insights into the broader domain of ERP implementation in SMEs.

Moreover, we only addressed two behavioral economics concepts. Many others could be included in a more comprehensive analysis (Parnell and Crandall, 2020). For example, we might have overlooked broader organizational, technical or cultural factors significantly impacting ERP success by concentrating on ERP decision-making in the context of cognitive biases. This exclusion can lead to a somewhat myopic view of the challenges and solutions in ERP implementation, potentially underestimating the complexity of ERP projects that involve multifaceted and interconnected factors beyond cognitive biases. Consequently, future research might benefit from a more inclusive framework that allows for exploring additional dimensions such as ERP customization, user training and post-implementation evaluations, which are also crucial to the success of ERP systems in SMEs.

The second limitation arises from our selective focus on a few behavioral economics concepts, primarily optimism bias and temporal discounting. While these are critical in understanding some of the decision-making failures in ERP implementations, behavioral economics offers a much richer array of theories and models that could provide further depth to our analysis. Parnell and Crandall (2020) highlight the expansive range of behavioral economics concepts that influence organizational decision-making, including loss aversion, risk preference and the endowment effect.

By not incorporating a broader spectrum of behavioral economics principles, our study may miss out on identifying other influential biases and psychological factors that could affect ERP decision-making processes. For instance, loss aversion could make SME leaders overly cautious and resistant to the upfront investments required for ERP systems despite potential long-term gains. Similarly, the endowment effect might lead SMEs to overvalue their legacy systems and processes, hindering the adoption of more efficient and integrated ERP solutions.

Scholars can address this limitation with a more comprehensive analysis of behavioral economics concepts. This approach would involve identifying a broader range of biases and examining how these biases interact with each other and influence decision-making in complex and often unpredictable ways. Such an analysis could lead to more robust strategies for mitigating the adverse effects of cognitive biases on ERP implementations, thereby enhancing the success rates and benefits realized from ERP systems in SMEs.

While our study leverages the structured approach of the PICOC framework and focuses on critical behavioral economics concepts to enhance understanding and mitigation of cognitive biases in ERP decision-making, it also acknowledges the need for a broader research scope. Doing so would ensure a more holistic understanding of SMEs’ multifaceted challenges in ERP implementations, leading to more effective and comprehensive solutions.

Future research

We identified several opportunities for future research. First, given the focused nature of the PICOC framework, future research can expand the scope of studies within the ERP decision-making field. Researchers could explore how modifying or extending the PICOC framework might allow for the inclusion of broader organizational, technological and cultural factors that impact ERP implementation success. For instance, future studies could incorporate additional elements such as stakeholder influence and change management within the PICOC framework, thus providing a more holistic view of ERP implementation in SMEs.

Future research might also benefit from adopting a multi-disciplinary approach, integrating insights from information technology, organizational behavior and change management to develop a more comprehensive understanding of ERP challenges. By doing so, researchers can capture the complex interplay between technology, people and processes, which are critical for successful ERP adoption and can often be overlooked when strictly adhering to the traditional PICOC structure.

The second avenue for future research involves deeper integration of behavioral economics into ERP decision-making studies. Beyond the initially examined concepts of optimism bias and temporal discounting, future research should investigate the impact of a broader range of behavioral economic principles on ERP decision-making, such as loss aversion, status quo bias and decision fatigue.

Each of these concepts offers a unique lens through which the decision-making processes of SMEs can be examined. For example, exploring how loss aversion influences SMEs’ reluctance to replace legacy systems or how the status quo bias affects the adoption of new technologies could provide valuable insights into the resistance often encountered during ERP projects. Moreover, considering the effect of decision fatigue on the quality of decisions made during long and complex ERP implementation projects could lead to developing strategies that help maintain decision quality throughout the project lifecycle.

Third, research could be broadened in various ways. By studying ERP implementations in diverse SMEs, researchers can better understand how specific industry requirements and cultural factors influence ERP success and decision-making biases. Doing so could lead to developing industry-specific guidelines and culturally sensitive practices for managing ERP projects. Moreover, there is a need for longitudinal studies that follow SMEs through the entire lifecycle of ERP implementation–from the decision-making phase to long-term usage. Such studies could examine how initial decisions impact later stages of the ERP lifecycle, how companies adjust their strategies over time and how these adjustments correlate with changes in business performance. Longitudinal research could also explore how cognitive biases evolve or are mitigated over time as organizations learn and adapt to their ERP systems.

Fourth, future research could explore the long-term effectiveness of the training modules designed to mitigate cognitive biases such as temporal discounting and optimism bias. Longitudinal studies could assess whether bias recognition and management improvements are sustained over time and how they influence the overall success of ERP implementations. This could involve following managers over multiple ERP projects or across different phases of a single extensive project to observe how bias mitigation impacts decision-making and project outcomes in the long run.

Fifth, investigating different training methodologies could provide insights into the most effective approaches for educating managers about cognitive biases. Comparative studies could examine various formats, such as interactive workshops, digital training modules or experiential learning, to determine which methods successfully reduce biases. Researchers could measure the effectiveness of these methods in terms of actual changes in decision-making behavior and ERP implementation success rates.

Sixth, additional work is needed on the impact of bias awareness on ERP outcomes. This research could use case studies or quantitative data to analyze how different placements within the matrix correlate with ERP success measures, such as system adoption rates, user satisfaction and return on investment. Doing so could validate the matrix as a tool for predicting and enhancing project success. Future studies could also explore the integration of advanced data analytics and artificial intelligence to refine the bias assessment and reassessment processes. Machine learning algorithms could analyze survey results and predict bias trends. This could lead to more personalized and adaptive training programs responsive to each manager’s needs and progress. Research could also include a broader range of cognitive biases, such as anchoring, overconfidence or risk aversion. Studies could investigate how these biases affect ERP decision-making, and the effectiveness of targeted training programs designed to address them.

By pursuing these avenues, future research can build upon the proposed strategy, offering more robust tools and methodologies to enhance decision-making processes in ERP implementations across various environments and conditions. These studies would contribute to academic knowledge and provide practical insights that can be directly applied in the field to improve ERP project outcomes in SMEs. Addressing these opportunities for future research can significantly advance our understanding of ERP decision-making in SMEs. By expanding the theoretical frameworks used, incorporating a broader array of behavioral economic concepts and exploring the ERP implementation process in more diverse contexts and over more extended periods, researchers can provide more nuanced insights and more robust recommendations to practitioners in the field. Doing so will improve the success rates of ERP implementations and ensure that these systems continue to deliver value as business environments evolve.

Table 1.

Cognitive bias awareness matrix

Matrix	Low proactivity	High proactivity
High awareness	Quadrant 2: Informed hesitation System quality: Recognized but not fully leveraged Information quality: Acknowledged but not utilized effectively Service quality: Aware but not acted upon Use: Moderate usage due to hesitation User satisfaction: Mixed satisfaction, hindered by inaction Net benefits: Moderate benefits Actions: Targeted workshops ERP benefits and overcoming biases	Quadrant 3: Proactive mastery System quality: Maximized through high awareness and proactive management Information quality: Optimized by addressing biases Service quality: High due to proactive and informed engagement. Use: Effective and efficient usage User satisfaction: High satisfaction from realistic expectations Net benefits: Maximized benefits through informed and proactive strategies Actions: Continuous improvement programs and advanced bias training
Low awareness	Quadrant 1: Passive unawareness System quality: Underestimating technical capabilities due to lack of awareness Information quality: Inconsistent and unreliable due to unrecognized biases Service quality: Poor due to lack of proactive engagement Use: Low due to unrecognized value User satisfaction: Low satisfaction due to unmet expectations Net benefits: Minimal benefits realized Actions: Basic training on ERP functions and bias awareness	Quadrant 4: Active misalignment System quality: High efforts but possibly misdirected due to unrecognized biases Information quality: Efforts made but possibly skewed by biases Service quality: Active engagement but misaligned with needs Use: High but potentially ineffective usage User satisfaction: High effort but inconsistent satisfaction Net benefits: Efforts not fully translating to benefits Actions: Realignment training and bias mitigation strategies

Source: Table by Authors

Table 2.

“Stylized” enhanced cognitive bias awareness matrix for ERP implementation in SMEs

Matrix	Low proactivity	High proactivity
High awareness	Quadrant 2: Informed hesitation Cognitive biases: - Optimism bias - Analysis paralysis Impact: - Timeline overruns: 30% - Budget overruns: 25% Mitigation: - Reference class forecasting - Contingency planning KPIs: - Project variance: 20% - Risk mitigation rate: 65% - User satisfaction: 3.5 / 5 Bias impact severity: 3.2 (moderate impact, mitigated by awareness) Recommended training: 36 h (action-oriented) ERP module performance (% of optimal use): finance (75%), HR (70%), ops. (65%) Time to value: 14–18 months Cost overrun risk: 40% likelihood of 20%+ overrun	Quadrant 3: Proactive mastery Cognitive bias management: - Optimism bias (controlled) - Temporal discounting (managed) Strategies: - Long-term ROI focus - Phased implementation KPIs: - User adoption rate: 92% - ROI realization: 110% of projection - Process efficiency improvement: 35% - User satisfaction: 4.7/5 Bias impact severity: 1.8 (low impact, effectively managed) Recommended training: 24 h (advanced/specialized) ERP module performance (% of optimal use): finance (95%), HR (92%), ops. (90%) Time to value: 8–10 months Cost overrun risk: 15% likelihood of 10%+ overrun
Low awareness	Quadrant 1: Passive unawareness Cognitive biases: - Dunning–Kruger effect - Temporal discounting Impact: - Underestimation of complexity: 70% - Short-term focus: 80% of decisions Mitigation: - Awareness training - Long-term value communication KPIs: - System usage: 40% - Data quality: 55% accuracy - User satisfaction: 2.1/5 Bias impact severity: 4.5 (high impact due to unawareness); Recommended training: 48 h (foundational) ERP module performance (% optimal use): finance (45%), HR (40%), ops. (35%) Time to value: 20–26 months Cost overrun risk: 60% likelihood of 30%+ overrun	Quadrant 4: Active misalignment Cognitive biases: - Temporal discounting - Overconfidence bias Impact: - Short-term solutions: 65% - Strategic misalignment: 50% Mitigation: - Long-term goal setting - Comprehensive project planning KPIs: - Long-term adoption rate: 70% - Process improvement: 15% - ROI achievement: 80% of projection Bias impact severity: 3.8 (high impact, exacerbated by misaligned actions) Recommended training: 60 h (comprehensive realignment) ERP module performance (% optimal use): finance (80%), HR (75%), ops. (72%) Time to value: 12–16 months Cost overrun risk: 45% likelihood of 25%+ overrun

Source: Table by Authors

Table 3.

PICOC Analysis for ERP decision-making enhancement in SMEs using a cognitive bias awareness matrix

Element	Description
P (problem)	The challenge of cognitive biases, specifically temporal discounting and optimism bias, affecting ERP decision-making in SMEs
I (intervention)	Development of cognitive bias awareness matrix, a tool designed to help SMEs self-assess and improve awareness and proactive strategies in recognizing and countering these biases during ERP system adoption decisions
C (comparison)	Comparing decision-making processes and outcomes in SMEs that utilize cognitive bias awareness matrix to those that do not
O (outcome)	Enhanced decision-making in ERP implementations, characterized by greater awareness of cognitive biases, more informed choices and improved success rates in ERP system adoption
C (context)	Small and medium enterprises across various industries, focusing on their approach to ERP system decision-making and the extent of their engagement with cognitive bias awareness matrix

Source: Table by Authors

Table 4.

PICOC Search flowchart

Step	Criteria	No.
1	Search with keywords “ERP,” “SMEs,” and “Decision-Making”	3,751
2	Apply filters: “Peer-Reviewed,” “books,” and “academic journals”	217
3	Apply date filter (published since 2000)	47
4	Manually review abstracts for relevance	24
5	Curated list of High-Quality articles	14
6	Articles selected	10

Source: Table by Authors

Table 5.

Search results

Article title	Author(s)
Enterprise resource planning (ERP) – a brief history	Jacobs and Weston (2006)
An approach to the valuation and decision of ERP investment projects based on real options	Wu et al. (2008)
One more time: How do you motivate employees?	Herzberg (2003)
A study of issues affecting ERP implementation in SMEs	Dixit and Prakash (2011)
Enterprise resource planning (ERP) implementations: theory and practice	Muscatello and Chen (2008)
Industry-oriented enterprise resource planning	Wu and Wang (2009)
ERP software selection processes: case study in metal transformation sector	Hidalgo Nuchera et al. (2011)
I want it and I want it now: using a temporal discounting paradigm to examine predictors of consumer impulsivity	Dittmar and Bond (2010)
Critical factors for successful implementation of enterprise systems	Nah et al. (2001)
A history of small business in America (2 ed.)	Blackford (2003)
Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems	Bradford and Florin (2003)
An updated ERP systems annotated bibliography: 2001–2005	Esteves and Bohórquez (2007)
Enterprise resource planning systems research: an annotated bibliography	Esteves and Pastor (2001)
The contribution of behavioral economics to crisis management decision-making	Parnell and Crandall (2020)

Source: Table by Authors

Table 6.

Critical evaluation of overall validity in the context of ERP decision-making in SMEs with cognitive bias awareness and proactivity

Research approach	Empirical basis	Analysis method	Overall validity
Jacobs and Weston (2006)	Qualitative, interviews industry experts, ERP founders and former executives, with historical data and information from industry	Qualitative analysis of historical data, interviews with industry experts and consultation primary and secondary sources	High: analysis on interviews with industry experts and extensive historical data, reliable account of ERP history
Wu et al. (2008)	Primarily relies on theoretical and model-based analysis with limited empirical	Real-option analysis framework utilizing multistage stochastic integer programming to formulate analytical model	Moderate: there is a lack of specific empirical data
Herzberg (2003)	Empirical examination data provides concrete evidence of optimism bias regarding OHS hazards	Assesses optimism bias regarding occupational health and safety hazards postgraduate students and university employee’s data analysis and statistical testing	High: quantitative approach and statistical analysis to identify optimism bias in the context of OHS hazards
Dixit and Prakash (2011)	Inductive approach focusing on the effectiveness of strategic planning in organizations	Qualitative case study involving semi-structured interviews, participatory observation and project documentation analysis	Moderate: study sheds light on strategic planning, it might not directly tie into ERP implementation in SMEs
Muscatello and Chen (2008)	Experimental qualitative study on empirical data collected through cross-sectional mail survey targeting business executives with ERP implementation experience	Analysis based on responses from participants using cross-sectional mail survey to investigate critical factors in ERP implementation	Moderate: survey data but provides valuable insights into ERP
Blackford (2003)	Experiments involving participants to examine discount rates for various consumer goods and association with materialistic values and identity deficits	Temporal discounting paradigm three experiments investigate consumer impulsivity concerning types of goods and relationship with materialistic values and identity deficits	Moderate: controlled experimental approach, but it may be context specific
Bradford and Florin (2003)	Bounded rationality systematic biases, separate beliefs people have and choices made from optimal beliefs and choices assumed in rational agent models	Collaboration with amos tversky explored psychology of intuitive beliefs and choices and examined bounded rationality	High: utility maximization replaced with satisficing Battleson and Mathiassen (2020)
Esteves and Bohórquez (2007)	Taxonomy based on implicit empirical foundation from ERP literature, research studies and organizational experiences	Novel taxonomy of critical success factors in ERP implementation by combining research studies and organizational experiences	High: Comprehensive analysis of ERP literature and real-world organizational insights
Esteves and Pastor (2001)	Investigative approach into how behavioral economics principles influence consumer behavior	Descriptive analysis drawing parallels between consumer behavior and principles of behavioral economics	Moderate: Provides insights into behavioral economics in the marketplace, indirectly relevant to ERP decision-making
Parnell and Crandall (2020)	Theoretical concepts and categorizes applications from behavioral economics in crisis management	Conceptual framework categorizes applications from behavioral economics into three stages of crisis management life cycle	Moderate: Conceptual framework but lacks empirical validation

Source: Table by Authors

Table 7.

Summary of findings

Study	Findings	Translation
Jacobs and Weston (2006)	Analysis provides insights into ERP history and key factors	Valuable historical context and insights for understanding ERP implementation
Wu et al. (2008)	Limited empirical data restricts in-depth analysis	Findings should be considered with caution due to the limited empirical basis
Herzberg (2003)	Quantitative analysis identifies optimism bias in OHS hazards	Concrete evidence of optimism bias in OHS hazards, valuable for safety management
Dixit and Prakash (2011)	Offers insights into strategic planning but not ERP directly	Findings may inform strategic planning but may not directly relate to ERP in SMEs
Muscatello and Chen (2008)	Survey data provides valuable insights into critical factors	Survey data offers insights into ERP implementation critical factors
Wu and Wang (2009)	Temporal discounting paradigm	Presents a framework association BEF factors ERP implementation
Hidalgo Nuchera et al. (2011)	Psychology, bounded rationality. Systematic beliefs and choices made -rational agent models	ERP selection methodology based on model utility maximization replaced with satisficing
Dittmar and Bond (2010)	Comprehensive taxonomy of ERP success factors	Comprehensive analysis of ERP literature, offering insights into success factors
Nah et al. (2001)	Provides insights into behavioral economics principles	Insights into how behavioral economics may indirectly impact ERP decisions
Blackford (2003)	Conceptual framework behavioral economics in crisis management	Categorizes applications of behavioral economics in crisis management

Source: Table by Authors