Literature Review

Organizational knowledge retention management using a constructivist multi-criteria model

Leonardo Ensslin, Clarissa Carneiro Mussi, Sandra Rolim Ensslin, Ademar Dutra and Lydia Pereira Bez Fontana

Abstract

Purpose – The purpose of this paper is to support the management of organizational knowledge retention through a multi-criteria decision aiding–constructivist model.

Design/methodology/approach – This exploratory and descriptive case study presents a decision support model guided by the constructivist approach and proactive in its operationalization.

Findings – The objectives and concerns of decision-makers regarding the retention of organizational knowledge are identified and organized into six strategic areas of concern, namely, recognition,

knowledge dissemination, organizational culture, succession of professionals, management of

vulnerability origins and knowledge management; a multi-criteria model is constructed and

operationalized by a cluster of cardinal scales, showing and measuring the status quo of the

performance profile, both in a local and global way, to support the management of the organization’s knowledge retention; activities are classified into three performance levels (compromising, competitive

and excellent), supported by graphical and numerical evidence; and the process to generate actions to

improve the performance of critical activities and create the conditions to maximize the results of the

organization is illustrated.

Practical implications – Based on the model, decision-makers are now aware of the essential aspects to support knowledge retention management, enabling them to monitor the current situation and

proactively respond to ensure that the current knowledge potential is maintained and exploited.

Originality/value – Use of a constructivist approach to support the management of knowledge retention, incorporating into the model the specifics of the context and the values of its managers, and thus giving it

legitimacy.

Keywords Performance evaluation, Knowledge management, Knowledge retention, Multi-criteria decision aiding–constructivist (MCDA-C)

Paper type Research paper

1. Introduction

Historically, organizations have sustained their success using strategies related to price

and quality. With the advent of global interconnection in real-time, dimensions such as

agility, flexibility, innovation and sustainability became increasingly important. In this

flourishing environment, knowledge has emerged as the main competence for success

(Spender and Grant, 1996; Nonaka and Von Krogh, 2009; Lin et al., 2016) and we now live

in a knowledge-based economy, where information, knowledge and learning are key

resources (Nonaka et al., 2006; Joshi et al., 2016).

In this context, organizational knowledge loss refers to a cluster of negative consequences

affecting performance and competitiveness (Schmitt et al., 2012; Lin et al., 2016;

Massingham, 2018; Rashida et al., 2019). Organizations run the risk of losing knowledge in

Leonardo Ensslin and Clarissa Carneiro Mussi both are based at the Department of Administration, University of Southern Santa Catarina (Unisul), Florianopolis, Brazil. Sandra Rolim Ensslin is based at the Department of Accounting, Federal University of Santa Catarina (Ufsc), Florianopolis, Brazil. Ademar Dutra is based at the Department of Administration, University of Southern Santa Catarina (Unisul), Florian�opolis, Brazil. Lydia Pereira Bez Fontana is based at PPGA, University of Southern Santa Catarina (Unisul), Florianopolis, Brazil.

Received 3 December 2019 Revised 12 March 2020 Accepted 15 April 2020

Authors would like to thank Editage (www.editage.com) for English language editing, the reviewers for contributions that allowed them to improve the manuscript and the Brazilian governmental agencies. Capes (Coordenação de Aperfeiçoamento de Pessoal de Nı́vel Superior) and CNPq (Conselho Nacional de Desenvolvimento Cientı́fico e Tecnol�ogico).

DOI 10.1108/JKM-12-2019-0689 VOL. 24 NO. 5 2020, pp. 985-1004, © Emerald Publishing Limited, ISSN 1367-3270j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 985

the face of various circumstances such as voluntary dismissals, layoffs, retirements,

downsizing, staff turnover, mergers and acquisitions (Martins and Meyer, 2012; Schmitt

et al., 2012). Thus, it is essential to ensure that the knowledge critical to the continuity of the

organization remains under the organization’s administration and control.

Knowledge retention formalizes a commitment to the organization’s long-term strategic

objectives and is especially important when people with long experience leave, taking with

them strategic knowledge and know-how. Although there is apparent unanimity about its

need and importance, organizations have difficulty understanding how well their processes

and procedures promote knowledge retention. Executives often cite the lack of knowledge

transfer within organizations as a key problem (Goh, 2002; Levy, 2017), suggesting that

knowledge transfer is an ongoing problem in organizations and needs to be better

understood (Osterloh and Frey, 2000; Reyes and Zapata, 2014).

As a sub-discipline of knowledge management, knowledge retention is not fully covered in

academic research or in published business case studies (Levy, 2011, 2017). According to

Levy (2011), knowledge retention, also known as knowledge continuity, deals with certain

challenges, about which classical knowledge management methodologies have been little

concerned. The dearth of appropriate models for managing knowledge retention is one of

the challenges of knowledge asset management in organizations, especially in the public

sector (Dewah and Mutula, 2016). Although existing organizational knowledge retention

models (Martins and Meyer, 2012; Dewah and Mutula, 2016; Syed-Ikhsan and Rowland,

2004; Fiedler and Welpe, 2010) contribute to research in the area, they are generic and do

not consider the specifics of context and actors for which they are developed from a

constructivist performance evaluation perspective.

This case study contributes to the knowledge management literature by addressing

organizational knowledge retention from the perspective of constructivist performance

evaluation as decision support. This approach is appropriate for complex situations, with

conflicts of interest, where the objectives are not clear and the uniqueness of the

organization and the profile of the actors are peculiar and essential to the competitiveness

of the organization. In the present case study, a model for the management of

organizational knowledge retention, based on the multi-criteria decision

aiding–constructivist (MCDA-C) methodology (Ensslin et al., 2000, 2017, 2018), is built to

support the decision-makers. This study answers the following research question:

RQ1. What criteria should be taken into account when constructing a performance

evaluation model to support the management of organizational knowledge retention?

The study examines knowledge-loss concerns among the decision-makers at a Brazilian

sanitation service concessionaire. In Brazil, sanitation concessionaires are increasingly

being held accountable for the effective provision of water supply and sewage treatment. In

the quest to provide the best service and considering the wide range of activities performed

by the company, knowledge management stands out as a strategic theme, particularly

when managing the pressures of voluntary dismissal programs, which are carried out with a

view to reducing costs and renewing the staff.

From a practical perspective, the results of this research will enable the decision-makers of

the organization to expand their knowledge of the subject, understand the consequences of

decisions on the essential results of the firm and the performance level of the measured

aspects and have a process to generate actions that promote organizational knowledge

retention management.

2. Literature review

Knowledge management has become increasingly important as organizations realize that

the effective use of their knowledge assets provides them with the ability to innovate and

PAGE 986j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

respond to customer expectations in a rapidly changing environment (Daghfous et al.,

2013; Joshi et al., 2016). Knowledge retention is one of the concerns of knowledge

management (Heisig, 2009; Liyanage et al., 2009; Martins and Meyer, 2012; Lin et al., 2016)

and involves the maintenance of critical skills, abilities, experience and knowledge, even

when the employees who possess these traits leave the organization (Mishra and Uday

Bhaskar, 2011; Schmitt et al., 2012). Therefore, there is a focus on maintaining information

and knowledge in the organization’s processes and activities, to avoid the discontinuity of

knowledge upon the departure of employees. Knowledge retention denotes the extraction

of tacit knowledge and its storage in the organizational memory so it can be explicit to

others and used later (Mishra and Uday Bhaskar, 2011).

Although relevant to organizations, knowledge retention and management, aimed at

minimizing the unintentional loss of critical knowledge that accumulates with learning and

with individual and collective actions, is not a simple task (Daghfous et al., 2013; Schmitt

et al., 2012; Lin et al., 2016). Even if organizations conduct knowledge management

practices, the possibility of knowledge loss still occurs (Lin et al., 2016).

Previous studies have found a set of negative impacts on organizations caused by

knowledge loss. Research by Daghfous et al. (2013) indicates that knowledge loss can lead

to significant and widespread implications for organizational performance such as

increased costs, lost productivity, reduced customer satisfaction, increased supply chain

risks and reduced core competencies. Lin et al. (2016) identify a relationship between

knowledge loss, decreased absorptive capacity and, consequently, decreased

performance. Similarly, Massingham (2008, 2018) finds that knowledge loss led to

numerous organizational problems (low productivity, misalignment of the workforce,

decreased work quantity and quality, resource cuts, unused work outputs, longer time to

competence with learning costs and slow task completion), an increased sense of risk

associated with work activities, a declining capacity to manage risk and a decreased

organizational knowledge base.

Given the unintended consequences of knowledge loss, research on knowledge retention

has made efforts to investigate the effects of different factors on the organizational capacity

to retain knowledge and to investigate strategies to minimize loss. The difficulty of retention

is associated with the type of knowledge involved. On the one hand, explicit knowledge,

objective and expressible by formal language, can be more easily identified, transmitted,

retrieved and used, although some organizations have problems managing its retention

(Nonaka and Takeuchi, 1995; Nonaka and Von Krogh, 2009). On the other hand, the tacit,

subjective and difficult knowledge of verbal expression presents a more complex

management challenge in terms of identification, retention, transmission and use (Nonaka

and Von Krogh, 2009; Nonaka and Takeuchi, 1995).

In addition to the type of knowledge, a cluster of elements inherent to the organization itself

can influence the organizational capacity to transfer and retain knowledge. Fiedler and

Welpe (2010) suggest that structural organizational factors such as specialization and

standardization, as well as organizational processes such as coding, personalization of

information and electronic communication, influence organizational memory. Martins and

Meyer (2012) identify behavioral and organizational factors that influence the retention of

knowledge, especially tacit knowledge; of these factors, the following are highlighted,

namely, knowledge behaviors, strategy implementation, leadership and knowledge loss

risks. Durst and Wilhelm (2012) demonstrate the influence of a precarious financial situation

on activities related to knowledge retention and succession planning in the context of a

medium-sized firm. Dewah and Mutula (2016) find that challenges associated with the

retention of organizational knowledge in the public sector relate mainly to limited

understanding of knowledge management benefits, shortage of skill, lack of incentives or

rewards to share knowledge, lack of appropriate technology, limited commitment from

senior management, lack of appropriate models from which to learn and brain drain.

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 987

Research by Sitlington and Marshall (2011), in the context of downsizing and restructuring

initiatives, shows that organizations need to consider the cultural and organizational climate

in relation to knowledge retention and the potential impact of these initiatives and make

efforts to maximize knowledge retention. Schmitt et al. (2012) identify the importance of high

levels of collaboration, strong network ties, maintenance of the leadership structure and

perceived justice to retain critical knowledge during downsizing.

With respect to knowledge retention strategies, Daghfous et al. (2013) suggest that

technical approaches alone such as the adoption of standard operating procedures,

information systems and codification of knowledge in databases can undermine knowledge

retention and lead to knowledge loss; to mitigate this, organizations should retain and

diffuse architectural knowledge, improve strategic coordination among units, develop

existing capabilities through different networking strategies and more effective networks

and transform these capabilities into effective organizational routines. Lin et al. (2016) show

the effectiveness of knowledge retention practices based on people management practices

and information systems to mitigate the effects of knowledge loss. In a more specific

example, Rashid (2019) investigated mechanisms to reduce the effects of knowledge loss

due to turnover in open source software development projects.

Previous studies commonly investigated the impacts of organizational knowledge loss, the

organizational factors that affect knowledge retention and the strategies to store and retain

knowledge while minimizing its loss. However, the literature largely fails to address the

development of organizational performance assessment models aimed at managing

knowledge retention.

Some organizational knowledge retention frameworks have been proposed, commonly

involving steps and activities related to retention, as well as proposals for its evaluation. Arif

et al. (2009) present a model for assessing an organization’s knowledge retention capacity

in four stages (socialization, codification, knowledge construction and knowledge retrieval)

and levels of maturity. At the first level, the extent of knowledge sharing in the organization is

demonstrated; the second level measures how much of the shared knowledge is

documented; the third level measures the effectiveness of the documented knowledge

storage; the last level evaluates the ease of stored knowledge accessibility and retrieval.

Jafari et al. (2011) propose a knowledge loss risk management model considering the

importance and criticality of different types of knowledge for the business and the level of

training required to retain it. The model involves six stages, namely, planning of knowledge risk

management, identification of risk factors, qualitative and quantitative assessment of the risk

factors, design and accomplishing the knowledge preservation plans, monitoring and

controlling risk. For the authors, the response to risk factors can be categorized into four main

reaction groups, namely, prevention, transfer, mitigation and acceptance of risk factors.

Levy (2011, 2017) presents three levels for management of the knowledge continuity of an

organization as follows: intensive and regular knowledge management (avoidance),

retention planning 3 to 12 months before the employee leaves (engagement) and no

preventive retention measures (reaction). Levy (2011) proposes a structure for knowledge

retention for the intermediate level (engagement), focused on structuring the retention

process and results, which includes three phases as follows:

1. project scope, with definitions of what will be retained and what will be ignored;

2. transfer, which encompasses planning and implementation, including storage; and

3. integration, which addresses the (re)use of knowledge and the incorporation of

knowledge in processes.

The proposed frameworks show that it is not enough to identify knowledge and store it;

fundamental parts of knowledge retention processes are sharing, accessing and using

PAGE 988j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

knowledge (Arif et al., 2009; Levy, 2011). However, these frameworks are generic, that is,

they do not consider the specifics of the context and of the decision-makers for which they

are developed from a constructivist performance evaluation perspective.

The use of performance evaluation in companies began with scientific administration, with

the objective of identifying, organizing and measuring the critical aspects of what is to be

monitored and improved (Franco-Santos et al., 2007). Performance evaluation has been

used from a realistic perspective to assist organizations generically, assuming that all have

the same opportunities and limitations and all actors have the same objectives and

understand them in the same way (Franco-Santos et al., 2007; Ghalayini and Noble, 1996).

The need to take into account specific environments, where a responsible person, wishing

to follow the strategic objectives of the organization, recognizes that they are only remotely

associated with their decision-making environment, and therefore needs to base their

choices on their values, motivations and preferences, in the expectation that these are

aligned with the objectives and values of the organization, brought out a new vision for

constructivist performance evaluation. In these models, structuring is the most important

step (Rittel and Webber, 1973; Roy, 1993, 1994); the specifics of the environment are taken

into account (Landry, 1995); the objectives are those of the decision-maker and the

decision-maker participates in the construction of the model (Roy, 1993, 1994); the scales

are constructed by observing the fundamentals of the measurement theory (Joint

Committee of Guides in Metrology, 2008; Micheli and Mari, 2014) and the integration of the

scales is carried out from the attractiveness interval between the reference levels of the

scales (Keeney, 1992). The model shows graphically and numerically the performance

profile of all factors considered essential by the decision-maker in the current situation and

presents a process for generating improvements (Ensslin et al., 2020).

From a constructivist perspective, performance evaluation has been used over the past

three decades (Bana e Costa et al., 1999; Ensslin et al., 2013, 2018). In this view:

Performance Evaluation is the process of approaching in harmony with its application to build

knowledge in the decision-maker, about the specific context to be evaluated, based on the

decision-maker’s own perception through activities that identify, organize, ordinarily and

cardinally measure, and its integration and the means to visualize the impact of actions and their

management (Ensslin et al., 2007, p. 5).

This concept will underpin the construction of the organizational knowledge retention

management model, associating the theory of constructivist performance evaluation with

the theme of knowledge retention management.

3. Methodology

Aiming at scientific acceptance and giving meaning and credibility to the results produced

by modeling in decision aiding contexts, researchers may follow three main ontological or

epistemological paths, namely, the realistic path, the axiomatic path and the constructivist

path (Roy, 1993). Each of these paths follows a very well-defined vision of the problem and

generates different solutions (Ensslin et al., 2020, 2001; Roy, 1993).

The philosophical conception adopted in this research is based on constructivism,

considering, which object and subject are engaged in the process of knowledge (Roy,

1993). In the constructivist paradigm, the objective of modeling is the generation of

knowledge to decision-makers. To this end, models should be developed that serves as a

means to inform decisions, in the way that decision-makers consider most appropriate,

according to their value systems (Ensslin et al., 2001).

In the constructivist path, some have asked what researchers can do to validate the work

they have produced, as the model constructed cannot be replicated when the problem

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 989

context changes as knowledge development evolve. In accordance with Roy (1993), to be

scientifically valid and legitimate, a constructivist decision-aiding model must have:

� a well-defined procedure protocol, which is scientifically recognized as appropriate;

� all protocol parts operationalized, evaluated and accepted individually and collectively

by a large scientific community interested in it and attesting to its appropriateness;

� a specific set of actors whom the model is meant to aid, who should recognize that the

model was helpful in acquiring the knowledge to understand the consequences of the

context for their individual goals, values and preferences, to manage in a conscious

and informed way (Ensslin et al., 2020).

The model was constructed following the constructivist path as proposed by Roy (1993),

using the protocol procedure phases of the MCDA-C as presented in Figure 1 to obtain the

data required (structuring, evaluation and recommendation).

The research follows a qualitative-quantitative approach (Cooper and Schindler, 2006). The

qualitative approach was used in the construction phases of the MCDA-C model,

“structuring” and “recommendations.” The quantitative approach was adopted in the

“evaluation” phase, in view of the use of cardinal scales to measure the indicators of the

built model.

A case study is adopted as the research strategy (Yin, 2003); specifically, a sanitation

service concessionaire located in the southern region of Brazil. The concessionaire is

responsible for providing services to approximately 2.7 million people in 195 municipalities.

It has a staff of around 2,500 employees and annual sales of more than BRL 1bn.

This company was chosen as the subject of the case study for several reasons. The

sanitation sector is prevalent in the country, the sector concessionaires are required by the

government and society to improve service and productivity, privatization pressure is

evident and voluntary dismissal programs are periodically implemented, resulting in the

departure of employees with considerable tacit knowledge.

Figure 1 Phases of the MCDA-C

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The model built is based on the perceptions of human resources managers and corporate

university representatives (decision-makers); a total of 11 people were directly involved.

These decision-makers were chosen because of their roles addressing knowledge

management and its impacts on people management, in addition to having the necessary

skills for the process and being in a position to monitor and promote improvement actions.

Data were collected through recorded interviews with decision-makers, who were

encouraged to speak freely about the context and problem, to gain their perceptions and

develop their understanding. The subjects presented were synthesized and organized by

the facilitators and forwarded to the decision-makers. Each subsequent meeting began with

an examination of the texts from the last meeting.

The constructivist model consists of building a cluster of tools in an interactive way with the

decision-making stakeholders that allows progress in the structuring process in a manner

consistent with the objectives and values of the decision-makers (Roy, 1994).

Data collection and analysis were operationalized based on MCDA-C procedures. Unlike

other methodologies, the MCDA-C approach assumes that those involved in the decision

must participate in the construction of the alternative evaluation model, defining the problem

to be solved and the criteria to be used (Ensslin et al., 2001; Bana e Costa et al., 1999). The

MCDA-C is implemented in three successive and complementary phases, namely,

structuring; evaluation and recommendations (Ensslin et al., 2010, 2000), as shown in

Figure 1.

The first stage of the structuring phase is “soft approach structuring,” which consists of

identifying the actors (Table 1) to interview to specify the context and its boundaries. The

next stage is called “family of points of view,” which is also done by open interviews with the

actors speaking freely about the context for which they wish to identify the aspects essential

to their management such as concerns, desirable characteristics, potential actions,

objectives, restrictions and recurring problems. Following this approach to data collection,

facilitators guide the content of the interviews according to the MCDA-C protocols, to

identify the primary elements of evaluation (PEEs), followed by transformation into action-

oriented concepts and grouping into areas of concern. The developed areas of concern are

tested for compliance with the properties of consensus, intelligibility, concision,

completeness, monotonicity and non-redundancy. If these criteria are met, the set of areas

of concern is called a family of fundamental points of view (FFPV) (Bana e Costa et al.,

1999; Ensslin et al., 2001, 2010). The third stage of the structuring phase, “construction of

descriptors” is done during the interview process to operationalize the fundamental points

of view (FPVs) and comprises four steps:

1. construction of cognitive maps;

2. identification of clusters and subclusters (FPV and elementary points of view or EPV);

Table 1 Identification of actors in the MCDA-C methodology process

Actors Function

Decision-makers Human resources managers

Corporate university manager and representatives

Stakeholders Other collaborators

Pairs

Acting Family members

Society

Facilitators Head of the division of positions and salaries

MCDA-C specialist

Source: Research data

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 991

3. construction of the hierarchical structure of values (HSV); and

4. construction of measurement scales or descriptors.

At this stage, the HSV is operationalized by ordinal scales, representing the characteristics

of the actions and values of the decision-maker and reflecting the qualitative knowledge of

the context that the decision-maker wishes to model (Ensslin et al., 2001). In the structuring

phase, 10 interviews were conducted at approximately 60 to 90 min each. In this stage, the

qualitative aspects that culminate in the recognition of strategic objectives FPV, tactical and

operational objectives with their scales, their reference levels and recognition of the current

situation and desired goal or target, are discovered.

In the next phase, Evaluation, the multi-criteria cardinal model is constructed to enable the

evaluation of potential actions. In the MCDA-C, the evaluation is done in the following

stages: independency analysis; construction of value functions and identification of

compensation rates; identification of the impact profile of alternatives and sensitivity

analysis. The purpose is to transform the ordinal (qualitative) scales developed in the

structuring phase into cardinal (quantitative) scales and determine the compensation rates

to integrate the criteria (Ensslin et al., 2010; Longaray and Ensslin, 2015). In the evaluation

phase, four meetings were held. The decision-makers’ preferred value judgments regarding

the difference in attractiveness between the levels of the scales were used to construct the

value functions (cardinal scales) and regarding the difference in attractiveness between the

reference levels of each scale to determine the compensation rates.

Once the structuring and evaluation phases have been completed, the decision-maker will

have an instrument in the model to use as guidance to discovering the aspects considered

essential to the management of the context, allowing the visualization of what is needed in

each aspect to achieve the goal. The third phase of MCDA-C then begins by developing

strategies (clusters of actions) that allow the goal to be achieved or even surpassed. The

third phase of recommendations supports the decision-maker by identifying actions that

contribute to improving the performance of the evaluated object and explaining the impact

of the consequences of each action on the strategic objectives of the decision-maker

(Ensslin et al., 2010). In the recommendation phase, six meetings were held. The decision-

makers knew the criteria with vulnerabilities and the interviews focused on the organization

and detailing the projects to take the performance toward the goal.

The entire process was carried out following the MCDA-C protocol as shown in Figure 1.

The operationalization of the three phases of the MCDA-C is described in the following

section.

4. Model construction and results

This section presents the phases of the model construction and illustrates its application in

the target organization as part of the process of knowledge retention management. The built

model was based on the MCDA-C methodology, considering its ability to deal in a specific

way with problems characterized as “confusing situations” that demand structuring,

evaluation and recommendation to support management in monitoring and improving

decision-making environments (Bana e Costa et al., 1999).

4.1 Structuring phase

In addition to explaining the context, the structuring phase aims to enable the decision-

maker to establish what belongs to the context, their directions of preference and to begin

describing the discrepancies that concern them in the environment (Ensslin et al., 2010;

Longaray and Ensslin, 2015).

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4.1.1 Contextualization, actors and labeling. The construction of the model begins with the

specification of the characteristics of the decision-making environment. Social problems,

unlike physical problems, require an understanding of the environment and actors, as well

as a delimitation of what belongs and what does not belong to the environment to be

modeled, to ensure its uniqueness and legitimacy (Rittel and Webber, 1973; Roy, 1993;

Landry, 1995; Ensslin et al., 2020). Because it is constructivist, the model represents how

the decision-maker perceives the decision-making environment. The process begins by

identifying the actors and the decision-maker (Table 1). After that, a 60-min interview is

conducted in which the decision-maker is asked to talk about the environment to justify its

importance and delimit its boundaries.

The decision-maker revealed that the organization, through a voluntary dismissal program

over the past two years, had laid off approximately 630 employees (25% of the total), all of

whom had more than 15 years of experience in the company and most of whom held critical

knowledge built and required for the successful operation of the organization. The decision-

maker was then asked to propose a label for the problem, which after interactions and

reflections resulted in “building a model to support organizational knowledge retention

management.”

This development closes the contextualization stage and creates the environment for the

next stage, which consists of identifying the areas of concern judged by the decision-maker

to be essential to the problem assessment.

4.1.2 Primary elements of evaluation concepts and areas of concern. At this stage, the

decision-maker discussed aspects of the defined problem such as concerns, desirable

characteristics, potential actions, objectives, restrictions and recurring themes. This

information was used to identify context properties perceived as essential to the value

system and the decision-makers’ concerns, constituting what the MCDA-C calls PEEs.

Open-ended questions were asked of the decision-maker to allow freedom of response and

to avoid inducing the reporting of issues absent from the decision-makers’ value system.

This approach also encouraged stakeholders to participate and speak at meetings,

expanding the decision-makers’ understanding.

As a result of these interviews, 66 PEEs were identified. New interviews following the same

protocol were then conducted to find the concept contained in each PEE. In MCDA-C, a

concept is an underlying objective of the PEE that represents the direction of preference of

the values associated with the characteristics or properties of the context that the decision-

maker deems important. When documented, PEEs begin with an action verb and the

psychological opposite pole representing what the decision-maker wants to avoid is also

defined (Eden and Ackermann, 1998; Lacerda et al., 2014; Ensslin et al., 2020). By way of

example, Table 2 shows the first five identified PEEs and their respective concepts.

The concepts are then grouped into areas of concern according to the contents presented

(Bana e Costa et al., 1999; Ensslin et al., 2010, 2018). Areas of concern can be understood

as the strategic objectives of the context or candidates for FPV, which represent a cluster of

context characteristics that the decision-maker associates with one or more values and

considers essential to the management of the knowledge retention process context (Ensslin

et al., 2001).

Six areas of concern have been identified; Figure 2 illustrates these areas of concern, each

with its denomination and the concepts that gave rise to it.

A set of areas of concern that meets the properties of consensus, intelligibility, concision,

completeness, monotonicity and non-redundancy can be considered as an FFPV (Bana e

Costa et al., 1999; Ensslin et al., 2001). The facilitator, together with the decision-maker,

tested and concluded that these properties were met and that these areas of concern, were

considered an FFPV. The next step is to operationalize the FPVs; that is, identify their

representative properties that can be measured.

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 993

4.1.3 Means-end relationship maps, hierarchical structure of values and descriptors. To

operationalize this FFPV, a cognitive map is constructed for each FPV showing how

the concepts are organized in a hierarchical structure of means-end relationships (Bana

e Costa et al., 1999; Ensslin et al., 2010, 2013). This process is carried out following

the same open interview protocol. Each concept, how it can be obtained and its

importance is discussed, generating the structure of the cognitive map (Montibeller Neto

et al., 2008).

The cause and effect analysis was performed for all concepts and, as the understanding of

the problem expanded, the concepts could be changed and new concepts introduced.

Once the construction of the cognitive map was completed, the means-end concepts

involving related content were grouped in clusters and subclusters, which are then labeled,

to denote the essence of the decision-makers’ interest.

Figure 2 Grouping of concepts to form the FFPV candidates

Support model for knowledge reten�on management in a sanita�on company

Recognition Dissemina�on of knowledge

Knowledge Management

Organizational culture

Manage source of vulnerability

Professional succession

C O N C E R N

Align organiza�onal

knowledge reten�on ac�ons

with business strategy, with the

involvement of staff to achieve

goals and indicators.

S�mulate and promote the

dissemina�on of strategic

knowledge at all levels of the

organiza�on, with accessible tools,

incen�ves to work together, and

exchange opportuni�es.

Act in a con�nuous and

systema�c manner to

ensure that the knowledge is acquired and

generated in a manner that

generates value and ensures it remains in the organiza�on.

Incorporate into the

organiza�onal culture the

ac�ons vital to knowledge reten�on,

par�cularly by promo�ng adequate

communica�on, valuing people

and collabora�on.

Act in a manner that iden�fies and minimizes vulnerability factors for knowledge

reten�on in the organiza�on.

Take ac�ons that value the succession of professionals with cri�cal

knowledge in order to control

the loss of knowledge

and the discon�nuity of

ac�vi�es.

02; 03; 04; 05; 13; 24; 37; 55; 56

06; 07; 08; 09; 10; 11; 15; 16; 17; 22; 23; 32; 34; 35; 42; 43; 44

01; 18; 20; 21; 25; 31; 38; 39; 57; 58; 59; 60; 61; 62; 64; 66

26; 27; 28; 29; 30; 36; 40; 41; 52; 63

47; 48; 49; 50; 51; 53; 54; 65

12; 14; 19; 33; 45; 46

C O N C E P T S

Source: Research data

Table 2 PEEs and concepts of the present pole and psychological pole

# PEE

Concept

Present pole Psychological pole opposite

1 Strategic knowledge Identify and retain strategic knowledge for the

company

Losing focus

2 Competitive differential Identify the company’s competitive differentials (in tasks, processes and

technologies)

Losing the opportunity to promote the

competitiveness of the organization

3 Indicators Identify the performance indicators in the

knowledge area

Managing without focus

4 Problem-solving methodologies Know procedures to follow when there is a risk

of loss of knowledge

Losing employees with strategic

knowledge

5 Technologies Identify the technological knowledge of

complex projects carried out by the company

Being vulnerable to people with this

knowledge

Source: Research data

PAGE 994j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

According to Eden (1988), a cluster is a set of concepts that represents a well-defined area

of interest judged by the decision-maker as essential to the context management. This

understanding allows migrating clusters such as FPVs and subclusters such as EPVs to an

HSV (Bana e Costa et al., 1999; Ensslin et al., 2001; Longaray and Ensslin, 2015). Figure 4

represents the HSV of FPV 1- recognition. As the EPVs of the extremities represent

properties that can be measured, the next step is to construct the scale that best represents

the values of the decision-maker associated with it.

4.1.4 Hierarchical structure of values and descriptors. The HSV consists of FPVs and EPVs.

According to Bana e Costa et al. (1999), a point of view will be fundamental when it is

justified as a factor informed by the decision-maker as relevant to the management of the

context, standing out as an axis of evaluation. An FPV is usually made up of several EPVs

that are the aspects that help to explain the FPV and that represent properties of the context

that are amenable to measurement in an objective, homogeneous, operational and

unambiguous manner (Ensslin et al., 2013). The HSV built from FPV 1 - recognition is

represented in the upper part of Figure 4.

From the non-operationalized HSV, the next step is the construction of ordinal scales, called

descriptors, which can be descriptive, graphic, pictorial or even represented by

alphanumeric symbols (Bana e Costa et al., 1999; Ensslin et al., 2010). Descriptors are the

ordinal (qualitative) scales formed by a cluster of impact levels associated with an objective,

which will describe and prioritize all possible consequences of the properties of the

alternatives to be measured (Bana e Costa et al., 1999). The construction of descriptors is

performed by the facilitator together with the decision-maker, based on the concepts that

form the subclusters of the cognitive map. The decision-maker is asked to discuss each

subcluster and its concepts in terms of which performances are feasible. From this

understanding, the facilitator proposes a scale that represents the decision-makers’

understanding of what he or she considers important to be measured and sets the

reference level thresholds of good and neutral. The upper reference level (good), indicates

performance judged to be at the level of excellence, while the lower level, (neutral),

indicates the threshold below which performance is deemed to be compromising. Between

these two thresholds, the performance is considered competitive or at the level of normality.

In the next step, working from the operationalized HSV, the decision-maker determines, on

each scale, the level of the current situation, that is, the status quo (SQ). After the definition

of the SQ, the goals are identified, that is, the performance, that is intended to be achieved

for each property associated with the descriptor, as illustrated in Figure 4.

Once the MCDA-C methodology is proposed to build a model that measures cardinally the

possible performances of the context and that performs the integration of the scales, it

becomes necessary to add the decision-makers’ preferential information to transform the

ordinal model into a cardinal one.

4.2 Evaluation phase

The ordinal scales are transformed into cardinal scales via the following steps:

� the ordinal and cardinal preferential independence test of the scales for the interval

between the reference levels is conducted;

� the ordinal scales are transformed into cardinal scales;

� the compensation rates are constructed;

� the impact profile of alternatives is identified; and

� sensitivity analysis is performed (Ensslin et al., 2013, 2000).

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 995

4.2.1 Preferred independence test. The preferential cardinal independence is obtained by

ensuring that the intensity of the attractiveness of moving from the lower (neutral) to the

upper (good) reference level is constant, irrespective of the performance that an alternative

may have on the other criteria (Ensslin et al., 2001). For the built model, all criteria were

peer-to-peer tested and showed to be mutually preferentially cardinally independent.

4.2.2 Construction of value functions. The aim of this stage is to transform the ordinal scales

into cardinal scales (interval scales), used in MCDA-C to represent the decision-makers’

preferential perception of the intensity of the attractiveness difference between the levels of

each scale, to be used in a Single Synthesis Criterion Aggregation model.

For the construction of the value functions, the semantic judgment method was used,

applying the measuring attractiveness by a categorical-based evaluation technique

(MACBETH) method, developed by Bana e Costa and Vansnick (1995) and incorporating

the semantic judgments of the decision-makers. The MACBETH software, from the

judgment matrix, proposes a cardinal scale that meets the decision-maker’s judgments.

4.2.3 Compensation rates. Once the knowledge required to carry out local cardinal

assessments has been reached, the criteria must be integrated, which is achieved by the

construction of the compensation rates, carried out in three stages:

1. identification of alternatives, as shown in the upper part of Figure 3,

2. the ordering of the alternatives, as illustrated in the lower-left corner of Figure 3; and

3. construction of the rates, using the MACBETH software, by filling in the semantic matrix

of the difference in attractiveness between the alternatives, to identify the replacement

rates that best represent numerically the semantic judgments, with the determination of

the rates of two of the model’s EPVs.

4.2.4 Global assessment and current situation impact profile. The HSV, in addition to

facilitating the identification of the performance corresponding to the target, as can be seen

Figure 3 Steps for preparing compensation rates for EPV 1.1.1.1.1.1.1 – scope and EPV 1.1.1.1.2 – intensity

PAGE 996j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

in Figure 4, enables the understanding of the performance of the context by highlighting the

competitive differentials and vulnerabilities, which are duly analyzed in the

recommendations phase.

4.3 Recommendations phase

The purpose of the MCDA-C recommendations phase is to provide, in graphic and

numerical form, the cardinal scales of the criteria judged by the decision-maker to be

essential to the management of the context, the current performance and the goal. This

information permits the understanding of potentialities and vulnerabilities to generate

actions for improvement (Longaray and Ensslin, 2015).

Using the knowledge built by the model, it is now possible to highlight the contributions of

performance improvement in the SQ of each criterion and the contribution by improving it to

achieve the goal. To ensure that the actions generated are those that promote the greatest

improvement to the context, an analysis of the contributions of each criterion can be carried

out when passing from the current level to the goal, with its weighting, as shown in Table 3

for FPV 1 – recognition.

The recommendations stage supports the decision-maker in identifying actions that

contribute to improving the performance of the evaluated object, on how to expose the

impact of the consequences of each action on the strategic objectives (Ensslin et al., 2010).

Figure 4 HSV operated with rates, SQ and goals of FPV 1 – recognition

Model to Support Knowledge Retention Management

= SQ = Status Quo = Current Situation

= Current Situation Impact (SQ) Profile or Current Situation Diagnosis

Excellence

Normality

Compromising

EPV 1.1.1 - Getting to Critical Knowledge

EPV 1.1 - Relevance

FPV 1 - Recogni�on

EPV 1.2 - Proactivity on Critical knowledge

EPV 1.2.2 - Management Participation

EPV 1.1.1.1 - Competitive Advantages

EPV 1.1.1.1.1 - Incentives

EPV 1.1.1.1.2 - Benchmarking

EPV 1.2.1 - Project Representatives

EPV 1.2.3 - Proactive Action

EPV 1.2.3.1 - Procedures

EPV 1.2.3.1.1 - Consequence Analysis

% of projects with previously

evidenced demand for knowledge

EPV 1.1.1.1.1.1 - Scope

EPV 1.1.1.1.1.2 - Intensity

% of Company units with a system of

suggestions for improvement

No. of winners with 3 or more bonus days in the past year

No. of companies that have come to the Company

in the last year to learn knowledge

at the level of excellence

EPV 1.1.1.2 – Critical Knowledge Repository

% of units with map of

knowledge

% of teams that have at least two employees with mastery over the project's subject

matter

EPV 1.2.1.1 - Knowledge Projects

EPV 1.2.1.2 - Participants

EPV 1.2.2.1 - Endorsement of actions

No. of communications

issued by the Board of Directors last

month to encourage critical knowledge

retention measures

% of critical knowledge mapped

for which the consequences of non-

observance are formally documented

100% (all)

75%

50%

25%

10%

0%

20 or more

10

8

6

4

0

15 or more

10

8

6

4

0

100% (all)

75%

50%

25%

10%

0%

100% (all)

75%

50%

25%

10%

0%

100% (all)

75%

50%

25%

10%

0%

3 or more

2

1

0

100% (all)

75%

50%

25%

10%

0%

= Estimated Impact Level for the Current Situation (SQ)

= Goal Impact Level

= Goal Impact Profile

(a) (a)(a) (a)f(a)f(a)f(a) f(a)

115

100

60

30

0

-30

140

100

60

40

0

-50

145

100

58

30

0

-14

130

100

70

40

0

-15

f(a)f(a)f(a) f(a)

115

100

70

30

0

-30

150

100

0

-35

140

100

60

40

0

-40

140

100

60

30

0

-40

(a) (a)(a) (a)

60% 40%

30%

30% 70% 70% 30%

50% 20% 30%

70%

60% 40%

Source: Research data

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 997

T a b le

3 D e te rm

in a tio

n o ft h e co

n tr ib u tio

n o fi m p ro ve

m e n ti n m o vi n g fr o m

th e S Q

to th e g o a la n d its

p ri o ri tiz a tio

n

C ri te ri a u s e d to

m e a s u re

th e p e rf o rm

a n c e o f F P V 1 – re c o g n it io n

S Q

v a lu e

G o a l

v a lu e

A d d in g v a lu e w h e n

m o v in g fr o m

S Q

to

th e g o a l( ~ )

L o c a lt o g lo b a lu n it

c o n v e rs io n ra te

L o c a lc o n tr ib u ti o n to

F P V 1 – re c o g n it io n

E P V 1 .1 .1 .1 .1 .1

– s c o p e

% o f c o m p a n y u n it s w it h a s y s te m

o f s u g g e s ti o n s fo r im

p ro v e m e n t

�3 0

1 0 0

1 3 0

0 .6

� 0 .7

� 0 .3

� 0 .6

0 .0 8

E P V 1 .1 .1 .1 .1 .2

– in te n s it y

N o . o f w in n e rs

w it h th re e o r m o re

b o n u s d a y s in th e p a s t y e a r

�1 4

1 0 0

1 1 4

0 .4

� 0 .7

� 0 .3

� 0 .6

0 .0 5

E P V 1 .1 .1 .1 .2

– b e n c h m a rk in g

N o . o f c o m p a n ie s th a t, in th e p a s t y e a r, c a m e to

th e c o n c e s s io n a ir e to

le a rn

k n o w le d g e a t th e le v e lo f e x c e lle n c e

5 0

1 0 0

5 0

0 .3

� 0 .3

� 0 .6

0 .0 5

E P V 1 .1 .1 .2

– c ri ti c a lk n o w le d g e re p o s it o ry

% o f u n it s w it h a m a p o f k n o w le d g e

�1 5

1 0 0

1 1 5

0 .7

� 0 .6

0 .4 2

E P V 1 .2 .1 .1

– k n o w le d g e p ro je c ts

% o f p ro je c ts w it h p re v io u s ly e v id e n c e d d e m a n d fo r k n o w le d g e

�3 0

1 0 0

1 3 0

0 .7

� 0 .5

� 0 .4

0 .1 4

E P V 1 .2 .1 .2

– p a rt ic ip a n ts

% o f te a m s th a t h a v e a t le a s t tw o e m p lo y e e s w it h m a s te ry

o v e r th e p ro je c t

s u b je c t

�4 0

1 0 0

1 4 0

0 .3

� 0 .5

� 0 .4

0 .0 6

E P V 1 .2 .2 .1

– s to c k e n d o rs e m e n t

N o . o f c o m m u n ic a ti o n s is s u e d b y th e b o a rd

o f d ir e c to rs

la s t m o n th

to e n c o u ra g e

c ri ti c a l k n o w le d g e re te n ti o n m e a s u re s

�3 5

1 0 0

1 3 5

0 .3

� 0 .4

0 .1 2

E P V 1 .2 .3 .1 .1

– c o n s e q u e n c e a n a ly s is

% o f c ri ti c a l k n o w le d g e m a p p e d fo r w h ic h th e c o n s e q u e n c e s o f n o n -

o b s e rv a n c e a re

fo rm

a lly

d o c u m e n te d

�4 0

1 0 0

1 4 0

0 .2

� 0 .4

0 .0 8

S o u rc e :R

e s e a rc h d a ta

PAGE 998j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

As illustrated in Figure 5, an improvement action was generated for criterion PVE 1.1.1.2 –

critical knowledge repository, measured by the descriptor percentage of units that have a

knowledge map, which has the largest local contribution to PVF 1 – recognition.

This process was repeated for the other criteria demonstrating the potential contribution that

the proposed model makes to the organization.

5. Conclusions and contributions

The competitiveness of organizations is based on knowledge. Organizational knowledge

retention, in particular by the valorization and management of people who have knowledge

in strategic areas, is one of the dimensions with the potential to increase competitiveness.

With this in mind, this study built a model to support the management of organizational

knowledge retention based on the MCDA-C methodology. This instrument was chosen for

its ability to deal with confusing situations that require structuring, evaluation and

recommendation, to monitor and improve decision-making environments in a unique way.

Figure 5 Action plan to increase the performance of the criterion percentage of units that have a knowledge map

VOL. 24 NO. 5 2020 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 999

To construct this model, interviews were conducted with decision-makers, which resulted in

the identification of 66 PEEs, from which created concepts were grouped into six areas of

concern. The areas of concern were tested for properties that constitute an FFPV, followed

by the construction of cognitive maps and their clusters and subclusters. These were

transferred to an HSV and descriptors were constructed for HSV operationalization.

Subsequently, the reference levels of each descriptor were defined, which show the

performances judged by the decision-maker to be excellent, competitive or compromising.

For each descriptor, the current performance level was identified and the goal or target was

established. The model showed potential and vulnerabilities and for the latter, created the

conditions necessary to generate proactive improvement actions in the process of

organizational knowledge retention.

This study uses a constructivist approach to support knowledge retention management,

incorporating the process into the structure, recognizing contextual specifics, identifying

criteria based on values of the decision-maker, building scales that meet the fundamentals

of measurement theory, integrating the criteria from reference levels, providing graphical

and numerical instruments to monitor the actual performance of each key factor and

generating improvement actions, thus giving legitimacy and scientific credibility to the

instrument to support knowledge retention management.

5.1 Findings and implications

The activities of building the model gave the actors the opportunity to debate and expand

their understanding of knowledge retention management and its implications for the

organization, with outcomes such that:

� the objectives and concerns of decision-makers regarding the retention of

organizational knowledge are identified and organized into six strategic areas of

concern, namely, recognition, knowledge dissemination, organizational culture,

succession of professionals, management of vulnerability origins and knowledge

management;

� a multi-criteria model is constructed and operationalized by a cluster of cardinal scales,

showing and measuring the SQ of the performance profile, in a local and global way, to

support the management of the organization’s knowledge retention;

� activities are classified into three performance levels (compromising, competitive and

excellent), supported by graphical and numerical evidence; and

� the process to generate actions to improve the performance of critical activities and

create the conditions to maximize the results of the organization is illustrated.

Several results were observed in the research. First, the MCDA-C protocol used in the

interviews allowed an organized forum of debate about knowledge retention in the

organization between the actors involved and expanded the understanding of

the consequences of disregarding or giving insufficient attention to the problem. Second,

the final model was widely recognized as representing the critical factors and the

assessment scales were recognized as representing the possible performance of the

knowledge retention context. Third, the model, once put into practice, evidenced the

advantages and contributions of the development and use of a multi-criteria decision aiding

model to support the knowledge retention management process. Fourth, the model

provides the organization with guidelines for creating improvement projects. Fifth, the use of

a constructivist approach and the aggregation to unique synthesis criteria using the

epistemological approach for structuring by points of view as proposed by Bana e Costa

et al. (1999), Roy (1993), Rittel and Webber (1973) and Ensslin et al. (2010) for modeling a

specific knowledge retention management context, is a contribution to the knowledge

retention management area.

PAGE 1000j JOURNAL OF KNOWLEDGE MANAGEMENTjVOL. 24 NO. 5 2020

The main practical implications of the research are, first, giving visibility to the

consequences of knowledge loss to the company in the form of the key factors for the

process of knowledge retention and its organization in an HSV operationalized by scales of

measurement and integrated; second, graphically and numerically visualizing the

vulnerabilities of the actual knowledge retention management context; third, creating a

process to generate projects to improve the performance of the knowledge retention

management context; fourth, creating a model to justify actions in the knowledge retention

management context; and fifth, having an objective tool to guide a discussion of the theme.

The main theoretical implication of the research is the use of a constructivist perspective to

model a context where the literature is realistic. A second theoretical implication is the

development of a practical and useful graphical and mathematical assessment model to

support knowledge retention management for a specific organizational context in

accordance with the scientific modeling requirement (Roy, 1993), with scales in accordance

with the measurement theory fundamental (Joint Committee of Guides in Metrology, 2008;

Micheli and Mari, 2014) and with the decision-makers’ (users’) participation and validation.

Of particular interest is that the indicators proposed by this study to evaluate the knowledge

retention model were not identified from the literature. The constructivist nature of the

methodology presumes knowledge construction by the decision-makers, considering their

own perceptions of the aspects deemed necessary and sufficient to evaluate the context

and considering the characteristics and peculiarities of the context within which the model’s

use is proposed. This approach ensures that the model is seen by decision-makers as

legitimate, as it represents, on the one hand, their values and preferences and on the other

hand, the specifics of the context.

5.2 Limitations and future research

The limitations of this study can be divided into practical (business) and theoretical

limitations. The main practical limitation is that the model was built from the perception of

specific decision-makers, for a specific context; its use cannot be generalized to other

decision-makers and/or contexts. It should be noted, however, that while the model is

specific, the process used is universal and others can build models with different context(s)

and decision-maker(s) using the MCDA-C protocols.

The main theoretical limitation is the model construction process. Although the model has

the approval of the decision-makers and the process was guided by the MCDA-C

protocols, conducting the interviews and synthesizing the outputs is a mix of art and

science, depending not only on knowledge of the scientific protocols but also on the

facilitator’s individual skill (Roy, 1993; Bana e Costa et al., 1999).

To expand the knowledge gained from this study, future research could focus on:

� exploring how effective the use of the model is for the organization after some years;

� constructing performance evaluation models using the MCDA-C methodology for other

organizational knowledge management processes, in particular the creation of

knowledge, with a focus on innovation, research and development; and

� conducting similar studies in other organizations to compare with the results of this

research.

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Corresponding author

Clarissa Carneiro Mussi can be contacted at: [email protected]

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