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An Adaptive Network Model Simulating the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning
Abstract
This paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency, and (3) Company culture and leader’s behavior must align for the best learning effects. Computational simulations of the introduced adaptive network were analyzed in different contexts varying in organization culture and leader characteristics. Statistical analysis results proved to be significant and supported the research hypotheses. Ultimately, this paper provides insight into how organizations that foster a mistake-tolerant attitude in alignment with the leader, can result in significantly better organizational learning on a team and individual level.
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Purpose – This study aims to present the overview of intellectual capital creation micro-mechanisms
concerning formal and informal knowledge processes. The organizational culture, transformational leadership
and innovativeness are also included in the investigation as ascendants and consequences of the focal relation
of intellectual capital and knowledge processes.
Design/methodology/approach – Based on a sample of 1,418 Polish knowledge workers from the
construction, healthcare, higher education and information technology (IT) industries, the empirical model was
developed using the structural equation modeling (SEM) method.
Findings – The study exposes that the essence of transformational leadership innovativeness oriented is
developing all intellectual capital components. To do so, leaders must support both formal and informal
knowledge processes through the organizational culture of knowledge and learning. Furthermore, for best
results of the knowledge transformation into intellectual capital, the learning culture must be shaped by both
components: learning climate and acceptance of mistakes.
Practical implications – Presented findings can be directly applied to organizations to enhance innovativeness.
Namely, leaders who observe that the more knowledge is formally managed in their organizations, the less effective
the knowledge exchange is-should put more effort into supporting informal knowledge processes to smoothly
develop human and relational intellectual capital components. Shortly, leaders must implement an authentic
learning culture, including the mistakes acceptance component, to use the full organizational potential to achieve
intellectual capital growth. Intellectual capital growth is essential for innovativeness.
Originality/value – This study presents the “big picture” of all intellectual capital creation micromechanisms linking transformational leadership with organizational innovativeness and explains the
“knowledge paradox” identified by Mabey and Zhao (2017). This explanation assumes that intellectual capital
components are created informally (i.e. human and relational ones) and formally (i.e. structural ones). Therefore,
for best effects, both formal and informal knowledge processes, must be supported. Furthermore, this study
exposes that the intensity of all explored micro-mechanisms is industry-specific
Although there is much literature on organisational learning, mathematical formalisation and computational simulation of it is considered a very challenging topic with almost no literature addressing it in a principled manner. This book provides an overview of recent work on mathematical formalisation and computational simulation of organisational learning by exploiting the possibilities of self-modeling network models to address it.
This paper addresses formalisation and computational modeling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end, a second-order adaptive self-modeling network model is introduced and an example simulation for the case study is discussed.
A video presentation of this paper at BICA'21 can be found at the YouTube Self-Modeling Networks channel at https://www.youtube.com/watch?v=StRGmqY0QD0. Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.
Purpose
This study aims to understand and compare how the mechanism of innovative processes in the information technology (IT) industry – the most innovative industry worldwide – is shaped in Poland and the USA in terms of tacit knowledge awareness and sharing driven by a culture of knowledge and learning, composed of a learning climate and mistake acceptance.
Design/methodology/approach
Study samples were drawn from the IT industry in Poland ( n = 350) and the USA ( n = 370) and analyzed using the structural equation modeling method.
Findings
True learning derives from mistake acceptance. As a result of a risk-taking attitude and critical thinking, the IT industry in the USA is consistently innovation-oriented. Specifically, external innovations are highly correlated with internal innovations. Moreover, a knowledge culture supports a learning culture via a learning climate. A learning climate is an important facilitator for learning from mistakes.
Originality/value
This study revealed that a high level of mistake acceptance stimulates a risk-taking attitude that offers a high level of tacit knowledge awareness as a result of critical thinking, but critical thinking without readiness to take a risk is useless for tacit knowledge capturing.
See a video presentation on YouTube here: https://www.youtube.com/channel/UCCO3i4_Fwi22cEqL8M_PgeA.
This paper covers the contents of the Keynote Speech with the same title. The paper addresses the use of self-modeling networks to model adaptive biological, mental, and social processes of any order of adaptation. A self-modeling network for some base network is a network extension that represents part of the base network structure by a self-model in terms of added network nodes and connections for them. A network structure, in general, involves network characteristics for connectivity (connections between nodes), aggregation (combining multiple incoming impacts on a node), and timing (node state dynamics speed). By representing some of these network characteristics by a self-model using dynamic node states, these characteristics become adaptive. By iterating this construction, multi-order network adaptation is easily obtained. A dedicated software environment for self-modeling networks that has been developed supports the modeling and simulation processes. This will be illustrated for a number of adaptation principles from a number of application domains, for example, for Cognitive Neuroscience by a second-order adaptive network model to model plasticity of connections and node excitability, and metaplasticity to control such plasticity.
For related videos, see the YouTube channel on Self-Modeling Networks here: https://www.youtube.com/channel/UCCO3i4_Fwi22cEqL8M_PgeA. In network models for real-world domains often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher-order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and maybe still more levels of higher-order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, al-so second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from Social Science for bonding based on homophily. This first-order adaptation principle describes how connections are changing, whereas this first-order adaptation principle itself changes over time by a second-order adaptation principle. As a second illustration, it is shown how plasticity and metaplasticity from Cognitive Neuroscience can be modeled.
This book addresses the challenging topic of modeling adaptive networks, which often have inherently complex behaviour. Networks by themselves usually can be modeled using a neat, declarative and conceptually transparent Network-Oriented Modeling approach. For adaptive networks changing the network’s structure, it is different; often separate procedural specifications are added for the adaptation process. This leaves you with a less transparent, hybrid specification, part of which often is more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach by which designing adaptive network models becomes much easier, as also the adaptation process is modeled in a neat, declarative and conceptually transparent network-oriented manner, like the network itself. Due to this dedicated overall Network-Oriented Modeling approach, no procedural, algorithmic or programming skills are needed to design complex adaptive network models.
A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, as adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive too, can be modeled just as easily; for example, this can be applied to model metaplasticity from Cognitive Neuroscience. The usefulness of this approach is illustrated in the book by many examples of complex (higher-order) adaptive network models for a wide variety of biological, mental and social processes.
The book has been written with multidisciplinary Master and Ph.D. students in mind without assuming much prior knowledge, although also some elementary mathematical analysis is not completely avoided. The detailed presentation makes that it can be used as an introduction in Network-Oriented Modelling for adaptive networks. Sometimes overlap between chapters can be found in order to make it easier to read each chapter separately. In each of the chapters, in the Discussion section, specific publications and authors are indicated that relate to the material presented in the chapter. The specific mathematical details concerning difference and differential equations have been concentrated in Chapters 10 to 15 in Part IV and Part V, which easily can be skipped if desired. For a modeler who just wants to use this modeling approach, Chapters 1 to 9 provide a good introduction.
The material in this book is being used in teaching undergraduate and graduate students with a multidisciplinary background or interest. Lecturers can contact me for additional material such as slides, assignments, and software. Videos of lectures for many of the chapters can be found at https://www.youtube.com/watch?v=8Nqp_dEIipU&list=PLF-Ldc28P1zUjk49iRnXYk4R-Jm4lkv2b.
This study aims to verify Lawler's (1992, 2008) theoretical proposition that the complementariness and coherence of leadership empowerment practices (LEB) need to be jointly considered in order to adequately understand their relation with employees’ levels of behavioral empowerment. Patterns of relations among three LEB (Delegation, Coaching, and Recognition), and five indicators of behavioral empowerment were analyzed among a sample of 474 Canadian employees. Lawler's proposition was tested using a person-centered mixture regression approach. The results revealed four distinct profiles of employees. At the profile level, results reveal that the joint implementation of a similar level of LEB in a complementary manner relates to employees’ levels of behavioral empowerment. However, within each profile, a lack of coherence in the levels of implementation of the three LEB resulted in a more complex pattern of associations with employees’ levels of behavioral empowerment. Taken together, these results offer practical guidance to guide supervisors in their utilization of LEB.
Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.
Although interest in organizational learning has grown dramatically in recent years, a general theory of organizational learning has remained elusive. We identify re- newal of the overall enterprise as the underlying phenomenon of interest and organ- izational learning as a principal means to this end. With this perspective we develop a framework for the process of organizational learning, presenting organizational learning as four processes-intuiting, interpreting, integrating, and institutionaliz- ing-linking the individual, group, and organizational levels.
Organizations are widely encouraged to learn from their failures, but it is something most find easier to espouse than to effect. This article synthesizes the authors' wide research in this field to offer a strategy for achieving the objective. Their framework relates techni-cal and social barriers to three key activities e identifying failure, analyzing failure and deliberate experimentation e to develop six recommendations for action. They suggest that these be implemented as an integrated set of practices by leaders who can 'walk the talk' and work to shift the managerial mindset in a way that redefines failure away from its discreditable associations, and view it instead as a critical first step in a journey of discovery and learning.
This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed.KeywordsOrganisational learningLeadership styleContext-sensitive controlComputational modellingSelf-modelling networks
The organizational learning literature recognizes that learning is a multilevel phenomenon that occurs between the individual, team and organizational levels. Existing literature has begun to identify linking mechanisms between these levels, but the research explaining how these mechanisms operate remains scarce. There is a limited understanding of the learning paths and connections between the individual, team and organizational levels. Using a systematic literature review, this paper synthesizes the research on multilevel learning to: (1) classify primary and less researched mechanisms enabling multilevel learning, and (2) explain how and in what direction these mechanisms operate to link the levels. We then propose a framework to summarize our findings. We investigate this phenomenon in both organizational and project‐based contexts due to the unique temporal and structural learning challenges of the latter. Future research directions are proposed for scholars who wish to further contribute to this important and growing field.
Organizational learning has been shown to affect performance. This study offers a fine-grained view regarding different types of learning opportunities. Specifically, opportunities to learn from mistakes are examined. Using three separate samples, we first establish statistically reliable and unidimensional measures of both organizational learning and mistake tolerance. Second, we empirically demonstrate the mediating role of organizational learning on the mistake tolerance–performance relationship. Our results offer findings that will generalize to other organizational contexts. We conclude with a dialogue suggesting prescriptive advice for managers and provide a discussion of how learning from mistakes can be an important catalyst in organizational change. Using specific items from our survey, we stress that managers need to make a conscious effort to communicate to employees the value in learning from mistakes as an important part of improving and changing existing organizational practices.
The concept of organizational culture has received increasing attention in recent years both from academics and practitioners. This article presents the author's view of how culture should be defined and analyzed if it is to be of use in the field of organizational psychology. Other concepts are reviewed, a brief history is provided, and case materials are presented to illustrate how to analyze culture and how to think about culture change.
The concept of organizational culture has received increasing attention in recent years both from academics and practitioners. This article presents the author's view of how culture should be defined and analyzed if it is to be of use in the field of organizational psychology. Other concepts are reviewed, a brief history is provided, and case materials are presented to illustrate how to analyze culture and how to think about culture change. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This is the Center for Effective Organizationsâs (CEO) fourth national study of the human resources (HR) function in large corporations. It is the only long-term national study of this important function. Like the previous studies, it focuses on measuring whether the HR function is changing and on gauging its effectiveness. The study focuses particularly on whether the HR function is changing to become an effective strategic partner. It also analyzes how organizations can more effectively manage their human capital. The present study compares data from earlier studies to data collected in 2004. The results show some important changes and indicate what HR needs to do to be effective. Practices are identified that enable HR functions to be high value-added strategic partners.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1993. Title as it appears in the June 1993 MIT Graduate List: A framework for linking individual and organizational learning. Includes bibliographical references. by Daniel H. Kim. Ph.D.
Perspective-taking is a stepping stone to human empathy. When empathizing with another individual, one can imagine how the other perceives the situation and feels as a result. To what extent does imagining the other differs from imagining oneself in similar painful situations? In this functional magnetic resonance imaging experiment, participants were shown pictures of people with their hands or feet in painful or non-painful situations and instructed to imagine and rate the level of pain perceived from different perspectives. Both the Self's and the Other's perspectives were associated with activation in the neural network involved in pain processing, including the parietal operculum, anterior cingulate cortex (ACC; BA32) and anterior insula. However, the Self-perspective yielded higher pain ratings and involved the pain matrix more extensively in the secondary somatosensory cortex, the ACC (BA 24a'/24b'), and the insula proper. Adopting the perspective of the Other was associated with specific increase in the posterior cingulate/precuneus and the right temporo-parietal junction. These results show the similarities between Self- and Other-pain representation, but most interestingly they also highlight some distinctiveness between these two representations, which is a crucial aspect of human empathy. It may be what allows us to distinguish empathic responses to others versus our own personal distress. These findings are consistent with the view that empathy does not involve a complete Self-Other merging.
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