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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 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.

The purpose of this paper is to compare the effectiveness of the various text mining techniques that can be used to classify maintenance work-order records into their respective failure modes, focussing on the choice of algorithm and preprocessing transforms. Three algorithms are evaluated, namely Bernoulli Naïve Bayes, multinomial Naïve Bayes and support vector machines.

The paper has both a theoretical and experimental component. In the literature review, the various algorithms and preprocessing techniques used in text classification is considered from three perspectives: the domain-specific maintenance literature, the broader short-form literature and the general text classification literature. The experimental component consists of a 5 × 2 nested cross-validation with an inner optimisation loop performed using a randomised search procedure.

From the literature review, the aspects most affected by short document length are identified as the feature representation scheme, higher-order n-grams, document length normalisation, stemming, stop-word removal and algorithm selection. However, from the experimental analysis, the selection of preprocessing transforms seemed more dependent on the particular algorithm than on short document length. Multinomial Naïve Bayes performs marginally better than the other algorithms, but overall, the performances of the optimised models are comparable.

This work highlights the importance of model optimisation, including the selection of preprocessing transforms. Not only did the optimisation improve the performance of all the algorithms substantially, but it also affects model comparisons, with multinomial Naïve Bayes going from the worst to the best performing algorithm.

This study aims to set out to identify and evaluate potential obstacles to successfully implementing lean construction (LC) as a result.

Several indicators were recognized as major obstacles following an exhaustive assessment of the literature and a multicriteria decision analysis based on the analytic hierarchy process (AHP) of information obtained from a questionnaire survey that was directed to practitioners in the Indian construction industry.

The results of this AHP model suggest that “Managerial” and “Inadequate resources” categories with a priority weight of “0.361” and “0.309” have the highest levels of influence, respectively, while “Inadequate knowledge” and “just in time (JIT)” categories with a priority weight of “0.053” and “0.034” have the lowest levels of influence, respectively.

Construction companies can use the study’s findings as a guide to determine whether they are ready to embrace LC, learn more about the components needed for implementation or investigate any challenges that may arise. These businesses can then create plans to promote the adoption and application of the lean philosophy.

The Indian construction industry may see great success with LC management initiatives. LC concepts have been adopted by many nations, but during the past 20 years, there has only appeared to be a limited amount of lean implementation in the Indian construction industry. It seems that several structural and cultural barriers are preventing its effective implementation. Organizations will not be able to determine what improvement efforts are required, where these efforts should be directed or which initiatives could provide the best outcomes if they are unaware of the elements that influence the effective implementation of LC.

The purpose of this paper is to develop an organized structure for damage detection of a cracked cantilever beam using finite element method and experimental method technique.

Due to presence of cracks the dynamic characteristics of structure change. The change in dynamic behavior has been used as one of the criteria of fault diagnosis for structures. Major characteristics of the structure which undergo change due to presence of crack are: natural frequencies, the amplitude responses due to vibration and the mode shapes. Therefore, an attempt has been made to formulate a smart technique for minimizing the amplitude of vibration for crack cantilever beam structures. In the analysis both single and double cracks are taken into account.

The results of the active vibration control experiments proved that piezoelectric sensor/actuator pair is an effective sensor and actuator configuration for active vibration control to reduce the amplitude of vibration for closed-loop system.

It is necessary that structures must safely work during its service life, but damages initiate a breakdown period on the structures which directly affect the industrial growth. It is a recognized fact that dynamic behavior of structures changes due to presence of crack. It has been observed that the presence of cracks in structures or in machine members leads to operational problem as well as premature failure.

The purpose of this work is (1) to supply a framework of actors in sport sponsorship and articulate the service relationships that support these partnerships and (2) to propose research questions in this space that are unaddressed and forward-looking.

Sponsorship is part of a complex network of actors and service relationships found in sport. The sports team, activity, or event is a sport property, often with long-term and dynamic service relationships. The authors consider how a sponsor's relationship with the sport property intersects with organizing bodies, venues, communities and society. The authors identify clusters of actors that interact with and influence other clusters (e.g. governing bodies, media, host community and venue/teams/fans) within an ecosystem, paying special attention to aspects of co-creation and co-destruction and the feedback loops that cause them.

Through this analysis, the authors identify areas of needed research at the intersection of sport sponsorship and service. The model synthesizes the literature from service-dominant logic, sports, sponsorship, systems thinking and co-creation/co-destruction research areas. Using the model and relevant cases, the authors can better understand the complexities of sport service relationships and advance research at the intersection of sport sponsorship and service.

This is the first sport sponsorship service ecosystem model. It is also the first integration of systems thinking with constructs in sport sponsorship and services.

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.

Circular economy (CE) disclosure is becoming urgent for firms, but an accepted and recognized approach to address it is still missing, especially at small and medium enterprises (SMEs) level. This study aims to contribute to this issue by exploring the potential of the adoption of Global Reporting Initiative (GRI) Standards as a standardized approach for CE disclosure. The paper proposes a framework that identifies the existence of specific relationships between the topics included in GRI 300 Standards and CE strategies, which can be considered as managerial guidelines for CE strategy disclosure by companies.

The paper uses an explorative research methodology based on the content analysis of secondary data taken from the sustainability reports of the SMEs listed in STAR segment of Borsa Italiana.

The analysis shows that GRI Standards are currently adopted to disclose the CE strategies by Italian SMEs across different economic sectors including construction, food, automotive, retail, personal and household goods, industrial goods and services, electronics, media and technology. In particular, GRI 301 is used for the disclosure about Recycling, Reuse, Reverse logistics, Industrial Symbiosis, Eco-Design, Product as a Service and Refurbishing/Reconditioning/Remanufacturing. GRI 302 is suited to disclose CE strategies about Eco-Design, Renewable Energy and Industrial Symbiosis. GRI 303 can be useful to disclose about Reuse and Industrial Symbiosis. GRI 305 is appropriate for Eco-Design, Renewable Energy, Product as a Service, Industrial Symbiosis and Recycling. Finally, GRI 306 is useful to communicate the CE strategies of Recycling, Reuse, Reverse Logistics, Eco-Design, Industrial Symbiosis, Maintenance/Repair, Refurbishing/Reconditioning/Remanufacturing.

The paper includes implications concerning how to report the implementation of a wide range of CE practices, mainly using GRI 301, 302, 303, 305 and 306. These implications are mainly addressed to SMEs in different economic sectors.

The use of the proposed framework, by improving the transparency and communication of the CE strategies used by companies, may contribute to accelerate the consumer awareness on CE practices fostering the CE transition, especially of SMEs, with a positive effect on society and environment.

This study extends the literature on CE by developing one among the few standardized approaches using the GRI Standards for the disclosure of CE strategies, a topic mainly investigated in the literature with reference to large companies in a few industries. The framework is also useful to explore the implementation of CE strategies across SMEs in different industrial sectors.

Within a Europe highly concerned about becoming a zero net greenhouse gas emission continent, and where the circular economy is strongly promoted as a proper lever in this sense, the present paper aims to raise awareness regarding best practices towards a “green” food retail sector in Romania. In a more specific regard, the “green” practices implemented by the main food retailers acting on the Romanian market are analysed, focusing on what these practices are, how they are measured and how transparent they are.

The paper is based on desk research consisting in the identification and content analysis of the “green”-related information outlined on the food retailers' websites and in their published sustainability reports.

Food retailers are concerned about becoming carbon neutral, implementing the “green” measures outlined in the paper through the lenses of waste management, minimising/eradicating plastic, reducing/eliminating food waste, energy efficiency/saving, water consumption efficiency, protecting biodiversity, transportation efficiency and compliance with standards/obtained certifications/adherence to pacts. In addition to actions, food retailers are concerned about their proper, continuous monitorization, tackling key indicators in four directions: overall impact on the environment, impact on resources, waste impact and transportation impact.

The paper provides a novel, exhaustive best practices guide to encourage the transition to a “greener” activity in the Romanian food retail sector, highlighting both action measures and key assessment indicators.

This study aims to create a system dynamics simulation model to forecast the performance of small and medium-sized enterprises (SMEs) if some decision-making is executed to reduce the negative of the coronavirus disease 2019 (COVID-19) pandemic. In particular, this study will focus on SMEs that belong to the furniture industry because the furniture industry is one of the leading industries in Indonesia.

The study develops a system dynamics-based model by using three subsystems, i.e. the “production subsystem,” “demand and revenue subsystem” and “raw material (or wood supply) subsystem.”

The best scenario is the third scenario which increases the capacity to the normal situation and government subsidy during and after the pandemic. This scenario gives the best performance for industry revenue and gross domestic product (GDP). However, for the government, the most significant expenditure occurs in the third scenario. This seems a trade-off for the government whether to save the wooden-based furniture industry by encouraging the industry to continue operating during the pandemic accompanied by high subsidies or limiting the activities of the wooden-based furniture industry to prevent the spread of COVID-19 by providing low subsidies.

First, this study does not try to combine the system dynamics (SD) methodology with the other method or use a multi-methodology since SD has several limitations and the other method may have several advantages compared to SD. Second, the models used in this study do not consider the decline in forest area and quality. Third, the demand for wooden-based furniture is obtained from historical data on domestic and foreign sales and fourth, the model does not include the government budget as a constraint to make any subsidy to help the SMEs.

This study provides essential insights into implementing the policies in the world pandemic situation when SMEs face lockdown policy.

The study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario's effect on SMEs' performance during the pandemic.

This study will enrich the previous study on the impact of the pandemic on SMEs and the dynamic system modeling on SMEs. The previous study discussed the pandemic's impact on SME performance and the impact's analysis in isolation from the dynamic nature of SME owners' decisions or government policy. In this study, the impact generated from the pandemic situation could be different depending on the decision and policies taken by managers from SMEs and the government.