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This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

This study applies D3.js and social network analysis (SNA) to examine the impact of collaboration patterns, research productivity patterns and publication patterns on the Ministry of Education (MOE) evaluation policies across all Management Information Systems (MIS) departments in Taiwan.

This study first retrieved data from the Ministry of Science and Technology of Taiwan (MOST) website from 1982 to 2015, the Journal Citation Reports (JCR) website, the Web of Science (WOS) website and Google Scholar. Then it applied power-law degree distribution, cumulative distribution function, weighted contribution score, exponential weighted moving average and network centrality score to visualize the MIS collaborations and research patterns.

The analysis concluded that most MIS professors focused primarily on SCIE-/SSCI-/TSSCI-/core indexed journals after 2005. Professors from public universities were drawn to collaboration and publishing in high-quality-based journals, while professors from private universities focused more on quantity-based publications. Female professors, by contrast, have a slightly higher single-authorship publication rate in SCIE-/SSCI-/TSSCI-indexed journals than do male professors. Meanwhile, professors in northern Taiwan emphasized quantity-based journal publications, while a focus on quality was more typical in the south. Furthermore, National Cheng Kung University has the most single-authorship or intrauniversity publications in SCIE-/SSCI-/TSSCI-/core journals, and National Sun Yat-Sen University published more SSCI-indexed articles than SCIE-indexed articles. All of these findings show that there is an explicit relation between MOE evaluation policies and MIS faculty members' collaboration/publication strategies.

The above findings explain how MOE evaluation policies affected MIS faculty members' collaboration and publication strategies in Taiwan, and the authors hope that such findings can constitute a resource for understanding and characterizing networking with MIS departments in Taiwan.

The UK Office for Library and Information Networking are engaged in a wide range of work in the area of metadata, in cooperation with various partners. Projects on metadata for Internet resource discovery, interoperability and digital preservation all point to the continuing need for something like traditional library services to organise, access and preserve networked information.

This study aims to investigate the diversification benefits attached to the crypto portfolios when combined with stocks, Forex instruments and commodity assets.

Markowitz diversification techniques have been used to analyze the risk-return tradeoffs of the individual portfolios. Daily prices on cryptocurrencies and the selected asset classes, cover the period before and during the pandemic COVID-19. The portfolio risk of the portfolios was calculated by identical techniques and analyzed with equal criteria.

The results with 270 trails indicate that stocks on average reduce the portfolio risk of crypto portfolios by 36% followed by fiat currency with 30.9% and commodities by 20.8%. Average daily returns stand in line with the standard portfolio theories where riskier portfolios offer higher returns and the other way around.

The authors contribute to the current literature by investigating the portfolio risk attached to the crypto portfolios when stocks, commodities and Forex instruments were added separately. To this end, results inform not only retail investors but also portfolio managers on the asset classes that generate better optimization for crypto portfolios.

The purpose of this study is to solve NP-Hard drone routing problem for the last-mile distribution. This is suitable for the multi-drones parcel delivery for the various items from a warehouse to many locations.

This study conducts as a mission assignment of the single location per flight with the constraint satisfactions such as various payloads in weight, drone speeds, flight times and coverage distances. A genetic algorithm is modified as the concurrent heuristics approach (GCH), which has the knapsack problem dealing initialization, gene elitism (crossover) and gene replacement (mutation). Those proposed operators can reduce the execution time consuming and enhance the routing assignment of multiple drones. The evaluation value of the routing assignment can be calculated from the chromosome/individual representation by applying the proposed concurrent fitness.

This study optimizes the total traveling time to accomplish the distribution. GCH is flexible and can provide a result according to the first-come-first-served, demanded weight or distance priority.

GCH is an alternative option, which differs from conventional vehicle routing researches. Such researches (traveling time optimization) attempt to minimize the total traveling time, distance or the number of vehicles by assuming all vehicles have the same traveling speed; therefore, a specific vehicle assignment to a location is neglected. Moreover, the main drawback is those concepts can lead the repeated selection of best quality vehicles concerning the speed without considering the vehicle fleet size and coverage distance while this study defines the various speeds for the vehicles. Unlike those, the concurrent concept ensures a faster delivery accomplishment by sharing the work load with all participant vehicles concerning to their different capabilities. If the concurrent assignment is applied to the drone delivery effectively, the entire delivery can be accomplished relatively faster than the traveling time optimization.

The relevance and importance of the high technology small firm sector to economies has been discussed by a number of authors and new technology‐based firms (NTBFs) have been viewed as having a very important role to play in job creation and as the seedbed of emerging industries. This paper illustrates the findings from a study involving 20 NTBFs based in the Aberdeen area of Scotland where small firms account for a large proportion of exports from the area. The methodology employed involved in‐depth interviews with owners, directors or senior managers. The sample was comprised of engineering companies, software developers, analytical services, instrumentation specialists and a biotech company. the strategic process was explored within each organisation and the difficulties that these NTBFs have in formulating strategies were identified. Questions were asked about relationships with stakeholders and the impacts of support mechanisms for the organisation in practice. Barriers to growth and development were identified and a model of the most important strategic issues was proposed which NTBFs could relate to. Key policy issues which the NTBFs consider important for their strategic growth included innovation, internationalisation, human resources and collaboration. Policy implications at the organisational, local and national levels are highlighted.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

This study aims to identify and analyse the interactions among drivers of anti-food waste behaviour at the consumer level. By understanding the mutual interactions among the drivers, an effort is made to identify the most driving and most dependent drivers through the total interpretive structural modelling (TISM) approach. Modelling offers inputs to propose focused interventions for reinforcing the identified drivers of anti-food waste consumer behaviour using the theoretical lens of social practices theory.

A proposed model of factors affecting anti-food waste behaviour is arrived at to suggest the most effective anti-food waste behavioural interventions. The factors were identified through an extensive literature search. A hierarchical structure of identified factors has been developed using TISM and MICMAC analysis through expert opinion. Focused marketing strategies towards promoting the identified factors for encouraging anti-food waste behaviour were suggested further.

This study identifies nine drivers based on extensive literature review, brainstorming and expert opinion. The TISM hierarchical model portrays the most important and least important drivers of household anti-food waste behaviour. It establishes that fundamental knowledge and socio-cultural norms are the most critical factors to drive the consumers. Marketers can focus on designing effective interventions to enhance the essential knowledge of the consumers and orient the socio-cultural norms towards anti-food waste behaviour.

This study offers implications for practitioners, policymakers and cause-driven marketing campaigns targeting anti-food waste behaviour. It provides an indicative list of critical factors relevant to household food waste behaviour, which can be used to drive effective marketing campaigns to nudge anti-food waste behaviours.

The proposed food waste behaviour management model was developed through modelling technique (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, and relating them to marketing interventions is a novel effort in the food waste domain.

Discusses the building of a prototype decision‐support system for a container transport company in the light of constraining factors which affect scheduling of trucks and vehicle routeing problems of various kinds. Considers the prototype’s data requirements and describes the information flow analysis, drawing conclusions and offering suggestions, before proceeding to model the problem as a vehicle routeing problem. Describes the development of an heuristic procedure and how it was tested and validated. Summarizes the benefits of the analysis and the prototype’s construction.