Investigating unlearning and forgetting in organizations: Research methods, designs and implications

Abstract

Purpose
Insight has grown that for an organization to learn and change successfully, forgetting and unlearning are required. The purpose of this paper is to summarize the relevant existing body of empirical research on forgetting and unlearning, to encourage research using a greater variety of methods and to contribute to a more complementary body of empirical work by using designs and instruments with a stronger reference to previous studies.

Design/methodology/approach
As the number of theoretical papers clearly exceeds the number of empirical papers, the present paper deals with the main insights based on the empirical state of research on unlearning and forgetting. So far, these empirical results have shown relationships between unlearning and other organizational outcomes such as innovation on an organizational level, but many of the other proposed relationships have not been investigated. The authors presents suggestion to apply a larger variety of qualitative, quantitative and mixed methods in organizational research.

Findings
Unlearning and forgetting research can benefit both from more diverse theoretical questions addressed in research and from a more complementary body of empirical work that applies methods, designs and instruments that refer to previous research designs and results. To understand and manage unlearning and forgetting, empirical work should relate to and expand upon previous empirical work to form a more coherent understanding of empirical results.

Originality/value
The paper presents a variety of research designs and methods that can be applied within the research context of understanding the nature of organizational forgetting and unlearning. Additionally, it illustrates the potential for different methods, such as experience sampling methods, which capture the temporal aspects of forgetting and unlearning.

Full text

Investigating unlearning and
forgetting in organizations
Research methods, designs and implications
Annette Kluge and Arnulf Sebastian Schüffler
Work, Organisational and Business Psychology, Ruhr-Universität Bochum,
Fakultät für Psychologie, Bochum, Germany, and
Christof Thim,Jennifer Haase and Norbert Gronau
Chair of Business Information Systems and Processes,
Universitat Potsdam, Potsdam, Germany
Abstract
Purpose –Insight has grown that for an organization to learn and change successfully, forgetting and
unlearning are required. The purpose of this paper is to summarize the relevant existing body of empirical
research on forgetting and unlearning, to encourage research using a greater variety of methods and to
contribute to a more complementary body of empirical work by using designs and instruments with a
stronger reference to previous studies.
Design/methodology/approach –As the number of theoretical papers clearly exceeds the number of
empirical papers, the present paper deals with the main insights based on the empirical state of research on
unlearning and forgetting. So far, these empirical results have shown relationships between unlearning and
other organizational outcomes such as innovation on an organizational level, but many of the other proposed
relationships have not been investigated. The authors presents suggestion to apply a larger variety of
qualitative, quantitative and mixed methods in organizational research.
Findings –Unlearning and forgetting research can benefit both from more diverse theoretical questions
addressed in research and from a more complementary body of empirical work that applies methods, designs
and instruments that refer to previous research designs and results. To understand and manage unlearning
and forgetting, empirical work should relate to and expand upon previous empirical work to form a more
coherent understanding of empirical results.
Originality/value –The paper presents a variety of research designs and methods that can be applied
within the research context of understanding the nature of organizational forgetting and unlearning.
Additionally, it illustrates the potential for different methods, such as experience sampling methods, which
capture the temporal aspects of forgetting and unlearning.
Keywords Mixed-methods, Research design, Longitudinal studies, Experience sampling,
Correlational designs, Quasi-experimental and experimental designs
Paper type Conceptual paper
1. Introduction
Insight has grown that for an organization to learn and change successfully, forgetting and
unlearning are required, in addition to knowledge acquisition and dissemination (Grisold
et al.,2017;Grisold and Kaiser, 2017;Fiol and O’Connor,2017a, 2017b;Morais-Storz and
Nguyen, 2017;Nguyen et al.,2018). While the term “organizational unlearning”was
The research was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft,
DFG) with grant number KL2207/6-1, and GR 1846/21-1.
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Received 16 September2018
Revised 29 January2019
13 March 2019
22 March 2019
23 April 2019
Accepted 6 May 2019
The Learning Organization
Vol. 26 No. 5, 2019
pp. 518-533
© Emerald Publishing Limited
0969-6474
DOI 10.1108/TLO-09-2018-0146
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0969-6474.htm

introduced almost as early as the term “organizational learning”appeared in management
research (Hedberg, 1981;Nystrom and Starbuck, 1984;Howells and Scholderer, 2016), the
term “organizational forgetting”only began to crop up in the business and management
literature some decades later, at the turn of the millennium (Argote, 2013;Easterby-Smith
and Lyles,2003, 2011;Martin de Holan et al.,2004;Martin de Holan and Phillips, 2004;
Martin de Holan, 2011). Unlearning has been defined as discarding and replacing old
routines (Huber, 1991;Tsang and Zahra, 2008), while forgetting has been defined as
reducing the influence of old knowledge on cognitive and behavioral processes (Grisold
et al.,2017;Kluge and Gronau, 2018), e.g. by ceasing to use knowledge (Hislop et al.,2014).
However, an imbalance has emerged between the number of theoretical papers and the
empirical testing thereof: in the organizational unlearning and forgetting literature, theories
have dominated over empirical evidence (Kluge and Gronau, 2018). While some empirical
studies have been conducted, these rather appear to stand in isolation.
The foundation of this paper is a recently published review (Kluge and Gronau, 2018)
describing the state of the art of theoretical concepts of intentional forgetting and unlearning
in organizations, which was conducted in 2018 based on the guidelines of Tranfield et al.
(2003). Leading electronic databases were used for the search, including peer-reviewed
publications, conference proceedings and internet sources listed in GoogleScholar,
PsycArticles, PsyINFO and Psyndex (via EBSCO) using the following keywords: organis(z)
ational forgetting, intentional forgetting in organis(z)ations, organis(z)ational unlearning,
organis(z)ational ignorance, knowledge management and forgetting and managing
forgetting.
Altogether, 246 publications were found. The 40 publications reviewed in Kluge and
Gronau (2018) were included by examining the abstracts and in-depth reviews to identify
core contributions. For the present paper, an additional search was conducted using the
terms “organis/ztional unlearning/forgetting þempirical”or “organis(z)ational unlearning/
forgetting þstudy”. In total, 15 scientifically sound empirical studies (in addition to the 40
reviewed earlier), which were conducted in relevant organizational settings and published in
scholarly journals, were identified and included in the present paper.
The purpose of this paper is not to re-assess the number of theoretical concepts, but
rather to illustrate several options for conducting unlearning and forgetting research and to
encourage more empirical studies. We wish to emphasize that unlearning and forgetting
research can benefit from a more complementary body of empirical work that applies
methods, designs and instruments that refer to previous research designs and results. To
achieve a deeper understanding of unlearning and forgetting, empirical work should relate
to and expand upon previous empirical work to form a more coherent understanding of
empirical results.
2. Theoretical background/foundation
In the present paper, we refer both to the term unlearning and to the term forgetting. From a
theoretical and conceptual perspective, most authors have agreed on the following
definitions: unlearning (Hedberg, 1981;Huber, 1991;Tsang and Zahra, 2008;Reese, 2017;
Visser, 2017;Tsang, 2017,Fiol and O’Connor,2017a, 2017b;Starbuck, 2017) means
discarding and replacing old routines (Huber, 1991) and is assumed to support the objective
to install new routines (Tsang and Zahra, 2008). Unlearning of routines, which no longer
serve the organizational objectives is required to successfully implement new routines,
which do support the organizational goals in the present and future (Ellwart and Kluge,
2019). Forgetting refers to the facilitation of change, especially, when current knowledge is
perceived as an obstruction and a competitor to new knowledge (Martin de Holan, 2011).
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During forgetting, managers work to forget established knowledge that was or is perceived
to be a barrier to increased organizational effectiveness (Martin de Holan and Phillips, 2004).
Forgetting in organizations involves processes that deliberately impede the recall of certain
organizational memory items; to support an organization’s changed strategic goal
achievement, these memory items and information elements are no longer provided in the
case of a certain query (Kluge and Gronau, 2018). The aim of forgetting is to reduce the
influence of old knowledge (Grisold et al.,2017) and to stop old knowledge from being used
(Hislop et al., 2014). Nevertheless, in the reviewed studies, the deployment and
operationalization of these terms are quite diverse.
From a methodological perspective, the overall plan of empirical research is termed the
research strategy. The strategy includes the research design and research method. The
research design encompasses the concrete plan to test a hypothesis or to answer a research
question. A research method is the choice of a concrete manner of data collection to
implement the overall plan.
Austin et al. (2002) and Scandura and Williams (2000) cluster aspects of a research design
into the general setting (e.g. laboratory, field and simulation), study design (e.g. passive
observation, experiment, case study and archival) and temporal aspects (e.g. cross-sectional,
longitudinal and cohort). All of these research designs can include qualitative and
quantitative research methods (Stone-Romero et al., 1995). Qualitative research (e.g. action
research, archival data, case study, document interpretation, ethnography, grounded theory
and interviewing, Aguinis et al., 2009) yields non-numerical data such as observations or
personal accounts of experiences (Pistrang and Barker, 2012;Zedeck, 2014). Quantitative
research (e.g. reaction times, tests, questionnaires, performance measures and log file data)
relies on measuring variables using a numerical system with the aim of analyzing the
measurements through the use of statistical methods (Zedeck, 2014).
In Sections 3, we give examples of empirical research on organizational unlearning and
forgetting. We group the work identified (see above) based on the distinction between
individual, team and organization level analysis and on the type of method used.
3. Empirical research on organizational unlearning and forgetting
3.1 Survey method (using questionnaires)
We start by presenting empirical research related to survey methods using questionnaires at
different levels of analysis: the individual, team and organizational level.
On an individual level, Gutiérrez et al. (2015) explored the influence of unlearning on the
acquisition and assimilation of knowledge (by conducting questionnaires with 55 doctors
and 62 nurses), the influence of acquisition and assimilation and how acquisition and
assimilation can help home care units to align technology and physician-patient knowledge.
Becker (2010) studied issues identified as potential influencers of unlearning. The authors
developed a survey, which they administered in an Australian corporation (N= 189) that
was undergoing large-scale change because of the implementation of an enterprise
information system. Based on the findings, the following factors that are relevant to the
unlearning process during times of change were identified: understanding the need for
change, the level of organizational support and training, assessment of the change, positive
experience and informal support, the organization’s history of change, individuals’prior
outlooks and individuals’feelings and expectations.
On the team level, Akgün et al. (2006) investigated unlearning as changes in beliefs and
routines during team-based projects in new product development teams. To test the
antecedents and consequences of a team unlearning model, 319 teams were investigated and
the data were analyzed using structural equation modeling. The results showed that team
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crisis and anxiety have a direct impact on team unlearning; environmental turbulence also
has a direct impact on team crisis, anxiety and team unlearning. Finally, after team beliefs
and project routines have changed, implementing new knowledge or information positively
affects new product success.
On the organizational level, several studies have been conducted, which are additionally
grouped according to higher-order themes and topics. A study by Becker et al. (2006),
addressed the question of “who unlearns?”and showed that larger organizations give far
more consideration to unlearning than do smaller organizations. Organizations with a high
labor turnover focus less on unlearning than those with a more stable workforce.
A study by Cegarra-Navarro and Moya (2005), focused on the relationship between
unlearning on the individual and group level and organizational outcomes. The authors
used structural equation modeling to test hypotheses on, for example, the relation between
individual unlearning, group unlearning with respect to human capital and performance.
The results indicated that “intellectual capital”depends on the unlearning among members
of the company.
Unlearning as a precondition for organizational outcomes was addressed by the
following studies:
Cegarra-Navarro and Dewhurst (2006) presented a structural equation model, which was
validated through an empirical investigation of 139 small- and medium-sized enterprises
(SMEs) in the Spanish optometry sector. The results showed that companies need to support
unlearning as a first step; otherwise, unlearning does not have any significant effect on the
creation of relational capital.
Leal-Rodríguez et al. (2015) tested the mediating role of innovation outcomes on the
relationship between organizational unlearning and overall performance by applying a
conditional process model (structural equation modeling) using data from 45 firms from the
Spanish automotive components manufacturing sector. They found that innovation
outcomes partially mediate the influence of organizational unlearning on overall
performance.
Yang et al. (2014) investigated 193 sample firms from high-technology industries and
showed that the change dimension of unlearning (as an internal process) positively affects
radical innovation, whereas the forgetting dimension (forgetting by external partners) has a
negative effect. Organizational unlearning was defined in terms of changes in routines and
beliefs. The “forgetting”dimension mostly affects external suppliers and customers because
these parties will lose the familiarity with or expectations of the firm in question that have
accumulated for years and is assumed to be an outside-in dimension.
Furthermore, work-life balance has been investigated as an outcome of unlearning.
Cegarra-Navarro et al. (2016) argued that an unlearning context that fosters the updating of
knowledge is likely to be essential for SMEs that are trying to implement a culture of work-
life balance. The authors investigated 229 SMEs in the Spanish metal industry. The results
showed that to strengthen a work-life balance culture and innovation-related outcomes,
SMEs must meet the challenge of developing an unlearning context to counteract the
negative effects of outdated knowledge in relevant areas and to facilitate the replacement of
out-of-date or obsolete knowledge.
Martelo-Landroguez et al. (2018) described in their research model how the
complementary roles of absorptive capacity (direct effect) and the fostering of an
organizational unlearning context (moderating effect) affect green customer capital within
the Spanish automotive component manufacturing sector. Based on a survey (with 112
usable surveys) and path modeling, the empirical results showed that to create green
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customer capital, companies should absorb new knowledge and build a context of
organizational unlearning.
Finally, Wong et al. (2018) reported on a study that aimed to examine the factors affecting
contractors’organizational readiness for more extensive use of prefabrication in projects. As
a conceptual framework that depicts the interrelationships among organizational readiness,
unlearning and counterknowledge were proposed. Data were collected from a survey
conducted in Australia. The results indicated that unlearning is positively correlated with
organizational readiness.
The fact that unlearning is an important mediator was demonstrated by Cegarra-
Navarro et al. (2011), who examined the relationship between the exploration and
exploitation of knowledge within an unlearning context and the effects of these two factors
on the improvement in the performance of 229 SMEs in the Spanish metal sector. The results
revealed that the effects of the exploration and exploitation of knowledge on organizational
performance are mediated through an unlearning context. Huang et al. (2018) examined how
organizational forgetting affects innovation performance under consideration of
environmental turbulence as a moderating factor of the analysis framework. Based on a
survey sample of 320 Chinese companies, the study validated a moderated mediating model
of organizational forgetting. According to the findings, organizational forgetting is a critical
determinant for improving innovation performance. In addition, organizational forgetting
cannot promote an organization’s innovation performance without absorptive capacity and
the mediating effect of absorptive capacity is more positive when turbulence is stronger.
In summary, the main focus of unlearning and forgetting research using surveys lies on
the organizational level and this research has mainly addressed the relationship between
unlearning as a prerequisite and organizational outcomes such as radical innovation and
culture, and the mediating role between innovation endeavors and organizational outcomes.
Other results show that unlearning is itself a precondition for innovation and readiness for
change. There are only isolated studies asking, which organizations unlearn and who in the
organization is unlearning. Some research exists on the individual level, which was
conducted during the implementation of new technology. On the team level, one study
described the antecedents (crisis and anxiety) of team unlearning. As this research is
descriptive and correlational in nature, none of the studies investigated what actually
happens during unlearning, and how unlearning or forgetting can be effectively
implemented.
3.2 Other methods
Archival data plus formal theory was applied by Agrawal and Muthulingam (2015), who
analyzed data on 2,732 quality improvement initiatives implemented by 295 vendors of a car
manufacturer. They found that organizational forgetting affects quality gains obtained from
learning by doing (autonomous learning) and from undertaking quality improvement
initiatives (induced learning).
To give examples of case studies, Fernandez and Sune (2009) used two qualitative case
studies in higher education involving situations of organizational forgetting to derive
propositions about the causes of forgetting. Usman et al. (2018) built mainly on social
learning theory, using a single case study as research methodology and collecting data from
40 semi-structured interviews to understand how two key aspects of ethical leadership –
accountability and honesty –facilitate the unlearning of destructive and inappropriate
behaviors and practices. The goal of a study by Matsuo (2017) was to examine the
managerial unlearning process upon promotion from senior manager to executive officer:
analyzes of interview data on an individual level with 46 executive officers at medium-sized
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and large Japanese firms indicated that managers unlearned and learned their managerial
skills in relation to “decision making”,“delegation and motivation”and “collecting
information”. Specifically, decision-making skills switched from “short-term, analytic and
partial”to “long-term, intuitive and holistic”.
A study by Mehrizi and Lashkarbolouki (2016), constituted an exception, by applying a
longitudinal research design (data used from the past six-eight years) and a mixed methods
approach (observation, document analysis and formal interviews). Based on two
longitudinal case studies, the authors proposed a process model that establishes four stages
of business model unlearning, namely, “realizing,”“revitalizing,”“parallelizing”and
“marginalizing.”They also discussed how unlearning dynamics help us to understand the
importance of single- and double-loop unlearning, consider the double-faceted nature of
business models and acknowledge the complex temporal dynamics of unlearning.
4. Discussion of research methods and (potential) findings
Tables I and II summarize categories of research designs (e.g. experimental, quasi-
experimental and non-experimental), methods (e.g. qualitative, quantitative and mixed
methods), strategies (e.g. formal theories/literature reviews, sample surveys, laboratory
experiments, experimental simulation, field studies, field experiments or computer
simulations) and the opportunities and challenges they bring. The summary is based on
review articles by (in chronological order) Podsakoff and Dalton (1987),Stone-Romero et al.
Table I.
Selection of
qualitative research
methods (case study
and interviews) and
archival data
analysis adapted
from and based on
Turner et al. (2017)
and Zedeck (2014)
arranged according
to aspects of intern
and extern validity
Research method Opportunities and challenges
Case study
In depth investigation of a single individual,
event or other entity, e.g. to describe and
understand the forgetting process of a single
organizational unit, department and section
Example: Fernandez and Sune (2009)
þSuited for capturing behaviors that were
displayed in an authentic context
þAllows for intensive analysis of an issue
þThe use of multiple case studies allows for more
claims regarding generalizability
Limited in the extent to which findings may be
generalized
Not well suited for maximizing generalizability
with respect to populations
Interviews
A directed conversation in which a researcher
intents to elicit specific information from an
individual for research purposes, e.g. interviews
with workers and managers on how they cope
with the requirement of forgetting
Example: Matsuo (2017)
þCapturing behaviors that have occurred in an
authentic context
Memory and self-serving biases might occur
Reliability is a concern: more active participation
in the situation, possible biases and impacts of
personal judgments
Analysis of archival records
Information about past events and/or behaviors,
that are stored in relative permanent form, e.g.
books, journals, historical documents and other
records, e.g. to understand the forgetting or
fading of procedures, knowledge elements, that
are not mentioned anymore and are removed
from or exchanged in a document
Example: Agrawal and Muthulingam (2015)
þAllows unobtrusive observation of human activity
in a natural setting
þEffective in maximizing generalizability with
respect to populations, enhancing precision in
control/measurement of variables and/or capturing
behaviors that have taken place in an authentic
context
Only past events are captured
Causal inferences are more tentative than lab
experiments
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Table II.
Selection of
quantitative and
formal methods
adapted from and
based on Turner
et al. (2017) and
Zedeck (2014)
arranged according
to aspects of intern
and extern validity
(Field)Surveys and Field Studies
Study in which a group of subjects is selected from
a population and some selected data are collected;
collecting information on a specific topic in a
relevant group or entity, in their natural
environment; and more passive observation of
relationships between variables, e.g. to understand
the relationship between team and leadership
variables and the support of perceived forgetting
Examples: Akgün et al. (2006),Becker (2010),
Cegarra-Navarro and Moya (2005),Cegarra-
Navarro and Dewhurst (2006),Cegarra-Navarro
et al. (2011),Cegarra-Navarro et al. (2016),Yang
et al. (2014).Leal-Rodríguez et al. (2015),Martelo-
Landroguez et al. (2018),Huang et al. (2018) and
Wong et al. (2018)
þPrecision in control/measurement of variables and
capturing behaviors that were displayed in an
authentic context
þAlready validated instruments can be used
Only snapshot of current situation
Possible memory biases
Subjects can respond only to predefined items
Challenges in extrapolation of findings to whole
populations
Causal relationship difficult to infer, only assumed
relationships
Field experiment
Study outside the laboratory; subjects are not
randomly selected and assigned to different
conditions (independent variable); some active
manipulation of variables, e.g. to investigate
different intervention forms (e.g. workshop,
trainings) to support forgetting
þEnhancing precision in control/measurement of
variables and capturing behaviors that have
occurred in an authentic context
þIncorporates mundane aspects of context
Less options for experimental manipulation
Possible confoundation with other variables, that
are difficult to control for over a period of time
Non-representative samples and settings
Use of operational definitions of manipulation and
measures of interest
Lab experiment and experimental simulation
Series of observations conducted under controlled
conditions to study the relationship between
predefined variables (independent and dependent
variables). Includes random selection of
participants and their random assignment to
conditions; active manipulation of independent
variable, e.g. to deliberately investigate micro-
processes and cognitive processes of forgetting in
teams and individuals
Examples: Kluge et al. (2018),Schüffler et al. (2019)
þSuited for precision in control/measurement of
variables
þControl over experimental manipulations
þAllows for causal inferences
þIn case of simulations: capturing behaviors that
have taken place in an authentic context
Limited with respect to generalization
Computational simulation
Artificial creation of experimental data through the
use of a mathematical or computer model to test
the behavior or model under controlled conditions,
e.g. to investigate forms of turn over or downsizing
and organizational forgetting and renewal over a
simulated period of time (e.g. decades)
Examples: Jain and Kogut, 2014 and Bruderer and
Singh (1996)
þEnhancing precision in control/ measurement of
variables
þEffective in maximizing generalizability with
respect to populations
Limited with respect to in depth understanding
Formal theory (mathematical)
A model or set of rules used to understand and
predict various behaviors in mathematical terms,
e.g. to compare different forms of forgetting of
different organizational structures, in combination
with the comparison of different market conditions
þEnhances precision in control/measurement of
variables and can be effective in maximizing
generalizability with respect to populations
Needs some empirical basis and data to be built on
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(1995),Scandura and Williams (2000), Austin et al. (2002), Aguinis et al. (2009), Cooper et al.
(2012) and Aguinis et al. (2019).
Table I describes the most commonly used qualitative methods and the use of archival
data in organizational research (Aguinis et al.,2009;Turner et al.,2017) and illustrates
possible applications for organizational forgetting and unlearning research.
Examples of studies using archival data (Agrawal and Muthulingam, 2015), interviews
(Matsuo, 2017) and case studies (Fernandez and Sune, 2009) were described above.
Table II describes the most commonly used quantitative methods in organizational
research in general (Aguinis et al.,2009;Turner et al.,2017) and illustrates possible
applications for organizational forgetting and unlearning research.
In Section 4.1, we elaborate in greater detail on methods that have been hitherto neglected
in empirical unlearning and forgetting research.
4.1 Quasi-experimental design
As examples of quasi-experimental designs are lacking to date, we give a hypothetical
example of a quasi-experimental design. Moreover, we use the propositions by Martin de
Holan (2011), who suggested that the amount and type of effort required to forget depend on
the category of knowledge involved, and on the relationship between the new knowledge
and the old knowledge (the distance between the new and old knowledge). Using a quasi-
experimental design to test these hypotheses, two comparable organizational departments
are required, which differ regarding the distance between the old and new knowledge (far
versus near). Organizational members of both departments could rate the distance between
the new and old knowledge and researchers could measure the rate or speed of forgetting
and the speed of change in both departments over time. The results of the quasi-
experimental design would then reveal assumed relationships between the independent
variable (the distance between old and new knowledge) and the impact on the dependent
variable (the speed of change). However, alternative explanations are difficult to rule out, as
other variables which may differ, e.g. charismatic leadership or supportive group dynamics,
could also serve as an explanation for the speed of change.
4.2 Randomized experimental design
A randomized experimental design could either use a special- or a non-special-purpose
setting (Stone-Romero, 2011) to investigate the influence of, for example, organizational
actions as independent variables and their impact on unlearning and forgetting as
dependent variables to measure effects on the organizational level. A special-purpose setting
might be a laboratory setting that is designed as a production setting or shop floor or an
industrial site that is used for experimental studies. Special-purpose settings cease to exist
when research has been completed and are designed for intentional manipulation of the
independent variable. For instance, a “learning factory”is a special-purpose setting with
high physical and psychological fidelity. A study by Schüffler et al. (2019) demonstrated
how a learning factory can be used to investigate the importance of eliminating retrieval
cues for forgetting a knowledge-intensive multi-actor routine. In the controlled setting with
four measurement times, it was shown that particular elements of a multi-actor routine are
more difficult to forget if they have been well learned before, compared to less well-learned
elements (Kluge et al.,2018). It follows that not all elements of a routine are forgotten at the
same speed.
Non-special-purpose settings (Stone-Romero, 2011), for instance, those used for field
experiments, share similar challenges to those of quasi-experimental settings. They would,
of course, include all organizational characteristics and their impact on unlearning and
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forgetting in parallel, such as organizational history, culture and values, human resource
management practices, leadership, structure and technology (Cheung et al.,2017). If one
wished to use a non-special-purpose setting to investigate, for example, the three phases of
unlearning as proposed by Reese (2017), Phase 1: destabilization, crisis and mismatch; Phase
2: discarding, weathering and interruption; and Phase 3: experimenting, obsolescence and
recovery, one could use two similar non-special-purpose settings, for instance, two
production sites of one company in different countries, to investigate the impact of different
leadership values that are displayed at these sites on workers’and employees’perceptions of
the phases through which they have to go.
4.3 Computer simulations
Computer simulations are model-based descriptions of the consequences of theoretical
assumptions and side effects in a fast-forward mode; they allow for the observation of
interdependencies and complex interactions between variables and their dynamics to
investigate process aspects more closely (Runkel and McGrath, 1972;Turner et al.,2017;
Zedeck, 2014). The results of a simulation conducted by Bruderer and Singh (1996) revealed
that replacing inappropriate organizational routines helps in the quick discovery of a new,
viable organizational form, which adapts better to a fast-changing environment. By using
computer simulations, it is possible to observe extreme and unusual system states, which
cannot be manipulated (for ethical reasons) in reality. Instead of direct observation,
consequences can be modeled and inferred from the simulation results. Finally, several
simulation runs can be implemented to vary system variables systematically in different
combinations (Kluge and Schilling, 2004). For research on organizational forgetting,
computer simulations could be used, for example, to model different forms of dynamic
environments, several forms of interventions or organizational features that are assumed to
support forgetting to observe the speed of forgetting and the success of change and
adaptation in the environments (Jain and Kogut, 2014).
4.4 Mixed methods
Finally, methods can be combined in mixed-methods approaches, which are based on the
idea that the use of multiple, different research methods generates a better understanding of
a given theory or phenomenon (Molina-Azorin et al., 2017;Turner et al., 2017); this can also
be applied to research on unlearning and forgetting in organizations. As all methods have
their limitations, a combination of different methods can compensate for the individual
shortcomings of a single method alone. The integration of qualitative and quantitative
methods as a mixed-methods approach in one study is an emerging trend, which matches
the complexities of organizational phenomena (Molina-Azorin et al., 2017). Turner et al.
(2017) offer a promising approach for a combination of different methods, namely, they
developed a framework for mixed methods, e.g. the combination of archival methods, case
studies, computer simulations, experimental simulations, field experiments, formal theory
(mathematical, laboratory experiments and surveys, see Tables I and II) and provided
several examples of benchmark studies using mixed methods. At the same time, they also
pointed to challenges, e.g. the replication of findings, especially, when qualitative data are
involved. Nevertheless, challenges depend on the study design and, for example, increase
when mixed methods are applied to the same sample or organizational setting.
4.5 Summary of findings
Taking into account the current state of the art of empirical research, from a content-related
perspective, the above-cited findings can be summarized as follows:
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Individual- and team-level effects:
not all elements of a routine are forgotten at the same speed. Particular elements of a
multi-actor routine are more difficult to forget if they have been well learned before,
compared to less well-learned elements (Kluge et al., 2018; Schüffler et al., 2019);
individual unlearning of managers is discontinuous and occurs during the process
of their promotion (Matsuo, 2017);
ethical leadership supports individual unlearning (Usman et al., 2018); and
crisis and anxiety are antecedents of team unlearning (Akgün et al., 2006).
Organizational-level effects:
replacing inappropriate organizational routines helps in the quick discovery of a
new, viable organizational form, which adapts better to a fast-changing
environment (Bruderer and Singh, 1996);
unlearning is a precondition for relational capital (Cegarra-Navarro and Dewhurst,
2006);
unlearning affects radical innovation (Yang et al., 2014);
unlearning supports cultural change (Cegarra-Navarro et al., 2016);
organizational forgetting supports quality improvement in autonomous learning
(Agrawal and Muthulingam, 2015);
unlearning has a positive relationship with organizational readiness (Wong et al.,
2018);
forgetting is a determinant for improving innovation in combination with
absorptive capacity under the influence of turbulence (Huang et al., 2018).
Mediator and moderator effects:
the effects of exploration and exploitation of knowledge on performance are
mediated by unlearning (Cegarra-Navarro et al., 2011);
the relation between unlearning and performance is mediated by innovation
outcomes (Leal-Rodríguez et al., 2015); and
unlearning is a moderator of the relationship between absorptive capacity and
creating green customer capital (Martelo-Landroguez et al., 2018).
From a methodological perspective, the empirical state of the art is limited for several
reasons: in relation to the large body of theoretical concepts, only a small number empirical
studies exist, the studies seem to stand alone and the studies predominantly used one
research method (cross-sectional survey data). What we can learn about unlearning and
forgetting from these studies is limited to the conclusion that unlearning and forgetting
matter as a predictor, mediator or moderator. However, what happens while managers,
employees and workers are unlearning? Which organizational characteristics support or
hinder unlearning and forgetting? To what extent does the technology used slow the
unlearning process down? Can unlearning and forgetting to be managed?
We assume that the understanding of organizational unlearning and forgetting can
benefit from both a more coherent and interrelated empirical investigation and more diverse
research in terms of research methods and strategies to foster the understanding of what
happens in the unlearning and forgetting processes. As a suggestion, we see some
innovative research potential in the development of smartphone use for online surveys. This
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overcomes some of the limitations of field studies (in terms of “snapshots”) and addresses
the challenge of measuring the temporal aspects of forgetting and unlearning. In particular,
experience sampling methods (ESM), in combination with more sophisticated statistical
analysis such as multilevel analysis, render it possible to gather data over a longer time
period (of forgetting or unlearning). ESM allows researchers to gather detailed data on
organizational members’daily experiences over time (Aguinis and Edwards, 2014).
Moreover, it offers the potential to combine several approaches and methods of analysis,
such as qualitative and quantitative methods and temporal aspects of forgetting such as
cross-sectional and longitudinal designs. As such, ESM is able to acknowledge intra- and
inter-individual forgetting and unlearning developments over time and reduces biases and
errors, which are inherent in the global retrospective reporting of forgetting experiences. A
further advantage lies in the possibility to study and capture the ongoing stream of
forgetting behavior in its natural sequence and occurrence (instead of cross-sectionally).
Finally, ESM data can be analyzed on an individual, team and organizational level (Fisher
and To, 2012;Csikszentmihalyi and Larson, 2014;Uy et al., 2010).
5. Conclusion
The outline of the current and existing empirical results on forgetting and unlearning
showed that only a limited number of empirical studies exist. The majority of the studies
used field surveys and cross-sectional designs, showing that unlearning and forgetting
contributes to organizational outcomes. The large number of involved organizations is
impressive and demonstrates the economic impact of unlearning and forgetting of human or
relational capital (Cegarra-Navarro et al.,2011). Other more or less “stand-alone”studies
show how managers unlearn and forget to reach the next management level (Matsuo, 2017)
or how ethical leadership facilitates the unlearning of destructive behavior (Fernandez and
Sune, 2009). Every empirical study summarized in the introduction makes a valuable and
unique contribution to the field. Nevertheless, the big picture is still hard to grasp, as the
samples selected, methods used, levels selected for analysis and designs are quite diverse.
5.1 Implications for theory
Some theoretical implications drawn from this review of empirical results are as follows: we
learned from the existing body of research that unlearning and forgetting matter. However,
of course, this would also hold true for change or organizational development in general.
From a theoretical point of view, empirical research could be more precise in differentiating
between change, development and unlearning and forgetting. In many studies, the items
used in several questionnaires seem to address change rather than unlearning or forgetting.
This can also be observed for the distinction between unlearning and learning. In several
studies, it seems to be implicitly assumed that if learning has taken place, unlearning must
have been the cause. While this might indeed be the case, it has not yet been addressed and
investigated. Further research could also clarify when unlearning and forgetting is
necessary and essential and when learning and change is sufficient to fit the purpose of
organizational adaptation. Is forgetting and unlearning essential for more radical change
such as double-loop learning and episodic change, while learning and development is more
relevant to continuous change? Under which conditions is unlearning and forgetting the
only way in which an organization can adapt?
Some studies demonstrate unlearning and forgetting in the development of managers or
show that ethical leadership supports unlearning of destructive behavior. But what is the
general role of leadership in unlearning? For instance, is transformational leadership as
relevant for unlearning as it is important for change? Do managers need to unlearn first
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before their subordinates can unlearn? Can leadership actively support employees’and
workers’unlearning in an organization?
Further “blind spots”in research are the roles of structure and technology within
organizations, which may either hinder or support unlearning and forgetting. Do
organizational structures differ in their ability to support unlearning? Are agile and young
organizations faster at unlearning and organizations with a long tradition and many
hierarchies slower? What is the role of technology? To what extent might existing
technology hinder unlearning because the routines that need to be unlearned are interwoven
with technology that has not yet been replaced? Does the implementation of new technology
accelerate unlearning and forgetting?
A more systematic approach to the development of research questions, e.g. derived from
a coherent theoretical framework that relates to the existing evidence, can be helpful to
realize this endeavor.
Finally, a practical outcome of further research has to be addressed if unlearning and
forgetting can be managed. Are there evaluation studies of intervention techniques that
accelerate unlearning and forgetting? Can forgetting and unlearning be managed in terms of
their speed?
5.2 Implications for practice
One practical implication drawn from the summary of existing studies is that there is a need
for studies, which mutually refer to each other. When preparing the summary of results, we
observed that while authors referred to many theoretical papers in their introductions, they
did not refer to the existing empirical body of research. The reason for a particular research
question was mainly driven by a theoretical and conceptual paper, rather than by an
advancement of empirical results. It can be assumed that research will continue to be slow to
advance if every study “reinvents the wheel”instead of building on existing research, e.g. by
re-using questionnaires, by conducting replication studies etc. For example, a worthwhile
endeavor could be to develop and validate a questionnaire that is used by several
researchers in many branches and on different levels of analysis. A standardized survey or
questionnaire instrument that is frequently used and becomes standard in the field of
organizational unlearning and forgetting could help to greatly increase the number of
studies and the empirical results.
Further practical implications concern the aspect of what is measured and how it is
measured. For example, the use of questionnaire data in a cross-sectional design is only one
research method taken from the variety of methods introduced. The empirical field of
organizational unlearning and forgetting is still mostly unexplored. The theoretical
questions raised above give first ideas for additional research questions, which are worthy
of investigation, e.g. the role of structure or technology. However, these questions might be
better investigated by using field experiments, longitudinal designs or ESM to observe
processes of organizational unlearning and forgetting. Moreover, laboratory experiments
can also be useful, for example, to address the role of technology and how technology-
embedded routines foster or hinder unlearning. As the suitability of each method depends
on the specific theoretical question, different methods (other than cross-sectional
questionnaire studies) need to be applied to address different theoretical questions. One
practical solution to achieve a more comprehensive understanding of organizational
unlearning and forgetting lies in mixed-methods approaches. With respect to limitations, as
pointed out by Turner et al. (2017), all methods include strengths and weaknesses regarding
internal and external validity, the precision of control and measurement, the authenticity of
context and the generalizability of findings.
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In this respect, the present paper tries to encourage more empirical research to enable us
to learn together and from each other, with the aim of deepening the insights into
organizational unlearning and forgetting, and bringing organizational forgetting research to
the next level.
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Corresponding author
Annette Kluge can be contacted at: annette.kluge@rub.de
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