Evidence-Based Management: A Design Theory,Template,and Technology for a Knowledge Delivery Platform

Abstract

We take a theory-driven approach to designing an evidence management system. The ultimate purpose is to promote evidence-based management. The immediate purpose is to capture, curate, maintain, and deliver evidence gathered from both management research and management practice in a format usable by both humans and intelligent systems. The unified theory of acceptance and use of technology (UTAUT) and the theory of planned behaviour serve as the framework for the design, resulting in a design theory and empirical propositions. Our design offers a platform to facilitate ongoing communication between researchers and practitioners as well as a trustworthy mechanism for delivering the evidence to the end user in real-time. It comprises an evidence template, a wiki platform for bidirectional communication, and blockchain technology for ensuring trust among evidence users.

Keywords

Evidence-based management evidence management system design theory research-practice gap

Organizational knowledge management (KM) practices have been largely unsuccessful in capturing, evaluat- ing, and sharing knowledge created by researchers and knowledge possessed by managers in a systematic and sustainable manner.¹ Academic research has not adequately met the knowledge needs of practice, leading to an ever-present gap between researchers and practitioners.^{2, 3} This shortfall has diminished the impact of business education and research on management practice.^{4, 5} In other words, the gap between management research and practice remains unbridged.⁶ Many attempts to bridge the gap have resulted in several suggestions.⁷ These include action research,⁸ Mode 2 research,⁹ and engaged scholarship,¹⁰ all of which focus on addressing real-life problems. Design science suggests adding a design mode to the more traditional scientific methods to generate and evaluate solutions to problems.^{11, 12} Many collaborative approaches bring together researchers and managers to cocreate⁶ and coproduce¹³ knowledge with practical value.

All of the approaches mentioned above offer progressive solutions that have the potential to close the gap.⁶ However, most fall short at the implementation stage¹³ for the following reasons: There were ontological and epistemological differences between research and practice,^{6, 14} as well as differences in emphasis between rigour and relevance.⁷ In addition, there were no mechanisms to maintain ongoing collaboration beyond a researcher and an organization.¹⁵ The collaboration mechanisms were not scalable to a macro level for usable knowledge to be generated and made available to the profession in real-time. Currently, no dynamic system exists that is capable of constant updates to functional knowledge. As such, the validity of the knowledge and its trustworthiness could not be guaranteed to the satisfaction of the end user.

Evidence-based management (EBMgt) is one of the more recent approaches that includes principles, processes, and practices to enhance the quality of decision-making in real-life situations.¹⁶ Because it is central to our discussion, we elaborate on the concept of EBMgt.

Evidence-Based Management

Although evidence-based practice originated in healthcare,¹⁷ using the best available evidence for decision- making is vital for success in many professions such as law, engineering, and management. Evidence-based practice is a disciplined approach to decision-making based on evidence that integrates scientific and professional knowledge sensitive to stakeholder values and preferences.¹⁸

Rousseau^{16, 19} makes the distinction between knowledge that originates from research (scientific knowledge) and knowledge that arises from practice (professional or local knowledge). Researchers synthesize scientific knowledge, which should be further complemented by situational information, stakeholder concerns, and practitioner experience in generating evidence for general use by the community.²⁰ This complementarity is attainable only through ongoing collaboration between the scientific and professional communities. However, the collaboration between researchers and practitioners in business management is quite limited.¹ There are reasons for this fragmentation. The problems identified by management researchers often have limited practical significance, and their recommended solutions have limited practical application.²¹ In addition, the scientific and professional communities have no common platform to exchange ideas.⁷ The resulting isolation renders practitioners unaware of research knowledge³ that could be helpful in their decision-making process. Consequently, the scaling up of knowledge to evidence-based practice and its systematic codification, common in healthcare, rarely happens in management.¹⁵

The concept of EBMgt^{19, 22} was introduced to bridge the gap between researchers and managers. EBMgt posits that scientific knowledge from researchers and practical knowledge from managers collated in the form of a systematic review (SR) and made available in real-time can improve decision quality and outcomes for organizations.¹⁵ This calls for an ongoing mechanism for the two communities to exchange ideas.⁶ Collaboration between researchers and practitioners is central to successful EBMgt.^{18, 23}

While EBMgt provides a robust collaborative solution with an institutional structure and a usable product,¹⁸ the delivery mechanism requires improvement.¹⁵ Our intention here is to move the solution forward by offering a design, technology, and delivery mechanism to gather and combine scientific and professional knowledge into usable evidence and make it available to practicing managers in a trustworthy manner to enable evidence-based decision-making in real-time.

Much of the existing discussion on EBMgt is predicated on the assumption that the evidence will be used by human agents and has resulted in creating a template for evidence for human use.¹⁵ Considering the tremendous progress over the past decade in machine learning (ML) and artificial intelligence (AI), we foresee that AI systems will likely use such evidence for decision-making. Most business processes have already been automated in large organizations; hence, we need information systems to codify business evidence that both humans and machines can interpret. This is crucial to any knowledge management system (KMS). We shall refer to these KMS as evidence management systems (EMS), which we believe are a vital component of EBMgt practice.

A design theory is required to support the construction of an EMS artefact. Design theories are theorized practical knowledge that give knowledge support to the design process²⁴ and provides guidance for building an artefact.²⁵ Consequently, we propose a design theory for EMS following an established framework²⁶ consisting of kernel theory, meta-requirements, meta-design, and empirical propositions for meta-design validation.

Motivation for a Knowledge Management System

In recent times, businesses have engaged in deploying KMS²⁷ based on the proposition that knowledge is a strategic organizational resource and a vital source of competitive advantage. KMS enable organizations to create, capture, locate, and share organizational knowledge.^{28, 29} KMS, therefore, incorporate information technology- enabled initiatives containing the conception of searchable document repositories, expertise databases, development of decision support and expert systems, and the hardwiring of social networks that facilitate access to resources for non-collocated individuals.³⁰ The combination of these resources would provide the best available evidence for practitioners, which is essential for managerial decision-making. For this reason, organizations have explored wiki technology to enable their KMS.

Wikis can be a useful tool in providing EBMgt for effective organizational decision-making. However, there are some pertinent questions about whether the knowledge created and made available through the wiki is the best evidence; that is, can the users of KMS trust the currency or reliability of the knowledge? Furthermore, wikis may be created or modified by individuals who are not competent enough to contribute or edit the knowledge stored and displayed within the system. In addition, some knowledge creators may have a malevolent intent to create dubious content. This is a phenomenon that is often observed in generating “fake news.”

These challenges tell us that wiki-enabled KMS can only succeed as EBMgt if knowledge creation, storage, and modification are envisioned as a data management technology that can be trusted and used effectively in decision-making. The driving technology should allow users to access the database and construct the best available evidence to be shared among the participating parties. Blockchain is a technology that can ensure trusted data management. Blockchain is a distributed database of records known as a public ledger that contains all the transactions (digital events) executed and shared among the participating parties and is transparent.³¹ Although this technology’s operational details are beyond this article’s scope, we propose a blockchain-enabled, KMS-based wiki, guided by EBMgt principles, to provide trustworthy and current knowledge to practicing managers and enable competent managerial decision-making.

A Framework for Knowledge Management

Management of organizations has become increasingly dependent on knowledge—both systematic knowledge produced through research and tacit knowledge of managers—which is often embedded in unique contexts.¹³ Organizations have implemented a variety of approaches and systems for effective and efficient deployment of knowledge to manage their operations.³² The classic view of the data-information-knowledge hierarchy in the KM value chain conceived knowledge as an individual’s state of mind.³³ The knowledge-based theory of the firm posits knowledge as the know-how of the firm.³⁴ Subsequently, with the growing popularity of the World Wide Web, the emphasis has shifted from know-how to “know-where” and “know-when,” underscoring the importance of timely access to information.³³ The paradigm shift introduced by data mining (DM), ML, and AI has transformed knowledge into a capability to predict future events and engage in timely action.³⁵ This has become the new value proposition driving modern business.

Knowledge needs to be rigorous, relevant, and actionable to be useful in the field.¹⁵ Knowledge that is created, maintained, modified, and used within organizations becomes the evidence for making decisions. This is the combination of scientific and professional knowledge referenced earlier. It is macro-level knowledge that transcends the level of the organization and is of broad value to corporations, governments, and other institutions of society that are connected to business. Micro-level knowledge generated at the firm level or across a sample of firms can be scaled up to the macro-level by an SR of many relevant studies that focus on a shared phenomenon or relationship.^{15, 18} For example, empirical studies conducted at individual organizations exploring the relationship between absence and performance can be gathered together in the form of an SR. The results may reveal a more sustainable relationship that can be simultaneously useful to a practicing manager interested in absence management or a theorist interested in exploring an enduring connection between absenteeism and productivity. However, for the knowledge to be of value to the practicing manager, it must incorporate relevant field evidence gathered from practitioners,¹⁸ not just theoretical evidence captured by researchers.

Scientific community members produce rigorous evidence based on research called Evidence with a capital “E.” In contrast, professional community members have access to relevant evidence from their practice, known as evidence with a small “e.”¹⁹ The two communities need to collaborate to generate evidence that is rigorous, relevant, and actionable.¹⁵ Evidence theory suggests that the strength of such evidence is contingent upon its fit with the context, its replicability, and its transparency.²⁰ Ongoing collaboration between the two communities is vital in creating and maintaining such evidence.^{2, 6,13}

We have so far discussed the motivation for the work, identified a gap between management knowledge and management practice, offered a critique, and suggested EBMgt as a solution for closing the gap. The need for EMS has been articulated, and KM as a conceptual framework for EMS has been developed. In what follows, we propose a design theory for EMS that includes the kernel theory, meta-requirements, meta-design, and empirical propositions for meta-design validation.^{26, 36} The unified theory of acceptance and use of technology (UTAUT)³⁷ and the theory of planned behaviour (TPB)³⁸ are presented as the kernel theories for design guidance. Next, we explore the meta-requirements—the class of goals informed by kernel theory. This is followed by a description of meta-design—a class of artefacts that satisfy the meta-requirements and the procedures for their construction. Finally, we propose a set of empirical propositions to assess whether the design artefact satisfies the meta-design.

Knowledge Management and the Unified Theory of Acceptance and Use of Technology

Nonaka and Takeuchi³⁹ developed a research framework that explains how new knowledge is created. There are two components to this framework: the epistemological and the ontological. The first one stands for the attributes of knowledge, which completely separate tacit knowledge from explicit knowledge. The essence of creating new knowledge is to use and modify tacit knowledge. Nonaka and Takeuchi³⁹ believe that knowledge is created when tacit and explicit knowledge intersect and interact with each other through a process of socialization, externalization, combination, and internalization (SECI). The second part of Nonaka and Takeuchi’s framework is ontological. This part looks at the levels of entities or mechanisms that create knowledge and can start the SECI processes, such as people and organizations. For knowledge to be created, the epistemological and ontological dimensions must converge and work together.

While Nonaka and Takeuchi³⁹ developed a theoretical framework for creating and using knowledge, they did not elaborate on the facilitating conditions that made knowledge creation possible from an ontological point of view.⁴⁰

We explore these facilitating conditions with the help of UTAUT.³⁷ The primary objective of the UTAUT is to illustrate why an end user wishes to use an information system and how they intend to use it. Four key constructs directly impact usage intention and behaviour: performance expectancy, effort expectancy, social influence, and facilitating conditions such as gender, age, experience, and choice in usage.³⁷ We will discuss them in detail when presenting the model. In the following sections, we combine and contextualize Nonaka and Takeuchi’s KM framework³⁹ with UTAUT theory³⁷ to determine whether the end-user will be interested in using the blockchain-enabled KMS wiki.

UTAUT provides a solid foundation and is a valuable tool for determining how likely new technologies and ideas will work. It also helps determine what makes people use, accept, and adopt new technologies. Researchers in the recent past have used the UTAUT to study the behavioural intention of end-users of different ages, genders, and levels of experience to accept a wide range of technologies and innovations,^40–44 including blockchain technology.^{31, 45} Blockchain is shaping up to attain optimum maturity level.⁴⁶ We posit that blockchain technology-enabled KMS wikis can be explained with the help of UTAUT. As mentioned earlier, the EMS must be trustworthy. This involves incorporating trust into the system, and blockchain is a technology that ensures trust and transparency in business transactions.

Trust, as a construct, has been used in systems of e-government,⁴⁷ e-banking,⁴⁸ e-commerce,⁴⁹ and supply chain transparency.⁵⁰ For the purposes of our study, “trust” is defined as having faith in technology manifested in belief in its functionality, helpfulness, and reliability.^{31, 51} We reiterate the fact that several theorists have suggested that trust evolves over time based on a series of observations and interactions. This invokes focus on the critical issue of the process by which trust evolves.⁵²

Functionality is the set of skills, abilities, and features needed to complete a required task; helpfulness is providing users with the most relevant and useful assistance when needed, while reliability ensures that the technology performs accurately and consistently every time. Thus, with the help of a blockchain-enabled KMS wiki, end-users can create and modify knowledge with the trust features of functionality, helpfulness, and reliability incorporated within the system. In what follows, we elaborate on the application of UTAUT theory in designing an EMS.

Theory and Design of an Evidence Management System

As our interest converges on managerial behaviour, we believe evidence-based decision-making will improve firm performance.¹⁷ Consequently, we would like to steer management decision-making behaviour towards evidence use. It is well established that intention drives behaviour.^{53, 54} The TPB suggests that such intention is influenced by the ease of effort and one’s belief that effort will lead to positive performance outcomes.³⁸ Behavioural norms further shape the intention within the social system and the perceived control one has over one’s own behaviour.³⁸ In contrast, social influence is sustained by trust in the influencers.⁵⁵ This framework further buttresses UTAUT,^{37, 55} as adapted and shown in Figure 1.

Figure 1.

Theoretical Framework for EMS Design.

To enable our effort to influence managers to use the best available evidence in their decision-making, we start with the assumption that the intention to use evidence in managerial decision-making is a function of

the ease of effort to access evidence (referred to as effort expectancy),

the belief that effort would lead to anticipated performance outcomes via evidence use (referred to as performance expectancy),

the norms that favour evidence use in decision- making (referred to as social influence),

the mechanisms of behavioural control that ensure decision use, and

trust in the system itself, as shown in Figure 1.³⁷

While these constructs were introduced earlier, our motivation here is to design a system that would facilitate the coalescence of these parameters towards evidence-based decision-making.^{20, 37} UTAUT and TPB serve as the theoretical framework, providing a conceptual lens for our design.

Meta-Requirements of an Evidence Management System

KMS are enabling technologies that support the creation, storage, retrieval, transfer, and application of knowledge.³³ KMS consist of an IT infrastructure, collaboration technologies such as discussion forums, shared databases, and wikis, as well as mechanisms to integrate the various features.⁵⁶ KMS do not represent a single technology, and the implementation depends upon organizational needs. Potentially valuable knowledge exists in many formats. Scientific knowledge often exists in an explicitly codified form, while practical knowledge is mostly the tacit knowledge of individuals embedded within a specific context. EMS should be able to assist the research community in systematically validating tacit knowledge and externalizing it into an explicit form.

Evidence, whether communal or macro in scope, combines the explicit and articulated knowledge of individual organizations—also called semantic memory.⁵⁷ Firms must combine internal and external intelligence from multiple stakeholders to manage their dynamic internal environment.⁵⁸ Sharing resources requires accurate and unambiguous codification and a machine-readable structure. Though EMS as a knowledge community resource can potentially benefit everyone, groups that contribute knowledge may try to control the evidence for proprietary leverage. A mechanism is needed to ensure evidence’s communal nature and guarantee authenticity so that all user groups trust and use it.

The meta-requirements for an EMS can be summarized as follows:

Information must be collected in a structured form from as many stakeholders as possible.

Knowledge flow must be bidirectional. Evidence users (i.e., managers in the business field) give practical recommendations, and experts (i.e., institutional researchers in business) systematically aggregate and curate evidence.

EMS must enforce a uniform ontology to minimize ambiguity.

EMS, as a platform, must offer a mechanism to ensure trust when the information is collected.

Next, we describe the meta-design to support these requirements.

Meta-Design Model

Meta-design is a class of artefacts that could meet the meta-requirements embedded in kernel theory.²⁶ We represent our meta-design in the form of a design-relevant explanatory/predictive model (DREPM)⁵⁹ that shows how the class of artefacts we propose meets the meta-requirements of our theoretical framework, as shown in Figure 2. The artefacts are the component mechanisms that operationalize the theory.

Figure 2.

Meta-design for EMS.

We propose the following components for our meta-design:

A structured, systematic review (SR) template for EBMgt.

A wiki platform for collaborative bidirectional information collation.

An ontology based on value theory to ensure uniform terminology.

The blockchain platform to ensure trust.

Structured Templates for Evidence-Based Management

HakemZadeh and Baba¹⁵ suggest a template that captures the best practices in business research with well-defined sections such as title, introduction and background, methodology, and measurement. This design ensures rigour, relevance, and actionability of evidence for business management. Evidence captured with an EBMgt template consists of similar standardized sections, including a title, introduction and background, methodology, related outcomes of interest, etc. The structured format ensures consistency and facilitates the use of evidence in decision-making. Natural language processing (NLP) techniques such as text summarization and topic modelling can extract information from such templates, facilitating knowledge reuse.⁶⁰ The use of a formal markup language such as XML may make structured evidence more machine-readable.

Wiki Design for Collaboration and Knowledge Sharing

Evidence has a moderate life span and should be examined periodically to verify its ongoing validity. Additionally, many stakeholders contribute knowledge that needs to be compiled into evidence by intelligent software agents or human curators. Since evidence is a resource that is collated collaboratively by a knowledge community, a wiki is a natural KMS for managing it.⁶¹

Wikis have been referred to as advanced KMS, providing an alternative to traditional KMS by addressing many of the limitations of the original systems.⁶² Wikis are essentially collectively edited websites created by groups of users on a web browser platform.⁶³ They are different from other websites in that users can modify content independently.⁶⁴

Wikipedia is the most widely known wiki, of course, and is one of the most popular applications on the Internet. Wikipedia democratized the platform by allowing anyone to create a new page and edit existing content, resulting in a “freely expandable collection of interlinked web pages.”⁶⁵ Consequently, there is no one owner of the content contained on these platforms. This feature allows the “combination” of various forms of explicit knowledge, such as documents and images. MediaWiki is another example of a free and open-source wiki platform initially created for use on Wikipedia.⁶⁶ MediaWiki software provides role-based access, giving special editorial rights to curators. The functionality of MediaWiki software can be extended using custom extensions and has built-in support for collaboration.

Wikis facilitate tacit knowledge sharing,⁶⁷ unlike other KMS implementations such as document repositories and discussion forms.²⁹ They augment all four modes of knowledge conversion in a KMS—socialization, externalization, combination, and internalization—as proposed by the SECI model.³⁹ The fact that they can be edited and maintained by a diverse group of users is one of the most appealing benefits of a wiki. A markup language that uses tags to annotate elements within a document makes text editing and formatting easy within a web browser.⁶³ The content’s version history and linked profile pages help identify subject matter experts for “socialization,” and the informal, unstructured markup format facilitates “externalization” of tacit knowledge.⁶⁸ The knowledge captured by wikis is semi-structured. Expert curators enforce minimum quality standards as required for the user community. Hence, the knowledge can be “internalized” and reused by various users. The crowd-sourced nature of wikis keeps the knowledge up-to-date and actionable. For these reasons, we believe the wiki, as a knowledge community platform, is ideal for an EMS.

EMS requires a mechanism for reconciling minimally codified practical knowledge with managers and structured knowledge with researchers. In an ideal EMS, there should be two-way communication between managers and researchers to combine practical knowledge and scientific knowledge and, ultimately, to provide evidence. However, most wiki platforms do not directly support this two-way communication between evidence users (managers) and evidence creators (researchers).

We recommend the following capabilities in the wiki platform to serve as an EMS:

Any new page created on a particular topic should enforce the EBMgt template.

The front page should list potential topics on which research evidence is available or being collected.

Evidence creators should be able to create and update evidence, while evidence users should have the ability to provide comments and suggestions in a separate module from the EBMgt template.

The evidence-users should be able to vote on the importance of these topics. This would work as a feedback mechanism for evidence creators to recognize practically relevant topics.

A wiki with customized extensions could ensure effective and efficient bi-directional knowledge transfer between evidence creators and evidence users.

Next, we discuss how the need for common terminology and trust can be addressed in an EMS.

A Business-specific Ontology

Collation of the E and e harvested from the semantic memory of various groups into community-owned evidence requires a mechanism to define concepts formally and explicitly. An ontology is an explicit, formal specification of terms in a domain and the relations among them.⁶⁹ Formal here refers to the machine-readability of knowledge in an ontology. It helps make domain assumptions unambiguous and enables information systems to capture, evaluate, and share domain knowledge reliably. The domain knowledge of business is complex with its own professional terminology and scientific jargon. Ontology makes knowledge repositories amenable to efficient search and reuse because of the unambiguous terminology and well-defined structure. We believe that an ontology-based search facilitated by EMS will increase the diffusion of complex knowledge even when knowledge users are scattered across the community.⁷⁰

Several independent ontologies define substantive domains in business research, such as accounting, finance, marketing, etc., Resource-Event-Agent (REA) is an ontology for accounting that focuses on the change of value happening in organizations.⁷¹ REA assumes that every business transaction can be conceived as an exchange of value embedded in the resources between two actors. Hence, the core concepts in REA are resource, event, and actor. e3-value is another business ontology covering value exchange between business partners, profitability analysis, and risk management.⁷² The business model ontology (BMO) describes the business model of organizations at the enterprise level, emphasizing internal capabilities and resource planning.⁷³ BMO has a broader scope, incorporating marketing aspects in addition to accounting. A reference ontology has been constructed from REA, e3-value, and BMO ontologies, reconciling their similarities and differences.⁷⁴ Financial Industry Business Ontology (FIBO) defines concepts for the financial industry⁷⁵ and uses the web ontology language (OWL) to abstract terminologies related to financial instruments and processes. These are examples of business ontologies currently in use.

When designing a framework, it is crucial to prevent the proliferation of ontologies within the same domain. It is in the best interests of the field to have an inclusive ontology with a shared vocabulary, architecture, and semantics. The use of a foundational ontology to define upper-level concepts is a common practice.⁷⁶ A mid-level ontology is needed to anchor the domain ontologies to the foundational ontology. The unified foundational ontology (UFO) is an ideal foundational ontology tailored for the business domain.⁷⁷

We suggest that the business community work towards a mid-level ontology that connects the UFO to domain ontologies such as REA and FIBO, as shown in Figure 3. While dictionaries and encyclopaedias on various business and management domains^{78, 79} could serve as the basis for a foundational ontology, the concepts are not guided by any enveloping framework of business. Thus, we felt the need to introduce a mid-level ontology that provides a theoretical framework for the domain. We use the value theory of business⁸⁰ to guide the design of such a mid-level ontology. Value theory envisions business as a transaction of value in society, resulting in wealth creation.⁸¹ The assumption here is that value creation leads to wealth creation.

Figure 3.

Proposed Mid-level Ontology to Connect Foundational Ontology to Domain Ontologies.

According to the theory, each functional field of business focuses on some aspect of value. Production and operations management focus on value creation. Accounting focuses on value assessment and finance on value allocation, while marketing focuses on value exchange. Similarly, subdomains such as organizational behaviour, human resources management, and management information systems also play essential roles in value creation and exchange. Value theory forms the intellectual platform for knowledge creation in business management.⁸⁰ It could serve as the conceptual guide for a mid-level ontology that links to the other domain ontologies in business. The ontology and the EBMgt template provide the base for collating evidence. The process involves combining and evaluating E evidence gathered from research studies and e evidence gathered from managerial practice. This combined and evaluated evidence is made available through an SR to end-users in a machine-friendly and seamless manner.

Blockchain and System Confidence

Here we discuss, at a general level, some of the important design features and pragmatic choices for blockchain in an EMS and how this technology can help ensure trust as part of the EMS framework. Blockchain was initially developed for managing cryptocurrencies such as Bitcoin.⁸² It is a promising tool for ensuring trust in community-owned evidence.⁸³ The foundation of the technology combines a distributed digital ledger, a decentralized consensus mechanism, and cryptographic security measures to ensure a tamper-proof system to record any information.⁸³ As a result, blockchain is essentially immutable, ensuring the authenticity of information stored within the system.

It is essential to decide how much data to keep in the ledger. Blockchain applications have high computational and storage overheads, especially when all the data are written into the system. In an EMS, ideally, the metadata should be included in the blockchain while the evidence itself remains in the EMS (off-chain).⁸⁴ A validation mechanism can be incorporated into the EMS platform that finds and verifies the information and submits it to the blockchain.⁸⁵ Universities can function as external validation agents, with academic researchers as curators of the information stored within the system. Public blockchain networks allow users to create personal accounts and interact with the platform.⁸⁶ This model will be appropriate for a resource such as an EMS and will likely promote active collaboration among those who generate information and those who use it.

The Role of Trust in Managing Evidence

The end-user must trust the evidence for it to be used, and determining its credibility involves assessments and guarantees. Evidence must be assessed at two levels: the measurement of the outcome (e.g., reduction in productivity) and the measurement of the uncertainty associated with the measurement itself (e.g., error in measuring productivity). Evidence improves as outcome measurement becomes more accurate, and the uncertainty associated with the measurement reduces.⁸⁷ Evidence may completely change when the outcome expectations change (e.g., productivity reduces with increased absenteeism, but this may change in the future). Its utility is contingent on the measurement methods at both levels (outcome and error). Therefore, a mechanism is needed to verify the outcome and error measurement methods and ascertain their credibility.

It has been established that trust plays a vital role in managing evidence. Conventionally, peer review and the professional consensus of reviewers are required to scale data to knowledge and evidence. Journals and other publishing platforms ensure trust by verifying the credentials of reviewers. The reviewers assess the validity of the methods and the accuracy of the researchers’ conclusions before incorporating them into evidence. However, evidence supplied by the practitioner community is not subjected to any systematic quality assessment and is likely to vary in value. Therefore, EMS requires a mechanism to ensure trust in the evidence it offers on its platform. One downfall of an online platform is that it inherently lacks trust, as contributors can potentially claim any identity. Though massive online KM platforms like Wikipedia have built-in mechanisms for ensuring credibility, they are not infallible. This is a key reason we recommend using blockchain as a foundational technology to reconcile this vulnerability.

Conventionally, trust is propitiated by the competence of the curators. Knowledge users must verify the identity and credibility of those who have contributed to the benchmarked evidence, and determine how and why they made these contributions. The end-users’ (whether human or machine) knowledge reuse goals depend on these metadata. Blockchain can store this metadata about evidence in a shared, decentralized, immutable, and tamper-proof database.

Empirical Propositions

Empirical propositions verify whether a design artefact is consistent with the meta-design.²⁶ As discussed, the four constructs driving evidence use are effort expectancy, performance expectancy, social influence, and conditions that facilitate control.³⁷ We have mapped the various functional modules in the EMS to these constructs, as depicted in Figure 2, to show how EMS facilitates evidence use and to formulate them as empirical propositions.

Structured knowledge capture is essential for knowledge communities. The EBMgt SR template¹⁵ helps provide structure to the knowledge capture process. Organizing knowledge this way assists in the “decontextualization” of the information from the context in which it was collected and then a subsequent “recontextualization” when applied to a new situation.⁸⁸ We believe that the EBMgt SR template to structure the codified evidence in a standardized manner augments effort expectancy—the degree of ease associated with using the system.³⁷

P1 : Template-based knowledge capture enhances effort expectancy

Performance expectancy is the belief that the system can positively influence the outcome. The uniform terminology in the form of a formal ontology will reduce the ambiguity associated with communications. Communication training is known to positively influence performance expectancy.⁸⁹ Consequently, we believe the ontology will improve performance expectancy by facilitating efficient communication between researchers and managers.

P2 : Adopting an ontology for knowledge capture enhances performance expectancy

The wiki platform enables the community to contribute evidence and communicate needs and preferences. The wiki platform can be augmented with additional modules for functions such as adding voting and social media sharing. Upvoting and downvoting are widespread techniques adopted by social media platforms to capture user feedback on specific pieces of information with minimal effort. The ability to upvote and downvote on the EMS wiki will enable knowledge users (managers) to prioritize the topics according to their practical relevance. Hence, we associate the voting module in the wiki platform with performance expectancy.

P3 : Voting module in a wiki platform enhances performance expectancy

The social media sharing module in the wiki platform enables users to share any content on their social networks. The comment module in the wiki setup allows the knowledge users to discuss the evidence with their peers. These modules will facilitate social influence. The wiki, with appropriate modules, could display links to other semantically related articles in the wiki and elsewhere on the Internet. This is an enabling condition that encourages use.

P4 : Social media sharing module enhances social influence

As previously emphasized, trust is central to evidence use, necessitating a mechanism that ensures the authenticity of the evidence made available to the community.⁵⁵ With the growing popularity of blockchain, users may become increasingly aware of the embedded privacy and security inherent to this technology, thereby positively influencing their trust in the platform.⁹⁰ The blockchain framework for ensuring trust in the EMS platform will facilitate evidence use.

P5 : The blockchain technology enhances trust and behavioural control

Discussion

Business is undergoing a paradigm shift. Corporate practices are gradually moving towards adaptable models used by humans and machines alike. As business researchers, we are responsible for adapting to these changing times and guiding the inevitable shift towards a machine-dominated world of ML and AI. At the same time, there is growing discontent in the widening gap between business research and everyday practice. Similarly, there is a burgeoning interest in learning how to close this gap and make our research relevant and actionable.⁹¹ The research community constantly generates peer-reviewed and thoroughly tested information that becomes codified in the published literature. The growth of corporate intranets has made it possible to collect and share tacit practical knowledge within the workplace. In fact, the value of such organizational knowledge is often greater than the organization’s tangible assets. Our interest is to bring the two bodies of knowledge—scientific and professional—together and elevate them from organizational-level KM to community-level evidence management. EMS provides the glue that brings both together—the community of researchers and practitioners unconstrained by organizational boundaries. In essence, EMS is a theory-driven approach to design. The design we recommend has safeguards that ensure evidence reliability, trustworthiness, currency, quality, and authenticity, along with ease of access, security, and privacy.

Ontologies or terminology systems are vital for collaboration between researchers and practitioners. The effectiveness of ontology engineering in collaborative research has been well established.⁹² Healthcare has benefitted enormously from such well-established ontologies. The blockchain is emerging as a technology disrupter, offering a simple and effective mechanism for brokering trust in online transactions. Trust is vital in machine-readable online EMS, as unaccounted meddling can lead to serious consequences. We offer insights on how blockchain technology can be used in EMS. It is important to note that blockchain is an evolving technology and may undergo major changes at the conceptual and implementation levels in the future.

Collaboration is the key to converting arcane research knowledge into actionable evidence. The EMS enhances collaboration by bringing new possibilities for knowledge synthesis. We propose customizing a wiki platform for SRs based on the EBMgt template.¹⁵ Standardization of terminology and benchmarking evidence are the main challenges to effective collaboration. We recommend building a mid-level ontology based on value theory to link a suitable foundational ontology to existing business ontologies. We believe that blockchain technology will ensure trust in the evidence extended by the process of collaborative benchmarking.⁹³ These components can be integrated into an overarching information system for evidence management.

We make four important contributions to the practice of management. The first is a system of evidence collection, collation, and use that adds value to the professional practice of management. The second is the design of a platform that allows for easy access to the best available evidence in real time for practicing managers and professional researchers. The third is a mechanism that engenders ongoing collaboration between the research and professional communities using ML and AI. The fourth is a technology that ensures trust in the evidence produced and made available to the participating community. We also contribute to the literature on EBMgt by addressing the challenge of evidence delivery. Additionally, we contribute to the literature on KM by moving its dominant locus from the level of the firm to the level of the profession and by making KM models receptive to ML and AI through the use of advanced technology.

Modern societies continue their inevitable transformation into knowledge societies as economic growth increasingly relies on intellectual capital. However, not every business sector will be knowledge-intensive enough to capitalize on this transformation. The EMS platform could prove to be instrumental in aiding this evolution towards a sophisticated knowledge society within the business domain.⁷⁰ We hope our recommendations will guide the development of a progressive EMS that can facilitate management decision-making.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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