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This study examines three significant collaborative journalism projects in the Americas: The Panama Papers, from the United States-based International Consortium of Investigative Journalists (ICIJ); “América Latina, Región de Carteles,” by Colombian-based Connectas; and the first phase of the Brazilian-based project, Comprova, supported by Brazilian Association of Investigative Journalists (Abraji) and First Draft. The work investigates what encompasses collaborative journalism; and explores whether it is a recent phenomenon of the news ecosystem, a consequence of the institutional crisis of journalism, and if it is influenced by a network-based and platformed society. A mixed-method approach is applied in a three-stage analysis: (1) desk research; (2) quantitative content analysis; and (3) qualitative semi-structured in-depth interviews. To gain a broader picture of the organizations and their respective projects, documental and bibliographical research was carried out with a focus on data from press releases, corporate reports, and articles published on the websites of the organizations coordinating the projects. Furthermore, a quantitative content analysis of 10 news articles published by each of these collaboration partnerships was completed. Finally, qualitative semi-structured in-depth interviews were conducted with the directors, managers, and professional journalists’ part of the organizations and project. This study emphasizes the importance of collaborative practices, demonstrates how collaborative practices contribute to a new modus operandi of the news ecosystem; and considers why journalists and media organizations have turned to collaborative journalism as a model of production, circulation, and distribution of journalistic investigations.

Contemporary cities are the subject of new forms of visualization that are not only changing how we see the urban world but how it operates as a social environment. This chapter explores Google's Street View database and the Google Maps platform as sites for the production of distinctive new streams of visual data about cities around the world. I argue that this kind of digital infrastructure presents urban researchers with both new opportunities and new challenges, raising complex questions about the role of visual images in the context of the ongoing transition to a digital, computational, and networked image world.

Design for manufacture and assembly (DfMA) challenges architects in managing diverse knowledge across different professional domains. Little research, if any, has documented DfMA as a knowledge-intensive activity happening in real-life cases. This research aims to investigate DfMA knowledge acquisition, sharing and implementation in real-life practice and develop DfMA knowledge-to-action framework for architects.

It does so by (1) conducting observations in design meetings, documentary analysis and interviews, (2) thematic analysis through knowledge management (KM) perspectives and (3) based on this profound understanding, enriching the prior understanding of design as a double diamond process.

Through the lens of KM, DfMA is not merely a double but multiple diamond process, involving the iteration of making absenting interdisciplinary knowledge available, deciphering tacit knowledge explicit and embedding the knowledge in a design that well considers various criteria, the downstream manufacturing, logistics and assembly in particular. To execute multi-faceted knowledge acquisitions and implementations in DfMA, architects should balance their roles as humble learners, team coordinators, creative leaders and democratic negotiators.

This research reveals the DfMA implementation process, activities and dilemmas from real-world design meetings. The DfMA knowledge-to-action framework developed in this study, along with practice examples and lessons learned, can facilitate architects to play new roles. Future research is recommended to fine-tune the framework by having other stakeholders’ perspectives, refining it with additional cases and developing assistive tools for designers based on the framework.

The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.

The COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.

The importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.

The findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.

While the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

The purpose of this paper is to examine the impact of working capital management (WCM) on profitability under different financial conditions (constraint/unconstraint) and WCM policy (aggressive/conservative). Furthermore, the study investigates the existence of optimal working capital levels under different financial conditions and WCM policy.

Two-step system generalized method of moments and fixed effect models are used to analyze the data collected from Prowess database from 2011 to 2020 for a sample of 1,104 Indian manufacturing companies.

The study finds an inverted U-shaped relationship between working capital and profitability in all financial conditions and working capital policy. This finding advocates the existence of an optimal level of working capital that equates the costs and benefits of holding working capital to maximize the companies’ profitability. However, holding working capital beyond the optimal level negatively affects profitability. Companies under financial constraints with aggressive working capital policies have the lowest optimal cash conversion cycle (CCC). Furthermore, the relationship of working capital with profitability and the optimal CCC varies owing to firm age and industry group.

To the best of the authors’ knowledge, this is the first paper that incorporates the impact of working capital on firm’s performance from both financial constraint (unconstraint) and aggressive (conservative) working capital policy perspectives in the Indian context. Furthermore, this study also contributes in terms of reflecting the effect of firm age and industry in determining the optimum CCC of the firms.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

This paper aims to empirically investigate how knowledge coordination is carried out within and across expert teams in the outsourcing service desk context from the standpoint of the information and communication technologies (ICT) provider.

The authors draw on an embedded case study to unravel the mechanisms used by expert teams to achieve knowledge coordination. Data collection included semi-structured interviews and document analysis over eight months.

Four groups of coordination mechanisms were found according to their nature and role in helping MESA achieve its organizational goals. The authors also identified how this set of mechanisms responds to the task-resource dependency and how it evolves over time to provide reliable ICT services. Furthermore, the analysis of four knowledge coordination dimensions inside each group uncovers the complexity of coordination in the studied context, with the “who” dimension being predominant across the groups. The analysis further reveals that the content (what) and mode of coordination (how) of each group respond to elements in the knowledge coordination process relevant for the different stages of the ICT service delivery process.

The case study addresses how people in outsourced services coordinate their knowledge to manage the interdependencies among the involved organizations and thus, achieving their goals. This study extends previous research that had analyzed the consequences of knowledge coordination for providers and customers by delving into the mechanisms used in this process from the provider’s point of view.