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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.

This paper aims to identify the interaction of different intervention strategies implemented in Malaysia towards flattening the curve of COVID-19 cases. Since the outbreak of COVID-19, many approaches were adopted and implemented by the Malaysian government. Some strategies gained quick wins but with negative unintended consequences after execution, whereas other strategies were slow to take effect. Learning from the previous strategies is pivotal to avoid repeating mistakes.

This paper presents the cause, effect of and connection among the implemented COVID-19 intervention strategies using systems thinking through the development of a causal loop diagram. It enables the visualisation of how each implemented strategy interacted with each other and collectively decreased or increased the spread of COVID-19.

The results of this study suggested that it is not only essential to control the spread of COVID-19, but also to prevent the transmission of the virus. The Malaysian experience has demonstrated that both control and preventive strategies need to be in a state of equilibrium. Focusing only on one spectrum will throw off the balance, leaving COVID-19 infection to escalate rapidly.

The developed feedback loops provided policy makers with the understanding of the merits, pitfalls and dynamics of prior implemented intervention strategies before devising other effective intervention strategies to defuse the spread of COVID-19 and prepare the nation for recovery.

Primary teachers play a vital role in fostering pupils' successful futures. Therefore, gaining knowledge of primary teacher students' learning processes, including the achievement of information-seeking skills, is crucial. The aim of this paper is to understand better the interplay between cognitive appraisals and emotions in the constructivist process of learning and achieving information-seeking skills.

In-depth semi-structured interviews were conducted with six Swedish primary teacher students. The analysis of qualitative data was deductive and theory-driven, guided by Kuhlthau's information search process model, Scherer's semantic space of emotions and Pekrun's control-value theory of achievement emotions.

Anger/frustration, enjoyment and boredom were identified as activity emotions and anxiety, hopelessness and hope as prospective outcome emotions. The retrospective outcome emotions found were pride, joy, gratitude, surprise and relief. The appraisals eliciting the achievement emotions were the control appraisals uncertainty/certainty (activity and prospective outcome) and oneself/other (retrospective), and value appraisals negative/positive intrinsic motivation (activity) and failure/success (prospective and retrospective). The interplay between appraisals and emotions was complex and dynamic. The processes were individually unique, non-linear and iterative, and the appraisals did not always elicit emotions.

The study has theoretical and methodological implications for information behaviour research in its application of appraisal theories and the Geneva affect label coder. In addition, it has practical implications for academic librarians teaching information-seeking skills.

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.

Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial resources. This study aims to create an understanding of how NPOs involved in textile reuse as a revenue-generating programme manage their reverse supply chains (RSC).

The research involves an embedded single-case study of NPOs in Finland involved in post-use textile collection. The main data sources are semi-structured interviews and participant observations.

This study is inspired by the microfoundations movement and identifies the underlying microfoundations of the NPOs’ capabilities for managing RSC for textile reuse. The study contributes to the literature by demonstrating NPOs’ lower-level, granular practices and their adaptations for achieving quality outcomes in textile reuse.

The findings have context sensitivity and apply to the NPOs which operate in a context similar to Finland, such as in other Nordic countries.

This study continues the discussion on the adoption of “business-like” practices in the NPOs’ pursuit of additional revenue streams to finance humanitarian work. The findings of this study can also be transferred to the growing area of domestic textile circularity.

Using the case of NPOs in textile reuse, the study illustrates how RSC management can serve a social, non-profit cause and transform unwanted textile products into a source of fundraising for humanitarian work.

This enriches the understanding of NPOs’ practices within the scope of revenue-generating programmes by examining one of them – textile reuse through charity shops from an RSC perspective.

This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines.

Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model.

In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft.

This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics.

A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC.

This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers’ understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses.

Digitalising cities requires new urban governance processes that account for rapidly changing environments and technological advances. In this context, agile development methods have become valuable, if not necessary. However, agile development contradicts public administration practices of risk aversion and long-term planning. The purpose of this study is to discuss practical avenues for navigating these two contradictions by adapting agile development to the needs of public sector organisations.

The authors review the collaborative elaboration of Dresden’s smart city strategy as a critical case study. Dresden’s smart city strategy was developed using agile development and quadruple-helix innovation. The year-long co-creation process involved stakeholders from various groups to conceive an integrated and sustainable vision for digitalisation-based urban development.

Despite the apparent contradictions, this study finds that key aspects of agile development are feasible for public sector innovation. Firstly, risks can be strategically managed and distributed among administration and non-administration stakeholders. Secondly, while delivering value through short iterative loops, adherence to formal processes remains possible. Informal feedback cycles can be harmoniously combined with official statements, allowing iterative progress.

The empirical material is based on a single case study and thus risks overemphasising the general applicability of the proposed methods.

This paper outlines practical steps to greater agility for public administration engaged in digitalising cities. The paper conceptualises a forward and lateral momentum for the agile development of a smart city strategy that aims to reconcile formal policymaking processes with short-term loops and risk aversion with experimental value creation. This approach balanced risks, created value and enhanced the strategy‘s alignment with strategic frameworks, ultimately promoting innovation in the public sector.

This paper proposes a novel, empirically grounded conceptualisation of implementing agile methods that explicitly recognises the peculiarities of public administrations. It conceptualises the orchestrated and pragmatic use of specific agile development methods to advance the digitalisation of cities.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four balanced scorecard (BSC) perspectives in operations management (OM) contexts and (2) understand how performance drivers and outcome measures (and substantiated perspectives) are related.

We undertake a systematic literature review of the BSC literature in OM journals. From the final sample of 40 articles, performance drivers and outcome measures have been identified, and the relationships amongst them have been synthesised according to the system dynamics approach.

Findings show (1) the most relevant performance drivers and outcome measures within each BSC perspective, (2) their relationships, (3) how the perspectives are linked through the performance drivers and outcome measures and (4) how the different measures relate systemically. Accordingly, four causal loops amongst identified measures have been built, which – jointly considered – allowed for the creation of a dynamic strategy map for OM.

This study is the first one that provides a comprehensive and holistic view of how the different performance drivers and outcome measures within and between the four BSC perspectives in OM relate systemically, increasing the knowledge and understanding of scholars and practitioners.

This study aims to determine the factors and dynamic systems behaviour of essential medicine stockout in public health-care supply chains. The authors examine the constraints and effects of mental models on medicine stockout to develop a dynamic theory of medicine availability towards saving patients’ lives.

This study uses a mixed-method approach. Starting with a survey method, followed by in-depth interviews with stakeholders within five health-care supply chains to determine the dynamic feedback leading to stockout and conclude by developing a network mental model for medicines availability.

The authors identified five constraints and developed five case mental models. The authors develop a dynamic theory of medicine availability across cases and identify feedback loops and variables leading to medicine availability.

The need to include mental models of stakeholders like manufacturers and distributors of medicines to understand the system completely. Group surveys are prone to power dynamics and bias from group thinking. This survey’s quantitative output could minimize the bias.

This study uniquely uses a mixed-method of survey method and in-depth interviews of experts to assess the essential medicine stockout in Nigeria. To improve medicine availability, the authors develop a dynamic network mental model to understand the system structure, feedback and behaviour driving stockouts. This research will benefit public policymakers and hospital managers in designing policies that reduce medicine stockout.