Search results

This paper applies systems thinking modelling to enhance the dynamic understanding of how to nurture an innovative culture in healthcare organisations to develop the innovation system in practice and speed up the innovative work. The model aims to provide a holistic view of a studied healthcare organisation's innovation processes, ranging from managerial values to its manifestation in improved results.

The study is based on empirical material from a healthcare unit that, within a few years, changed from having no innovations to repeatedly generating innovations. The study uses the modelling language of causal loop diagrams (CLDs) in the system dynamics methodology to identify the key important aspects found in the empirical material.

The proposed model, based on the stories of the interviewees, explores the dynamics of inertia when nurturing an innovative culture, identifying delays attributed to the internal change processes and system relationships. These findings underscored the need for perseverance when developing an innovative culture in the entrepreneurial phases.

The approach of using systems thinking to make empirical healthcare research results more tangible through the visual notations of CLDs and mental simulations is believed to support exploring complex phenomena to induce and nurture both individual and organisational learning.

The results from this approach provide deepened analysis and provoke the systems view to explain how the nurturing of the culture can accelerate the innovation processes, which helps practitioners and researchers to further expand their understanding of their healthcare contexts.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

Ranulph Glanville has argued that ambitions of strict control are misplaced in epistemic processes such as learning and designing. Among other reasons, he has presented quantitative arguments for this ethical position. As a part of these arguments, Glanville claimed that strict control even of modest systems transcends the computational limits of our planet. The purpose of this paper is to review the related discourse and to examine the soundness of this claim.

Related literature is reviewed and pertinent lines of reasoning are illustrated and critically examined using examples and straightforward language.

The claim that even modest epistemic processes transcend the computational means of our planet is challenged. The recommendation to assume out-of-control postures in epistemic processes, however, is maintained on ethical rather than on quantitative grounds.

The presented reasoning is limited in as far as it is ultimately based on an ethical standpoint.

This paper summarizes an important cybernetic discourse and dispels the notion therein that epistemic processes necessarily involve computational demands of astronomical proportions. Furthermore, this paper presents a rare discussion of Glanville’s Corollary of Ashby’s Law of Requisite Variety.

This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential “game changers” in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no “silver bullet” for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.

Based on a combination of the authors’ research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research.

Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions.

This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.

Since Weick’s (1993) seminal Mann Gulch paper articulated a collapse of sensemaking, scholars have repeatedly investigated sensemaking downstream of enactment. Motivated by another wildland firefighting tragedy, the tragic loss of 19 firefighters in Arizona in 2013, this study aims to look at enactment itself and reveals that the endogenous creation and re-creation of the wildland fire caused a fatal feedback loop of “trigger traps” leading to perpetual enactment that short-circuited sensemaking. Wildland fires can have unpredictable consequences, which triggers in individual sensemakers a fatal and continuous return to the beginning of the sensemaking process.

This paper’s approach is a case study based on a textual analysis of sources investigating the 2013 Yarnell Hill fire. The authors also carefully compared the Yarnell Hill and Mann Gulch disasters in search of breakdowns in sensemaking that could help us understand why we continue to lose firefighters in the line of duty.

The simultaneously volatile and complex environment at Yarnell illustrates sensemaking antecedents to the study of enactment. The findings suggest ways that organizations – those fighting wildfire or those fighting a global pandemic – can avoid getting trapped in the early stages of enactment and can retain resilience in their sensemaking.

This paper introduces the concept of “trigger traps” to help explain the fatal feedback loop of repeated environmental triggers in the early stages of sensemaking in volatile environments.

The purpose of this study is to identify signs of unnecessary assurance of learning (AoL) complexity and to provide suggestions for simplifying the AoL processes.

While this paper is grounded in the existent AoL literature, the paper also presents several anecdotal observations from the authors’ practical knowledge in designing, leading, maintaining and consulting on AoL systems and processes.

Based on both a conceptual review of AoL literature and the authors’ own experiences, the authors outline 13 specific symptoms of unnecessary AoL complexity, identify potential underlying causes for each symptom and propose practical solutions that can increase the efficiency and effectiveness of dysfunctional AoL systems and processes.

Although this work is grounded in the existent AoL literature, the present paper presents several anecdotal observations from the authors’ experiences. While the intent is to provide guidance that is actionable, it is understood that variability exists within and across schools and programs. Future research is needed to provide a more formal structure for reviewing AoL complexity, efficiency and effectiveness.

While future research is needed to provide a more formal structure for reviewing AoL complexity, efficiency and effectiveness, the intent of this paper is to provide guidance that is actionable with the understanding that variability exists within and across schools and programs.

Society increasingly is demanding accountability from institutions of higher learning, and properly structured AoL programs can provide evidence of institutional effectiveness in preparing students to be productive members of society in their chosen fields of study. Stated succinctly, “although accountability matters, learning still matters most” (Angelo, 1999, n.p.).

Consideration of the 13 symptoms presented here along with other drivers that are unique to each school and program should result in the identification and development of practicable remedies to simplify AoL processes and systems, increase efficiency and effectiveness and improve the documentation of improvements to student learning.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

The purpose of this paper is to introduce the concept of meaning as a relevant but missing link in understanding the building blocks of service innovation informed by service-dominant (S-D) logic. In exploring the role of meaning in service innovation, especially related to new value propositions, resource integration and new value cocreation, the authors suggest using the conceptualization of meaning within human-centered design, which has an established body of knowledge on addressing how actors engage and interact.

The paper builds an actionable conceptual framework that relates meaning to central tenets of service innovation, such as resource integration, value propositions and cocreation of value. It delineates the central building blocks of service innovation and conceptually integrates them with meaning to explain the underlying mechanisms of service innovation related both to its development and adoption.

The findings highlight how and why meaning precedes value creation and directs resource integration. Indicating that meaning is driven by experience of earlier interactions it delineates its relationships with new value formation and positions resource interpretation as a driver of this process.

This paper extends the understanding of service innovation in relation to S-D logic, with meaning as a conceptual link to aspects of S-D logic that claim a phenomenological nature. Meaning contributes to S-D logic by providing an understanding of how beneficiaries form intentions to engage in value creation and resource integration. Additionally, by integrating service and design research domains, this paper suggests possibilities for multidisciplinary contributions in future research.

The purpose of this paper is to challenge how we have traditionally thought about organisations and introduce two frameworks to enable us to understand how change in organisations might be facilitated better.

The paper discusses organisations as complex adaptive systems and uses complexity theory to inform two new frameworks for facilitating organisational learning and change.

In order for organisational learning to occur we need to change our mind-set of how we see organisations and to think of learning not just as individual but also as generative “communicative action” that emerge out of collaborative relationships.

The frameworks proposed are grounded in organisational learning literature and the experience of the author. The proposed agenda for organisational learning has yet to be acted upon and evaluated.

The frameworks can be used to enhance understanding of learning and change in organisations. The agenda for enabling organisational transformation identifies key steps to put the ideas developed in the paper into practice.

The approach advocated for use within organisations is one of empowerment and collaboration rather than top down direction.

The paper introduces new frameworks and a practical agenda to bring about organisational transformation through work-applied learning.

Smallholder value chains are dynamic, changing over time in sudden, unpredictable ways as they adapt to shocks. Understanding these dynamics and adaptation is essential for these chains to remain competitive in turbulent markets. Many guides to value chain development, though they focus welcome attention on snapshots of current structure and performance, pay limited attention to the dynamic forces affecting these chains or to adaptation. The paper aims to discuss these issues.

This paper develops an expanded conceptual framework to understand value chain performance based on the theory of complex adaptive systems. The framework combines seven common properties of complex systems: time, uncertainty, sensitivity to initial conditions, endogenous shocks, sudden change, interacting agents and adaptation.

The authors outline how the framework can be used to ask new research questions and analyze case studies in order to improve our understanding of the development of smallholder value chains and their capacity for adaptation.

The framework highlights the need for greater attention to value chain dynamics.

The framework offers a new perspective on the dynamics of smallholder value chains.