Mission of Scientific Confraternity of Evidence Researchers
We create and promote a new theory of Evidence-Based Management, based on the analysis of large data sets from research in the field of management science and quality, supporting organizations in making decisions based on scientific evidence. If you need more information or clarification, let us know!
Discussions about information quality usually focus on fake news,
disinformation, and manipulation. While these phenomena are important,
they do not fully explain how poor decisions emerge in organizations,
education, consulting, coaching, or social media environments.
In practice, many harmful beliefs are not the result of deliberate deception.
They often originate from oversimplifications, incomplete interpretations,
selective use of evidence, or the repetition of attractive statements whose
evidential foundations have never been critically examined.
Definition
A Viral Error is a small, partially true or seemingly
credible piece of information whose capacity for dissemination exceeds the
capacity of the information environment to effectively correct it.
The defining characteristic of a Viral Error is not necessarily falsity,
but transmissibility. Such claims spread because they are simple,
memorable, emotionally attractive, and socially useful. They provide
quick explanations and reduce complexity, even when the underlying
evidence is weak or incomplete.
Fake News versus Viral Error
Criterion
Fake News
Viral Error
Nature
False information presented as factual
Simplified, partial, or misinterpreted information
Intention
Often deliberate and manipulative
Often unintentional
Source of influence
Sensationalism, conflict, falsehood
Simplicity, repetition, apparent plausibility
Difficulty of correction
Depends on the scale of falsehood
High, because it often contains elements of truth
Risk for EBM
Decisions based on false information
Decisions based on weak or distorted evidence
How Viral Errors Emerge
Viral Errors usually emerge when complex phenomena are reduced to simple
formulas. Consider the statement:
"People leave companies because of their managers."
The claim may contain an element of truth, yet it fails to explain employee
turnover as a multidimensional phenomenon.
Factors such as compensation, labour market conditions, organizational
culture, career opportunities, workload, and personal circumstances are
often omitted. The result is a statement that is easy to communicate but
methodologically insufficient.
This is precisely why Viral Errors are difficult to identify. They do not
sound false. They sound true—just not true enough.
Relevance for Evidence-Based Management
Within Evidence-Based Management, the challenge is not merely the absence
of data. The challenge is the mistaken attribution of evidential value to
information that does not justify the conclusions being drawn.
Data may be accurate and still insufficient. An anecdote may be authentic
and still lack representativeness. A scientific study may exist and still
be misinterpreted or applied outside its valid context.
Viral Errors emerge precisely in this space between information and
evidence. They create an illusion of understanding and provide a language
of certainty without the methodological foundations required for reliable
decision-making.
Methodological Implications
In qualitative research, discourse analysis, social media studies, and
Big Qual Data projects, Viral Error may be treated as a distinct analytical
category. Rather than asking only whether a statement is true or false,
researchers may investigate why a particular simplification became easy to
replicate and socially attractive.
Such analyses may focus on the origin of the claim, contextual conditions
of dissemination, patterns of repetition, forms of justification,
authority references, selective use of evidence, and resistance to
correction.
Conclusion
Viral Error is a useful methodological concept because it highlights a
problem that is often overlooked in discussions about information quality.
Many poor decisions do not arise from obvious falsehoods but from claims
that are partially true, insufficiently supported, and excessively simple.
From an Evidence-Based Management perspective, the essential question is
not only:
"Is this true?"
but also:
"Is this sufficiently strong evidence for the decision being made?"
The development of artificial intelligence in education not only increases access to information but also introduces a new epistemological risk:
AI hallucinations,
that is, content which appears credible yet lacks grounding in reality. This article analyzes the phenomenon from the perspective of Evidence-Based Management (EBMnt), arguing that the issue is not merely technological but fundamentally methodological and decision-related. In particular, AI hallucinations are compared with classical scientific malpractices such as HARKing and p-hacking, demonstrating that all these phenomena lead to the production of pseudo-evidence. Consequently, a redefinition of evidence becomes necessary, alongside a shift toward managing the credibility of information.
Credibility
For a long time, education operated under conditions of information scarcity. The key competence was the ability to search for and assimilate knowledge. With the emergence of AI tools, this situation has changed radically — information has become instantly available, personalized, and linguistically refined. However, this shift has revealed a new problem: not the lack of knowledge, but its overabundance in forms that are difficult to distinguish from reliable information. AI hallucinations are a symptom of a deeper transformation — from the problem of information accessibility to the problem of its credibility.
From the perspective of Evidence-Based Management, it is particularly significant that the source of information begins to simulate evidence. AI-generated responses may fulfill all superficial criteria of correctness while lacking empirical grounding.
An Epistemological or Merely Technical Phenomenon?
In educational practice, AI hallucinations manifest themselves in various forms: from subtle shifts in meaning, through incorrect interpretations, to entirely fictitious sources. Their defining characteristic, however, is not falsehood itself, but its credible form. AI hallucinations are “complete,” “elaborated,” and “persuasive” — they imitate the structure of knowledge rather than its sources. From an EBMnt perspective, this implies a crucial shift: from evaluating content to evaluating the process by which it is produced.
This approach is especially important in areas requiring specialized competencies — such as remote education, accounting, coaching, or craftsmanship — where apparent correctness may lead to real decision-making errors. In such fields, knowledge is not merely informational but actionable, making the issue of hallucinations particularly consequential.
AI hallucinations are often treated as a technological imperfection. However, their nature is more deeply rooted and resembles well-known methodological flaws in science. Two phenomena are especially relevant here:
HARKing (Hypothesizing After the Results are Known) consists in formulating hypotheses only after obtaining the results and then presenting them as prior research assumptions.
p-hacking consists in manipulating data analysis (for example, through variable or sample selection) in order to obtain statistically significant results.
Structurally, AI hallucinations work in an analogous way:
the result (the answer) is generated first,
the justification is added afterward,
the whole is presented as a coherent and rational process.
In this way, an illusion of evidence emerges, one that results neither from data nor from methodology, but from alignment with the expectations of the audience.
This logic is increasingly relevant in education and management as well. In a world saturated with AI-generated content, it is not enough to expand access to information. The cognitive environment must be designed to support selection, verification, and credibility assessment. In other words, the challenge is not to see more, but to recognize more accurately what truly matters.
Knowledge or “Magic”?
Evidence-Based Management assumes the integration of multiple sources of evidence. Traditionally, the main problem was the limited availability of evidence or its uneven quality. Today, this situation is reversed — the challenge is no longer information scarcity, but its overproduction, in which distinguishing knowledge from its simulation becomes increasingly difficult. AI hallucinations, much like HARKing and p-hacking known from scientific methodology, lead to what may be described as pseudo-evidence. These are constructs that take the form of argument, use the language of science, and fit the cognitive expectations of the recipient, yet lack genuine grounding in empirical data. Their strength does not derive from truth, but from credible form.
In this context, artificial intelligence ceases to be a source of knowledge in the classical sense. Rather, it becomes a generator of possible narratives — linguistic hypotheses that must still be confronted with scientific research, operational data, practical experience, and stakeholder values. It is precisely this integration that constitutes the core of Evidence-Based Management and, at the same time, the fundamental mechanism of defense against pseudo-knowledge.
One may say that contemporary knowledge management increasingly resembles a choice between two orders: knowledge understood as a process of arriving at truth, and “magic,” which creates its convincing illusion. In the first case, we are dealing with the effort of verification, uncertainty, and methodological discipline. In the second, there appears the temptation of a quick effect, where the result matters regardless of its epistemic foundation. AI hallucinations situate themselves dangerously close to this latter order.
They produce answers that work — they are coherent, persuasive, and useful — but they are not necessarily true.
As a result, a shift occurs in which decisions may increasingly be made not on the basis of evidence, but on the basis of its simulation.
From the perspective of management practice and didactics, this means that the relationship between data, interpretation, and decision must be redefined. Particularly in digital environments, it becomes easy for the tool to begin replacing the cognitive process rather than supporting it. Experience at the intersection of entrepreneurship, education, and research shows that technology — however useful — must be balanced by methodological rigor. In this sense, it is no longer sufficient to manage information. What becomes necessary is the management of its credibility.
Ultimately, then, the question is not whether to use AI, but in what epistemological order we want to function: in a world of knowledge that requires verification, or in a world of “magic” that merely imitates it.
Alex Andrews | pexels.com
Hallucinations as a Cognitive Stress Test
Rather than treating AI hallucinations solely as a threat, they may also be understood as a diagnostic tool. They reveal not only flaws in the technology itself, but also weaknesses in the educational and decision-making systems within which they operate. When confronted with AI-generated content, it quickly becomes clear whether the user can distinguish information from evidence, understands the process by which knowledge is produced, and is able to recognize methodological errors — both those resulting from human simplifications and those generated algorithmically. In this sense, AI hallucinations function as a kind of stress test for Evidence-Based Management. A system that truly relies on evidence integration remains resilient; a system that merely declares such an orientation succumbs to the illusion of credibility.
AI hallucinations, like HARKing and p-hacking, share a common denominator: they produce images of reality that are persuasive yet epistemically fragile. Although they differ in their mechanisms — from algorithmic prediction to selective data interpretation — they lead to the same result: the erosion of decision quality. Consequently, the essential shift no longer concerns technology itself, but the way we think about knowledge and its role in action. What becomes crucial is the transition from information management, understood as collecting and processing data, to the management of information credibility, which assumes its constant verification and confrontation with multiple sources.
In a world saturated with AI-generated content, Evidence-Based Management ceases to be merely one possible method among many. It becomes a condition of meaningful decision-making. Without it, the risk grows of acting on the basis of what merely resembles evidence, but is not evidence at all. Ultimately, then, the problem does not reduce to the question of whether AI makes mistakes, but to a far more demanding one: whether the system in which we operate — educational, organizational, or cognitive — is capable of recognizing, understanding, and correcting those mistakes.
Source: M. Jabłoński, A. Jabłoński, P. Janulek, D. Dulęba, M. Glenszczyk, Treatise on the Principles of Evidence-Based Management – The Future of Management (TRAKTAT o zasadach zarządzania dowodowego – przyszłość zarządzania) 2025, CeDeWu, p. 190.
The EQUATOR (Enhancing the QUAlity and Transparency of health Research) network is a global initiative focused on improving the quality and transparency of research reports. It offers a wide range of resources to support researchers, such as checklists and risk of bias tools.
Why are they needed?
Checklists are key to ensuring the quality of research reports, minimizing the risk of omitting important information. Risk of bias tools help identify potential weaknesses in studies, allowing them to be examined more critically.
These tools are particularly useful for those working in clinical, epidemiological, and basic science research. They can help researchers avoid methodological errors that could affect the credibility of results.
The EQUATOR website provides a number of guides and checklists, including PRISMA (for reporting meta-analyses) and CONSORT (for reporting clinical trials). Each of these resources is designed to support transparency and reliability in scientific reporting.
Randomized controlled trials (RCTs) are considered the “gold standard” in medical research. They involve randomly assigning participants to study groups, such as an experimental group (receiving a new treatment) and a control group (receiving a placebo or standard treatment). This allows for an objective assessment of the effectiveness and safety of medical interventions. RCTs are a key element of evidence-based medicine (EBM) and are used not only in medicine but also in other fields such as psychology and social sciences.
RCTs can be very useful in management, especially in the area of evidence-based decision-making. They can be used to test different management strategies, organizational policies, or training methods. For example, if a company wants to implement a new way of motivating employees, it can conduct an RCT to see if it actually produces better results compared to current practices. Such studies help minimize the risk of decision-making errors and also help understand which interventions are most effective.
How are systematic literature reviews adapting to turbulent times and changing the face of EBMnt? Through a critical literature review, we explored how traditional, static reviews, usually conducted as one-time projects, are transforming into dynamic, continuously updated “living literature reviews.”
Key findings: – Traditional, static reviews, conducted as one-time reports, are gradually evolving as technology advances and the number of scientific publications increases. – Dynamic approaches, based on the concept of living systematic reviews, meet the contemporary needs of updating reviews in real time.
Added value: Traditional reviews, once sufficient as one-time reports, are losing their relevance in the face of the growing number of new studies. Our research findings indicate the need to develop more flexible and dynamic methods for conducting literature reviews that can keep up with continuous innovation.
Contemporary organizational management not only seeks operational efficiency but also relies on scientific research and empirical evidence to support decision-making. Within the Evidence-Based Management (EBMnt) paradigm, concepts such as the learning curve and the DART (Dynamic Assessment of Real-Time Learning) approach are analyzed in the context of process and work method effectiveness. Below, I will discuss these two concepts in the context of EBMnt, considering their role in improving organizational performance.
Learning Curve in EBMnt
Learning curve is a concept that illustrates how work efficiency (e.g., productivity, task completion time) changes as experience is gained in a given area. Simply put, the more we perform a certain activity, the more proficient we become, thus reducing the time needed to complete it and improving the quality of results.
In the Evidence-Based Management (EBMnt) paradigm, learning curve analysis is the basis for making decisions about resource allocation, training planning, process improvement, or identifying areas that may require greater support. The key element in this approach is the use of empirical data and scientific evidence that indicate how the learning curve differs depending on the organizational context, type of work, or industry specifics.
Andrew Neel / piexels.com
Empirical evidence in this area may indicate that, for example, in manufacturing companies, the learning curve is strongly related to automation and standardization processes, while in the creative industry it may be more complex, and the time to perfect skills is more difficult to measure in a linear way. EBMnt allows for monitoring and analyzing these changes over time, which enables process optimization.
DART (Dynamic Assessment of Real-Time Learning) in EBMnt
DART is an approach that uses dynamic assessment of progress in real-time, in the context of organizational learning. Instead of analyzing only changes in efficiency over the long term, DART focuses on continuously collecting data that allows for an assessment of how an organization (or its members) absorb new knowledge and how it affects their results in the short term.
Within EBMnt, the DART approach becomes particularly important as it enables organizations not only to respond to problems in real-time but also to monitor which specific strategies or training interventions bring measurable effects in a short time. By using real-time evidence, organizations can more quickly identify effective learning and adaptation techniques that lead to better results.
DART in the context of EBMnt allows for quick testing of different management strategies and assessment of their impact on the learning process. An example could be analyzing the effectiveness of training or employee development programs at both individual and team levels. Through an evidence-based approach, organizations can make adjustments to development strategies in a more flexible way that is tailored to current needs.
Combining Learning Curve and DART in the EBMnt Paradigm
Both approaches – learning curve and DART – in the context of EBMnt can be mutually complementary. The learning curve shows the long-term trend of efficiency as experience is gained, while DART focuses on current assessment and adaptation, based on data collected in real-time. Combining these two elements within the EBMnt paradigm enables organizations not only to better forecast but also to quickly respond to changes and optimize learning processes.
In practice, organizations can use the learning curve for long-term planning of training, competency development, or process design. At the same time, the application of DART allows for ongoing monitoring of the effectiveness of these activities and making adjustments when data indicate that a given process or intervention is not yielding the expected results. From the EBMnt perspective, both these tools should be supported by empirical evidence that allows for an objective assessment of the effectiveness of actions and making decisions based on them.
Summary
In the context of Evidence-Based Management (EBMnt), both the learning curve and DART are powerful analytical tools that allow organizations to improve efficiency through the use of scientific evidence and data. The learning curve provides a broad view of long-term development, while DART allows for quick and ongoing adjustment of management strategies in real-time. As a result, organizations can not only optimize their processes but also dynamically adapt to changing market conditions and employee needs.
The REF (Research Excellence Framework) is a research assessment framework that simultaneously considers the quality of research and its impact on society, the economy and other areas. To effectively balance these two aspects, it is crucial to understand the criteria and expectations for both.
1. Understand the assessment criteria
The first step is to understand how the quality and impact of research are assessed in the REF. Research quality is assessed by expert panels on four elements: originality, significance, reliability and contribution to the field. Research impact is assessed through case studies that show how research has impacted the economy, society, culture, environment, health or public policy. The REF documentation provides detailed definitions, examples and standards for each of these elements. It is worth familiarising yourself with the relevant guidelines for your discipline and unit of assessment.
2. Focus on research quality
To ensure high-quality research, attention should be paid to its originality, significance, and methodological integrity. In practice, this means designing studies that not only provide new, valuable information, but are also well-documented and conducted according to rigorous research standards.
Leonardo Luncasu / pexels.com
3. Build impact case studies
To demonstrate the impact of your research, it is important to develop case studies that show how the research has had an impact. An example would be a study related to sustainability in business, where the research may have an impact on changes in corporate policies regarding sustainable resource use. It should be clearly shown how the research has influenced changes in practices and decisions outside of academia.
For example, if your research explores the EBMnt model, highlight how implementing evidence into decision-making processes increases organizational effectiveness and helps adapt to changing market conditions.
4. Combine quality and impact
The key to success is the integration of quality and impact. Good research is research that not only contributes new, valuable information to its field, but also has a practical, visible impact on the world outside the university. Engage in interdisciplinary collaborations that increase the chance of broad impact while maintaining high research standards.
5. A culture of reflection and continuous improvement
It is also important to build a culture of reflection among researchers, enabling them to share experiences, discuss challenges and successes, and promote continuous improvement in their research. Regular training and resources for researchers can help them communicate their research more effectively and better align it with the requirements of the REF.
Summary
Balancing the impact and quality of research in the REF requires a deliberate strategy. Understanding the evaluation criteria, investing in high-quality research, and being able to communicate its impact on the wider community are key elements of success in the research evaluation process.
1. Introduction to Nondestructive Testing (NDT) and EBMnt Nondestructive testing (NDT) is a set of methods used to examine materials and structures, allowing the assessment of their technical condition without causing any damage. NDT is widely used in industries such as aerospace, energy, automotive, and construction, where critical components of machines, devices, and structures must be regularly checked for damage or defects that could threaten safety or functionality.
David Brown / pexels.com
On the other hand, EBMnt (Evidence-Based Management) is a management approach based on empirical evidence that uses data and analysis to make decisions within an organization. In the field of NDT, this approach is becoming increasingly important because it allows the systematic incorporation of NDT test data and results into decision-making processes, which in turn enables more precise and fact-based decisions regarding the technical condition, maintenance, or replacement of components.
2. NDT in the Context of EBMnt In the context of EBMnt, NDT plays the role of providing hard evidence about the condition of materials and equipment, enabling a comprehensive analysis of their state. NDT tests generate data that is used to make decisions regarding further usage, maintenance, or replacement of components. In traditional approaches, these decisions might be based on subjective assessments or intuitive judgments, but through EBMnt, this process becomes more objective, transparent, and evidence-based.
NDT tests offer a wide range of methods, such as:
Ultrasonic Testing (UT)
Radiographic Testing (RT)
Magnetic Particle Testing (MT)
Eddy Current Testing (ET)
Thermography (IR)
Visual Testing (VT)
Each of these methods provides detailed data on the material's structure, its integrity, and any hidden defects. It is worth noting that in the use of EBMnt, NDT test results must be properly analyzed, documented, and used in the decision-making process, which enhances operational efficiency.
3. Key Findings from the Integration of NDT and EBMnt
Objectivity of Decisions – the integration of NDT with EBMnt allows decisions to be made based on accurate test data, eliminating subjectivity in assessing the technical condition of machines and structures. This enables managers to make more precise decisions about the need for maintenance or replacement of parts, minimizing the risk of errors.
Risk Minimization – regular NDT tests allow for early detection of damage that may lead to failures. Combined with the EBMnt approach, decisions regarding the need for repair or replacement are based on empirical data, reducing the risk of sudden failures and related production downtime.
Cost Optimization – using NDT tests helps optimize maintenance costs. Maintenance and repair activities can be precisely scheduled when most needed, preventing unnecessary replacement costs for components that are in good condition, as well as repair costs after failures that could have been anticipated.
Operational Efficiency – NDT allows for continuous monitoring of equipment and material conditions without disrupting their operation. This ensures the continuity of production and operations in enterprises while maintaining safety and reliability.
4. Synergy Perspective
Integrating NDT methods with the EBMnt approach brings numerous benefits in risk management, cost optimization, and improving operational safety.
Here are some key areas of synergy between these two approaches:
Improved Quality of Decision-Making Processes – NDT provides detailed, measurable data about the technical condition of equipment and materials, which, combined with EBMnt analysis, allows for better decisions in maintenance, repair, and technical resource management.
Continuous Improvement of Production Processes – according to the EBMnt principle, processes within an organization are continuously improved based on analysis and data. NDT tests allow for constant monitoring of material and product quality, enabling quick responses to irregularities and the implementation of effective corrective actions.
Optimization of Maintenance Planning – by combining NDT results with EBMnt analysis, better planning of maintenance activities is possible. Based on the evidence from tests, organizations can move to an approach based on the actual condition of equipment, leading to efficient planning of repairs, part replacements, and other maintenance activities.
5. Conclusions The integration of NDT with EBMnt enables organizations to manage technical resources more precisely and consciously. NDT research provides data that, when combined with the EBMnt approach, can lead to better operational decision-making, risk minimization, and reduced maintenance costs. NDT becomes a key tool in this context, providing organizations with evidence of the actual technical condition of their resources, enabling the implementation of effective, fact-based decisions that contribute to increased efficiency, reliability, and operational safety.
We are pleased to invite you to explore our latest scientific article, which delves into the key determinants of studying ontological entities in quality management sciences through the lens of the Evidence Based Practice concept. This article provides an in-depth critical analysis of previous studies, highlighting the cognitive gap and emphasizing the significance of systematic literature reviews, meta-analyses, and other methodologies in advancing our understanding of ontological entities within management and quality sciences.
Summary: The article aims to identify the primary determinants of studying ontological entities in quality management sciences using the Evidence Based Practice concept. The research draws on critical analyses of prior studies to elucidate the current status and knowledge gaps in this field. Our findings demonstrate the effective application of research methodologies such as systematic literature reviews and meta-analyses, adapted from the medical industry, in the management and quality sciences. These methodologies provide a broader scientific understanding compared to traditional literature reviews. Moreover, the article addresses the challenges posed by managers' limited knowledge of ontological entities and the ontological significance of their decisions in business. The increasing availability of large sets of scientific data enhances the understanding and utilization of evidence-based management practices.
We believe that this article will offer valuable insights and contribute to the ongoing discourse in management and quality sciences. We look forward to your feedback and engagement with our research.
The Scientific Confraternity of Evidence Researchers (SCoER) implements a unique research approach in management sciences. The team consisting of Adam Jabłoński, Marek Jabłoński, Daniel Dulęba, Mariusz Glenszczyk and Piotr Janulek focuses its activities on the evolutionary combination of methodologies developed in evidence-based medicine (Evidence-Based Medicine) with management sciences.
The foundation of the team's work is the belief that rigorous research methods used in medicine and pharmacy can significantly improve the quality of the decision-making process in managing organizations. The Confraternity undertakes pioneering attempts to adapt meta-analyses and systematic literature reviews — methods commonly used in medical research — to the context of enterprise management. This is a particularly innovative approach, because so far management sciences have been based mainly on other research methods, often less methodologically rigorous.
The team develops the concept of management based on systematic evidence (Systematic Evidence-Based Management), which may constitute the seed of a new subdiscipline in management sciences. This can be provisionally called "Systematic Evidence-Based Management" or "Medical Standards Management".
Within this concept, researchers propose using the hierarchy of scientific evidence, characteristic of medicine, in the process of making managerial decisions. This approach aims to increase the objectivity and effectiveness of management actions by basing them on systematically verified scientific evidence.
The Confraternity introduces the methodology of meta-analysis to management sciences, which allows for obtaining quantitative, precise and integrated conclusions from various studies. This is particularly important in the context of the growing complexity of management problems and the need to make decisions based on reliable data.
The team's research is characterized by a holistic approach to science, combining the methodological rigor of medical sciences with practical aspects of managing organizations. This interdisciplinary perspective allows for the creation of new solutions in the field of scientific research and management practice.
The Confraternity's activities can contribute to significant changes in the way research is conducted in management sciences, introducing higher methodological standards and new research tools. In a practical perspective, the team's work can lead to the development of more effective and objective management strategies based on systematically verified scientific evidence.
The team believes that introducing rigorous methodological standards to management sciences is necessary in the face of the growing complexity of modern organizations and their environment. By adapting proven methods from the field of medicine, the Confraternity aims to improve the quality of research in management sciences and increase their practical usefulness.
This pioneering activity of the Scientific Confraternity of Evidence Researchers fills a significant gap in the literature on the subject, creating new standards for conducting scientific research in the field of management and quality. It can also provide a basis for the development of a new paradigm in management sciences, combining best practices from various fields of science.
Evolutionary Integration of Evidence-Based Medicine (EBM) Methodology with Management Sciences
The integration of methodologies developed in evidence-based medicine (EBM) with management sciences requires consideration of various information sources that together form a coherent and comprehensive foundation for decision-making. One of the key elements is scientific research, which provides reliable evidence of the effectiveness of methods and strategies. Rigorous analyses, such as meta-analyses and systematic literature reviews, serve as a solid reference point, helping to avoid subjectivity and reliance on unverified assumptions.
Another important source of information is grey literature, which includes reports, working papers, presentations, and other materials not considered traditional academic publications. Although often less formal, grey literature provides practical context and data that can be crucial for analyzing management realities, especially in situations where scientific publications are lacking.
Equally important are organizational data, which come from internal resources of the organization, such as operational analyses, performance indicators, and employee satisfaction survey results. This data allows decisions to be tailored to the unique conditions and challenges faced by a given organization, enhancing the effectiveness of implemented strategies.
Expert opinions should also be taken into account as a valuable source of practical knowledge and intuitive insights. Experts with experience in a specific field can provide guidance that may be difficult to find in literature or numerical data, particularly in the context of innovation or crisis management.
Stakeholder interests also play a crucial role in the decision-making process. Evidence-based management requires understanding and considering the needs, expectations, and preferences of individuals and groups involved in the organization's activities. This allows the development of strategies that are not only effective but also accepted and supported by stakeholders.
The final, but equally important, aspect is cultural factors. Every organization operates within a specific cultural context, which influences decision-making, communication, and the implementation of changes. Considering cultural aspects allows strategies to be better tailored to local conditions and minimizes the risk of resistance to change.
The integration of these six information collectors creates a foundation for evidence-based management, which, much like in medicine, enables decision-making that is highly effective and contextually appropriate.
The Evidence-Based Management Canvas (EBMc) is a tool that helps organize the decision-making process in an organization systematically. It is based on analyzing reliable data and evidence from various sources, such as scientific research, organizational data, expert opinions, and the values or expectations of stakeholders. It enables managers to make effective, thoughtful, and justified decisions that enhance the efficiency and accuracy of actions within the organization.
Evidence-Based Management Canvas (EBMc)
It enables managers to make effective, thoughtful, and justified decisions.
1. Problem Question
Identifying the key management issue or challenge. This stage involves:
Thorough understanding of the organizational context and the situation in which the problem arises.
Formulating the question clearly and precisely to enable targeted evidence searching.
Determining the objectives to be achieved and the criteria that will indicate success in solving the problem.
Ensuring the question is relevant to stakeholders and aligns with the organization's strategic goals.
2. Data and Evidence
Gathering evidence from six sources:
Scientific Research: Review of scientific literature, such as peer-reviewed articles, meta-analyses, and research reports. It is important to consider studies on organizational behavior and the specificities of local and global markets.
Gray Literature: Government reports, white papers, conference materials, and industry organization studies that often provide the latest practical data.
Organizational Data: Internal company data, including operational reports, financial results, KPI analyses, or employee and customer satisfaction data, helping identify current challenges and trends.
Expert Opinions: Recommendations from practitioners and industry specialists who can provide in-depth insights based on professional experience. Critical thinking is essential to avoid bias.
Stakeholder Values and Expectations: Understanding the needs and priorities of individuals involved in the decision-making process, both internally and externally. Including their perspective builds engagement and acceptance of implemented solutions.
Cultural Aspects: Analysis of norms, values, beliefs, and practices characteristic of the organization, industry, and broader environment in which the company operates.
3. Data Analysis
Analyzing collected evidence using quantitative and qualitative methods.
4. Conclusions and Recommendations
Developing and prioritizing data-driven recommendations.
5. Implementation
Implementing the recommendations in practice and monitoring outcomes.
Scientific research is the foundation of progress in science, but the question of whether it should be conducted individually or in teams remains relevant. The same applies to social sciences. The word social here plays an object role.
Max Ringelmann's 1913 experiment on tug-of-war can serve as a starting point for analyzing the impact of a group on the effectiveness of research work. This study reveals an interesting phenomenon related to the diffusion of responsibility, which can be crucial in the context of teams (including research teams). It is also worth considering what psychological mechanisms might influence research outcomes in a group, including the application of theories related to motivation and group dynamics.
Max Ringelmann’s Experiment
Max Ringelmann conducted an experiment comparing the effectiveness of individual and group tug-of-war. The results showed that participants who pulled the rope in a group exerted less force than those who participated in individual competitions. This phenomenon, called the Ringelmann Effect, indicates a decrease in effort in group situations, which stems from the "diffusion of responsibility." When people work together, they often feel that their individual contribution is not crucial to the final result. This can lead to reduced engagement and, consequently, lower group effectiveness.
Image by Ron Lach: pexels.com
Other Psychological Models
When analyzing the impact of teamwork on scientific research, it is useful to consider two other psychological models that can explain why working in a group may lead to less engagement:
Diffusion of Responsibility Theory (Latané, Williams, & Harkins, 1979)
According to the diffusion of responsibility theory, when people work in a group, they tend to reduce their effort because they feel their individual contribution will not significantly affect the outcome. This effect is particularly pronounced when group members do not have clearly assigned roles or responsibilities. In scientific research, where each team member contributes their knowledge and skills, the lack of individual responsibility for the whole project can lead to decreased motivation and lower engagement in the task.
Social Proof Theory (Cialdini, 1984)
According to this theory, people in a group often follow the behavior of other members, especially in situations where they are unsure of how to behave. In the context of scientific research, this can lead to a situation where group members conform to the expectations and standards of other researchers, which can reduce creativity and originality in their work. Rather than actively engaging in searching for new solutions, researchers may rely on behaviors and methods already used by others, diminishing their individual contribution.
Individual Work vs. Team Collaboration
Based on the Ringelmann Effect and the aforementioned psychological theories, conclusions can be drawn about the advantages and disadvantages of individual and team-based work in scientific research:
Benefits of Individual Work:
Working individually allows for complete control over the research process. The researcher independently makes decisions regarding methodology, data analysis, and the direction of research. Additionally, it may lead to a deeper understanding of the subject, as the researcher has more freedom to explore specific issues. Moreover, the lack of external expectations fosters greater creativity and independence.
Benefits of Team Collaboration:
On the other hand, collaboration in a research team allows for the exchange of ideas, integration of different specialized skills and experiences, which can lead to more comprehensive and accurate research. However, as shown by the Ringelmann Effect, it is crucial for the team to be well-organized, with clear responsibility for results, to avoid diffusion of responsibility and decreased engagement.
The Two Pizza Rule
In the context of organizing effective research teams, it is also worth mentioning the so-called Two Pizza Rule, proposed by Jeff Bezos, the founder of Amazon. This rule states that teams should have as many members as can be fed with two pizzas. This means the team should consist of 4 to 6 members to be small enough to enable effective communication and collaboration but also large enough to have a diverse set of skills and perspectives. In scientific research, this rule emphasizes the importance of maintaining small, agile groups that can quickly make decisions and focus on specific tasks, minimizing the risk of reduced engagement and diffusion of responsibility.
“Together, with full responsibility – every strength matters.”
Summary
Max Ringelmann's experiment and psychological models such as the diffusion of responsibility theory and social proof theory show that scientific research conducted in groups may encounter challenges related to motivation and engagement among team members. In individual work, the researcher has full control over the project, which leads to greater engagement. However, in the case of complex research, teamwork can yield better results, provided that each team member is responsible for their contribution and engagement. Ultimately, the key to success lies in the proper organization of team work and clear division of responsibilities.
Elizabeth Mieczkowski, Cameron Turner, Natalia Vélez, Thomas L. Griffiths, *Many Hands Don’t Always Make Light Work: Explaining Social Loafing via Multiprocessing Efficiency*, “Proceedings of the Annual Meeting of the Cognitive Science Society” (2024), pp. 5958–6005, Link.
The reality of management science is based on solid methodological foundations that ensure the reliability, objectivity, and effectiveness of research and scientific processes. Merton's norms, developed by Robert Merton, introduced principles that form the foundation for a scientific approach to research in various fields. However, in the context of management science, there is also a need to consider the counter-norms proposed by Ian Mitroff, which address practical challenges that may arise in organizational reality. This article discusses the application of Merton's norms and Mitroff's counter-norms in the context of management Science, highlighting their significance in ensuring the effectiveness of organizational processes.
Merton's Norms
Robert Merton introduced four key principles (here presented as five, in a later variation) that are fundamental to science and research, and their application in the field of management science can serve as the foundation for an effective approach to innovation, development, and process improvement. In the context of management, these principles help ensure the reliability and transparency of decision-making and maintain high standards in the management science.
1. Universality
The principle of universality assumes that the results and decisions regarding quality management and organizational processes should be evaluated based on uniform, objective criteria, regardless of the identity of the person making the decision. In practice, this means that methods for assessing quality and management effectiveness must be universal, so they can be applied in different contexts and organizations.
2. Objectivity
The principle of objectivity means that decisions based on measurement results and analysis should be made independently of personal preferences, interests, or external pressures. Objectivity guarantees that the evaluation and improvement processes will be conducted based on actual data, not subjective opinions.
3. Skepticism
Scientific skepticism means that all solutions and processes must be regularly evaluated and verified, even if they have been considered effective in the past. The principle of skepticism allows organizations to avoid stagnation and adapt to changing market, technological, or social conditions.
4. Organization
The principle of organization refers to collaboration within the scientific community, but in the context of management and quality, it also has an organizational dimension. This means that quality management and innovation processes must be carried out collaboratively, with the exchange of information and a common effort toward excellence.
5. Public Accessibility Scientific knowledge should be available to everyone. Research results must be published and shared with the broader scientific community so that they can be further analyzed, criticized, and developed.
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Mitroff's Counter-Norms
While Merton's norms represent a theoretical ideal, real scientific processes often encounter challenges that lead to the application of counter-norms proposed by Ian Mitroff. Mitroff pointed out that in practice, these norms are not always adhered to, which may lead to the introduction of new rules that better reflect the complexity of modern organizations.
1. Relativism
Mitroff highlights relativism, which arises in organizational practice when research results or quality decisions are evaluated through the lens of the interests of the organization. Instead of universal criteria for evaluation, decisions may be based on local, political, or economic conditions.
2. Subjectivism
In management, subjectivism may occur when decisions regarding quality assessment are based on the preferences and personal beliefs of those responsible for the processes. Instead of using objective assessment tools, processes may be shaped by the subjective opinions of managers or project leaders.
3. Dogmatism
Dogmatism in management means relying on previously accepted solutions without verifying their effectiveness in new conditions. In organizations that adopt a dogmatic approach, there is a strong tendency to maintain old methods despite emerging new challenges.
4. Isolationism
Isolationism in management may occur when different departments within an organization do not collaborate on quality improvement, leading to discrepancies in approach and a lack of consistency in processes.
5. Public availability
The public availability of research results may be limited in cases where sensitive data, intellectual property or commercial interests prevent their full publication.
Conclusion
Merton's norms and Mitroff's counter-norms provide valuable insights into how any science, including management science, should be conducted. While Merton's principles represent an ideal model for striving for objectivity, universality, and transparency, Mitroff's counter-norms remind us of the realities in which organizations must adjust their approach to changing market, technological, and political conditions. Understanding these two perspectives allows for better management, providing a balance between the ideal scientific approach and the demands of business practice.
The modern world of management faces a vast amount of data and theories that aim to support decision-making. However, not all approaches are based on solid scientific foundations. Often, theories appear credible but, upon closer examination, prove to be pseudoscientific, parascientific, or protonscientific. Distinguishing these approaches and understanding how to use evidence-based management (EBM) in management is crucial for making effective decisions. To better understand these differences, let’s examine controversial theories such as wandering RNA and the structure of water, and their relevance to management practice.
We believe that in an era of easy access to information and frequent encounters with pseudoscientific theories, our role as responsible professionals is to clearly distinguish science from pseudoscience, parascience, and protonscience.
Pseudoscience, Parascience, and Protonscience
Pseudoscience
Pseudoscience refers to a set of theories or claims that present themselves as scientific but lack solid evidence and fail to meet basic methodological standards. Pseudoscience often lacks the verifiability of results, and the hypotheses are untestable or disproven by experimental evidence.
Example in management: The idea that an organization can achieve success solely through a so-called "secret formula" based on unverified thoughts or popular but unconfirmed ideas (e.g., manipulation of "organizational energy").
Parascience
Parascience is a field that is not fully recognized by mainstream science but still remains within the scientific interest. It often lacks the standards required to be considered full science but may be helpful in certain research or experimental cases.
Example in management: Theories based on popular but unconfirmed research suggesting that certain motivational techniques (such as affirmations or "sound therapy") can be effective in management, despite lacking full confirmation in studies on management effectiveness.
Protonscience
Protonscience refers to an area that lies on the border between science and pseudoscience but has the potential for further development and could lead to real science. Protonscience does not yet meet all scientific requirements but research in this area may provide new tools to understand phenomena.
Example in management: Research on organizational psychology that begins to study subjective factors influencing employee performance but is not yet fully confirmed, such as studying the impact of "employee feelings" on their results, which needs further validation in different organizational contexts.
Image by Abby Chung: https://www.pexels.com
Wandering RNA and the Structure of Water
Wandering RNA is an example of pseudoscience because this theory lacks experimental confirmation and does not meet the methodological standards of science. Claims that RNA can wander freely between cells in a way unrelated to existing biological mechanisms are an example of speculation that lacks solid scientific support. From the EBM perspective, such theories would have no place in decision-making processes because they are not based on reliable evidence.
Application in management: Using theories that lack solid scientific evidence can lead to wrong decisions by management. For example, implementing unconfirmed motivational or developmental methods that may turn out to be ineffective, instead of using proven management practices based on evidence.
Theories about the structure of water, which suggest that water has "memory" and can store information (e.g., in homeopathy), are classic examples of parascience. While research on water and its properties is fascinating, there is still little evidence to support this theory. Water may have some structures that are an interesting area of research, but claims that it can store information are speculative and lack solid evidence.
Application in management: Just like with pseudoscience, the use of theories that are not confirmed by evidence can lead to ineffective solutions in organizations. In management, using unproven theories (e.g., related to organizational psychology or motivation) can result in poor decisions that do not deliver the expected outcomes.
Evidence-Based Management (EBM)
Evidence-Based Management (EBM) is an approach that relies on solid scientific evidence when making managerial decisions. EBM requires organizations to use available research, data, and experiences to make informed decisions. This approach is the opposite of using pseudoscientific, parascientific, or protonscientific theories, which can lead to erroneous conclusions.
EBM example: Instead of relying on unverified ideas, organizations using EBM base their decisions on data regarding employee performance, organizational outcomes, and scientific research to implement practices that have been proven to be effective.
An essential element of every research process, occurring at almost every stage of a researcher's work, is the review and analysis of the subject literature. The aim of the training is to draw attention to how to organize literature using databases in combination with modern technological tools, such as bibliography managers (RMS, Reference Management Software). Such activities, which will soon become a permanent element of scientific research methodology, should be of particular importance in the discipline of management and quality sciences, the main goal of which is to search for newer and newer solutions that affect better organization and greater efficiency of performed tasks and works.
Framework Program
Citavi: program installation and cooperation with plugins for Adobe and WORD,
Citavi: automation of creating a copy of the project,
Citavi: adding and describing sources of offline publications,
Citavi: adding and describing sources by DOI number, ISBN,
Citavi: adding and describing sources from a pdf file,
Citavi: searching in databases or directories,
Citavi: importing and exporting sources between Citavi, ORCID and Google Schoolar,
Chrome browser: adding publications using Citavi Picker,
WORD: embedding citations from sources in the style of the Oxford Referencing System, Harvard Referencing System: converting in-text citations to citations in footnotes and vice versa.
Price: by agreement or barter Training language: Polish
Certificate based on § 23 ust. 4 Rozporządzenia Ministra Edukacji i Nauki z dnia 6 października 2023 r. w sprawie kształcenia ustawicznego w formach pozaszkolnych (Dz. U. z 2023 r., poz. 2175).
Trainer: Piotr Janulek, PhD. Entrepreneur since 1997, building bridges between education and business. Runs the akademia-nauki.eu platform, focusing on accounting, communication and personal development. Interested in social media, accounting, innovative business models and evidence-based management.
Scientific evidence is the foundation for reasoning and decision-making in various fields, such as medicine, psychology, and education. To understand how to assess the reliability and credibility of evidence, it is worth looking at the evidence hierarchy, which divides evidence into five levels, depending on the methodology and nature of the studies. Below is a detailed description of each level.
The highest level contains evidence from experimental studies, such as randomized controlled trials (RCTs) and their systematic reviews with or without meta-analysis. These studies are characterized by the highest methodological quality, because random selection of participants and control of variables minimize the risk of systematic errors. Additionally, meta-analyses allow the results of multiple studies to be combined, which increases statistical power and precision of conclusions.
Level II: Quasi-experimental studies
The second level includes quasi-experimental studies, systematic reviews combining the results of RCTs and quasi-experimental studies, and quasi-experimental studies alone, with or without meta-analysis. Although the lack of random assignment of groups may introduce the risk of bias, such studies still provide valuable information, especially in situations where RCTs are not feasible.
Level III: Non-experimental studies and systematic reviews
The third level includes non-experimental studies, such as observational studies, systematic reviews of RCTs, quasi-experimental studies, and qualitative studies with or without meta-synthesis. The inclusion of qualitative methods allows for a deeper understanding of social and behavioral phenomena, but the lack of control over variables may limit the generalizability of the results.
Level IV: Opinions of recognized authorities
The fourth level refers to the opinions of respected authorities, reports of expert committees or consensus panels based on scientific evidence. Although this type of evidence does not come directly from empirical research, it is important for decision-making, especially in areas where solid empirical data are lacking.
Level V: Literature Reviews and Expert Experience
At the lowest level are literature reviews, program evaluation reports, financial analyses, case studies, and expert opinion based on experience. Although this evidence is of limited strength, it is often a starting point for more rigorous research.
The Importance of the Evidence Hierarchy
Understanding the different levels of evidence allows us to critically evaluate the credibility of information and make decisions based on scientific evidence. In practice, this hierarchy helps researchers, practitioners, and policymakers choose the best available evidence to address specific problems.
This approach helps us build a solid foundation for science, education, and public policy while promoting transparency and effectiveness in our actions.
Evidence-based management has not yet been considered in the context of practical managerial decision-making systems that leverage modern information technologies and big data. While management and quality sciences have always focused on application solutions, modern technologies have significantly broadened the scope of utilizing scientific research results in managerial decision-making.
Our aim is to demonstrate the rationale for developing a new theory of business decision-making systems based on large datasets of scientific research results. New management concepts, ideas, and messages should be created, developed, and refined through a systematic and interdisciplinary approach to evaluating solutions described in carefully selected scientific publications.
The modern world is characterized by a multitude of views and beliefs. In societies that value freedom of speech and individualism, there is a tendency to treat truth as something subjective, determined on the basis of personal experiences, beliefs, or emotions. If everyone has the right to their own truth, is there any point in scientific research? Does research that allows us to discover new facts and solve puzzles have any justification in such a world? The answer is yes, because the pursuit of truth is based on the assumption that there are better answers than others, and truth is not just the effect of a subjective view of reality.
Objective vs. subjective truth
In a situation where all views are treated as equal and equally valuable, a fundamental question arises: why undertake any scientific research? If there is no single objective truth, then why analyze the histories of the past, investigate the causes of contemporary conflicts, or search for new methods of treatment? In such reasoning, all answers may seem equally important, and each form of knowledge equally valid. However, what distinguishes science from relativism is the assumption that there are objective, measurable criteria for evaluating different views, discoveries, and theories.
Scientific research only makes sense when verifiable answers are more accurate than others, when there is something behind the truth that is independent of our subjective preferences.
Also in the case of archaeological excavations, medicine, or research on the Universe, the goal is to discover objective facts that, regardless of who examines them, will be an invariable part of our knowledge of the world.
Science as the pursuit of better answers
One example that perfectly illustrates the meaning of research is medicine. The search for a cure for cancer is an issue in which research can really change the lives of millions of people. If all views on cancer treatment were treated equally, we would not make an effort to develop effective therapies. Medical knowledge develops on the basis of scientific evidence that indicates the effectiveness of specific treatments and rejects others that turn out to be ineffective or harmful.
We are dealing with a similar logic in the case of analyzing the causes of tensions in the Middle East. To understand why conflicts occur, we need to go beyond subjective interpretations and learn about the objective factors – historical, political, social – that influence them. It is this research that allows for effective diplomatic and aid actions.
Discovering the truth about the Universe
The same is true for space exploration. Man has always been curious about what lies beyond our planet. When we begin to explore the Galaxy, we ask about its structure, origins, and the mechanisms that govern its functioning. There is no point in seeking answers to these questions if we do not believe that there is an objective truth that can be discovered. Understanding the Universe is not about subjective interpretations, but about discovering the laws of physics that govern reality, regardless of whether someone believes in them or not.
Seeking truth as the fundamental goal of human action
Every field of science aims to discover the truth – not only to expand our knowledge, but above all to improve the quality of life, increase understanding of the world, and facilitate solving global problems. If we assumed that every truth was equally good, we would not make any effort to find out which answer is more accurate, more evidence-based, and more useful in practice.
Truth, while difficult to grasp, is not a product of subjective beliefs. It is an independent, objective reality that can be discovered through research, experimentation, and analysis. When we allow everyone to determine their own truth, we give up the pursuit of knowing reality in a way that is independent of personal preferences. The effort that scientists, researchers, and doctors make to discover what is objectively true only makes sense if we believe that there are better answers than others, and that truth is something to strive for.
Foto: Luis Quintero: https://www.pexels.com/pl-pl/zdjecie/czlowiek-dotyka-ksiazki-2258252/
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