Collaboration Matrix Research Articles

Roadmap for National Adoption of Blockchain Technology Towards Securing the Food System of Qatar

The national adoption of any technology involves the tight cooperation between the government and the key players involved in the sector of significance. This study highlights a road map toward blockchain technology (BCT) adoption and how it can catalyze better collaboration between the national authorities who play a vital role in securing food systems. The study discusses novel organizational and management concepts to blockchain-based digital governance and lays the foundation for future research. The methodology involves a combination of a systematic review and field research with officials of Qatar’s Food Security Program. As a result of analysing the case of Qatar, this paper analyzes the key features of both the BCT and the national food security goals of Qatar and investigates the technology–strategy fit. The outcomes include a blockchain collaboration matrix, “Technology Adoption and Stakeholder Effect” Matrix, and mapping the national strategies of Qatar Food Security to the capabilities of blockchain technology. Finally, this work concludes by providing concrete suggestions to help facilitate blockchain adoption within the national IT infrastructure, for better traceability and transparency in the food system.

COLLABORATION IN CREATIVE INDUSTRIES – FROM CREATIVE INDIVIDUALS AND INTERMEDIARIES TO NETWORKS

Collaboration is the core of creative work, and it can help small and medium-sized enterprises, as well as individual creative professionals, develop creative products that would not be possible if these parties were working separately. The aim of the paper is to reveal benefits of collaboration in creative industries from theoretical and practical perspectives. The paper analyses the concept of collaboration in creative industries and provides a theoretical overview of collaboration models that can take various forms, such as networks, creative intermediaries, and clusters. Also, the author has developed a creative industry collaboration matrix, in­cluding the Ministry of Culture of the Republic of Latvia’s list of creative industries (Architecture, Design, Cinema, Performing arts, Visual arts, Music, Publishing, Television, Radio and interactive media, Advertising, Computer games and Interactive software, Cultural heritage, Cultural education, Recreation, Advertising, Entertainment and other cultural activities) [LR Ministry of Culture 2022], as well as adding two more creative industries, which are identified in other theoretical models of creative industries used in Europe – Fashion and Crafts. By interviewing various experts from creative industries, the intensity and frequency of collaboration in creative industries are compared, and it is evaluated which creative industries are more prone to collaboration.

Keeping to your lane

There has been a sharp rise in the use of electronic mail (email) in public organisations owing to its low cost, ease of use, and speed of transmission of information, among other advantages. This enhanced public service delivery in an era when clients are increasingly calling for public accountability and improved service delivery. While the use of electronic mail seems to be attractive in many respects, there is contention over who is or should be responsible for managing email records in an organisation. Informed by the skills theory, this study holds that acquisition of requisite skills and competencies is a necessary condition for excellence in job performance, productivity, and service delivery. This mixed methods research approach used a convergent research design where 240 records, administration, and information and communication technology officers from 12 government ministries completed questionnaires while 10 officers were interviewed. The interviewees comprised seven National Archives of Zimbabwe archivists, the director of the National Archives of Zimbabwe, one administration director, and one information and communication technology director from Zimbabwe’s central government. Data were further solicited using personal observation and document reviews. Ethical considerations such as seeking consent from participants, obtaining authorisation from organisations, and anonymising participants were observed in the study. The study established that information and communication technology officers were mostly entrusted with managing email records ahead of records officers, an arrangement which the study regards as rather misplaced. A collaboration matrix was proposed where records officers work as email records managers while information and communication technology officers work as email system enablers, thus emphasising the importance of each “keeping to their lane.”

HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features.

Social reviews are indispensable resources for modern consumers' decision making. To influence the reviews, for financial gains, some companies may choose to pay groups of fraudsters rather than individuals to demote or promote products and services. This is because consumers are more likely to be misled by a large amount of similar reviews, produced by a group of fraudsters. Semantic relation such as content similarity (CS) and polarity similarity is an important factor characterizing solicited group frauds. Recent approaches on fraudster group detection employed handcrafted features of group behaviors that failed to capture the semantic relation of review text from the reviewers. In this article, we propose the first neural approach, HIN-RNN, a heterogeneous information network (HIN) compatible recurrent neural network (RNN) for fraudster group detection that makes use of semantic similarity and requires no handcrafted features. The HIN-RNN provides a unifying architecture for representation learning of each reviewer, with the initial vector as the sum of word embeddings (SoWEs) of all review text written by the same reviewer, concatenated by the ratio of negative reviews. Given a co-review network representing reviewers who have reviewed the same items with similar ratings and the reviewers' vector representation, a collaboration matrix is captured through the HIN-RNN training. The proposed approach is demonstrated to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively) datasets.

Establishing a lesson study collaboration matrix

PurposeLesson study (LS) is a collaborative form of action research. Collaboration is central to LS methodology, therefore exploring and expanding the understanding of the collaborative features that occur in LS is a priority. This paper explores the features of collaboration in existing publications on LS to consider if, as Quaresma (2020) notes, collaboration is simplistically referred to within LS research.Design/methodology/approachUtilising a qualitative review of LS literature to explore LS collaboration through Mynott's (2019) outcome model and Huxham and Vangen's (2005) theory of collaborative advantage and inertia. 396 publications using “lesson study” and “collaboration” as key words were considered and reviewed, with 26 articles further analysed and coded, generating a collaborative feature matrix.FindingsWhile collaboration in LS is referred to generically in the articles analysed, the authors found examples where collaboration is considered at a meta, meso and micro level (Lemon and Salmons, 2021), and a balance between collaborative advantage and inertia. However, only a small proportion of LS publications discuss collaboration in depth and, while the matrix will support future research, more focus needs to be given to how collaboration functions within LS.Originality/valueThrough answering Robutti et al.'s (2016) question about what can be learnt from the existing LS research studies on collaboration, this paper builds on Mynott's (2019) outcome model by providing a detailed matrix of collaborative features that can be found in LS work. This matrix has applications beyond the paper for use by facilitators, leaders of LS, and researchers to explore their LS collaborations through improved understanding of collaboration.

Improving health through multisectoral collaboration: enablers and barriers.

Multisectoral partnerships (MPs) are increasingly viewed as an excellent strategy for promoting population health, although the Canadian evidence on MPs remains scant. The objective of this research was to identify enablers and barriers to multisectoral collaboration across three MPs (focused on food systems, urban development, and active transportation) in a Canadian urban centre. This study is part of a pan-Canadian research program-MUSE (Multisectoral Urban Systems for health and Equity in Canadian cities). A qualitative case study methodology was used to explore enablers and barriers to collaboration among three MPs in a mid-sized Canadian city. Key strategy documents of the MPs and 13 in-depth, semi-structured interviews were conducted with stakeholders from each MP. Interview data were transcribed and subjected to thematic analysis using NVivo 12 software, with rigour ensured through member checking. Enablers to collaboration included agenda and goal alignment among partners, quality of relationships in MPs, and external enabling factors. Barriers to multisectoral collaboration included agenda and priority differences, factors related to partnership structure, constitution and processes, and external barriers. Based on these factors, we developed a multisectoral collaboration matrix that dichotomizes enablers and barriers into intrinsic/internal and extrinsic/external to increase understanding of health-promoting MPs in Canada. Various enablers and barriers promote or inhibit multisectoral partnerships. By casting these factors into a matrix, members of ongoing or emerging MPs could take advantage of the factors that promote their work and are in their control (intrinsic enablers) or outside their control (extrinsic enablers) while working to overcome challenges presented by collaboration barriers.

Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for collaboration design is generally non-convex. We establish correspondence between the sparse collaboration matrix and the non-sparse vector consisting of the nonzero elements of the collaboration matrix. Then, we reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). The compression design problem is solved using the same methodology. We propose two versions of compression design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We show that the objective function for collaboration design can be non-convex depending on the network topology. We reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). Moreover, the compression design problem is also formulated as a QCQP. We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently. It is noted that the design of decentralized compression strategy is a non-convex problem. We obtain a near-optimal solution by using the bisection method. In contrast to the one-shot estimator, our proposed algorithm is capable of handling dynamic system parameters such as channel gains and energy constraints. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Spatial Structure, Hierarchy and Formation Mechanisms of Scientific Collaboration Networks: Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative, and remarkable progress has been made since 2013. Taking the 65 countries along the Belt and Road (BRI countries) as the research areas and using collaborated Web of Science (WOS) core collection papers to construct an international scientific collaboration matrix, the paper explores the spatial structure, hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road. The results show that: 1) Beyond the Belt and Road regions (BRI regions), Central & Eastern Europe, China and West Asia & North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions. China has the dominant role over other BRI countries in generating scientific links. The overall spatial structure has changed to a skeleton structure consisting of many dense regions, such as Europe, North America, East Asia and Oceania. 2) Within the Belt and Road regions, Central & Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries. The spatial structure of scientific collaboration networks has transformed from the ‘dual core’ composed of China and the Central & Eastern Europe region, to the ‘multi-polarization’ composed of ‘one zone and multi-points’. 3) The hierarchical structure of scientific collaboration networks presents a typical ‘core-periphery’ structure, and changes from ‘single core’ to ‘double cores’. 4) Among the formation mechanisms of scientific collaboration networks, scientific research strength and social proximity play the most important roles, while geographical distance gradually weakens the hindrance to scientific collaboration.

Composite multi-objective optimization on a new collaborative vehicle routing problem with shared carriers and depots

The collaborative vehicle routing problem for logistic optimization is a fundamental part of the sustainable supply chain, and has increasingly been of concern in recent years. This study proposes a novel logistics collaboration model to address the collaborative vehicle routing problem that involves shared carriers and depots (CVRP-SCD). This is inspired by the fact that a depot often has orders for multiple carriers, and a carrier often delivers orders to multiple depots. This problem involves decreasing transportation distances and improving capacity utilization by extending supply-side unilateral logistics collaborations to simultaneously consider collaborations between supply- and demand-sides. Further, the proposed CVRP-SCD model uses a composite objective that is a weighted sum of four objectives — including quality, reliability, cost, and time — to accurately evaluate and analyze the efficiency improvement. An extended variable neighborhood search algorithm is presented based on three matrices, including the carrier collaboration matrix, depot collaboration matrix, and transportation sequence matrix. This algorithm aims to address the trade-offs among multiple objectives for the CVRP-SCD model by incorporating new operators based on specific features involved in the problem. Simulation experiments are performed on two sets of instances to evaluate the proposed algorithm’s effectiveness as well as the savings produced from the proposed model. Further, a comparison experiment between different objective optimization also verifies the rationality of the proposed composite multi-objective optimization.

Development of collaboration matrix using co-occurrence and combinatoric system as scientometrics analysis

Purpose – The purpose of this study is to propose a new method for revealing the collaboration frequencies among the author’s name and the author’s affiliation through various analysis and visualization methods.Design/methodology/approach – This study takes metadata of the author’s name, author’s affiliate, and keyword from National Scientific Repository (RIN). Each article will be extracted accord to the entity, the author’s name is separated into first author, second author, third author, etc. The affiliation is also separated according to the author’s affiliation using machine learning algorithms.Findings – The implementation of the collaboration matrix form of visualization can be used effectively in looking at the number of collaborative frequencies of scientific works among author’s names and author’s affiliations. Presentations and calculations of keywords that often appear in each scientific paper resulting from collaboration between name pairs can be used to identify the contents of the study of scientific articles as a whole, see the topics discussed and find out the trends and interests of research.Originality/value – This study applied collaboration matrix using cooccurrence and combinatoric methods. The result of the analysis is the number of collaboration among the authors and the highest number of collaborations will be placed on the top rank.

Mapping the efficiency of international scientific collaboration between cities worldwide

International scientific collaboration, a fundamental phenomenon of science, has been studied from several perspectives for decades. In the spatial aspect of science, cities have generally been considered by their publication output or by their citation impact. Only a minority of scientometric studies focus on exploring collaboration patterns of cities. In this visualisation, we go beyond the well-known approaches and map international scientific collaboration patterns of the most prominent science hubs considering both the quantity and the impact of papers produced in the collaboration. The analysis involves 245 cities and the collaboration matrix contains a total number of 7718 international collaboration links. Results show that US–Europe co-publication links are more efficient in terms of producing highly cited papers than those international links that Asian cities have built in scientific collaboration.

Collaboration Matrix Factorization on Rate and Review for Recommendation

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

Water research and collaboration in the Southern African Development Community

This article investigates water-related research in the Southern African Development Community. Water issues are part of the region’s science and technology priorities as 4countries receive less rain than the global average of 860 mm/yr – Botswana with 400 mm/yr, Namibia with 254 mm/yr, South Africa with 497 mm/yr and Zimbabwe with 652 mm/yr. Furthermore, the international literature indicates that joint or internationally coordinated research has the potential to improve the scientific–technical quality of international agreements, prevent conflict and shape the way for appropriate management of the shared resources. Scientometric analysis using the Web of Science database is employed in order to identify the state of water research and collaboration in the SADC region. The Web of Science indexes a defined set of journals worldwide and the South African Government provides incentives/subsidies for publications indexed by this database. The results show that South Africa is the main producer (80%) of research publications in the region. Similarly, in the field of water research South Africa produces 75% of the region’s research. The SADC collaboration matrix in water-related research reveals that there is minimal, if any, collaborative research on the topic. Some seed-level research exists between South Africa, Zimbabwe and Namibia. The main funders of research are the South African National Research Foundation (NRF) (acknowledged in 180 publications), the Bill & Melinda Gates Foundation (72 publications), the National Institutes of Health (64 publications) and the Wellcome Trust (60 publications). Policy implications are discussed (e.g. the establishment of SADC Common Water Research Area; research support for the region, etc.).

A Macro and Micro-Level Evaluation of Stakeholders’ Collaboration for Sustainable City Logistics Operations

City logistics involves movement of city goods in urban areas respecting the municipal and administrative guidelines. The key goals of city logistics planning are maximizing vehicle movement and utilization, while minimising vehicle emissions and traffic congestion. Collaboration is vital to managing city logistics operations efficiently. Collaboration can take place in the form of goods consolidation, sharing of resources, information sharing, and so on. Two categories of models are proposed to evaluate these collaboration strategies. At the macro level, we present the collaboration matrix model; and at the micro level, we present the operational level model. The macro-level model focuses on the strategic decision making process necessary for stakeholders’ collaboration given the socio-cultural characteristics, economic, and environmental constraint factors, while the micro-level model applies the collaboration decision-making criteria derived from the macro-level analytic result to improve the activities of the city logistics operators. Results of the computational testing of our methodology on vehicle selection, goods to vehicle assignment, goods distribution and environmental impact assessment are discussed, showing that the collaboration strategies of stakeholders, if optimized, can improve city logistics operations. The proposed work is novel and has strong practical applicability for logistics planners and decision makers in planning right collaboration strategies for sustainable city logistics operations.

Adaptive Fuzzy Collaborative Task Assignment for Heterogeneous Multirobot Systems

Heterogeneity in robot model mix is advantageous in emerging and future robotic applications. For instance, in a fully automated industrial package loading and unloading scenario, a variety uncertain types of packages need to be continuously sorted and loaded onto designated trucks. To handle the variation of tasks, multiple types of robots, or heterogeneous robots, are designed in the system. However, in such a collaborative system consisting of heterogeneous robots, ineffective task assignments often lead to bad collaboration and thus poor efficiency. To improve the robot collaboration, we herein define the collaborative task assignment problem and develop a fuzzy collaborative intelligence based algorithm to optimize the assignment plans. Specifically, the collaboration type, the collaboration matrix, and the assignment matrix are defined and the new algorithm for adaptive fuzzy collaborative task assignment relies on intuitionistic fuzzy set theory. An unsorted package loading and unloading tasks by heterogeneous robots is used as a case example to validate the new algorithm. Experiments indicate with statistical significance that the new approach shortens total completion time by 21%, reduces total energy consumption by 23%, and increases loading accuracy by 31%, compared with the traditional static task assignment method commonly practiced. The developed approach can be applied to different emerging collaborative systems to improve systems’ collaborative intelligence.

Collaborative Customer Relationship Management-Co-Creation and Collaboration through Online Communities

There are four trends reshaping the world of business-technological advances and the speed with which new technologies are created and copied, the loss of geographic advantage resulting from globalization, the shake-up of traditionally stable industries as a result of deregulation and the rising power of the consumer and their ability to get what they want, when they want it, from whomever they want. With this in mind, the collaboration experience becomes one of the greatest competitive aspects for a business’s survival. This has made companies realize the significance of the two levers of Customer Value Management –Co-creation and Collaboration. Further, with the internet having built an open network where information can flow freely, innovation, entrepreneurship and democracy are fast thriving over the world. Self directed co-creation is a wide range of consumer activities that amount to consumer-side production of value. Thus individuals, online communities have become a considerable prolific force in e- commerce. This research paper details the usage of online communities as tool for co-creation and collaboration The research study further classifies the online communities of organizations on the basis of their scores on co-creation and collaboration and further goes on to develop a Community Co-creation and Collaboration Matrix (CCCM). The online communities which depict a high score on co-creation and collaboration are the most desirable from the organization’s perspective. They facilitate a dynamic environment in terms of reciprocity and optimal level of customization for a win-win organization-community member relationship. Co-creation and collaboration is the strategic outcome of collaborative customer relationship management. Thus online communities can be used to achieve the two important functions of Customer Relationship Management i.e. co-creation and collaboration.

Visualizing cooperation networks of elite institutions in India

In this paper we have analyzed the pattern of cooperation links among fifty most prolific institutions (hereafter called “elite institutions”) in India. The network of relationships among these institutions is sparse and more than two thirds of the cells in the collaboration matrix are empty. The network is centralized, but no institution dominates the network. It is only a set of few institutions that dominate the network. We have constructed a measure (Bonacich eigenvector centrality index) to assess the position of each institution in the network. Barring a few notable exceptions, scientific size of an institution is directly related to its position in the network. We have graphically depicted the network of relationships among these institutions above a certain threshold of cooperation strength. The network incorporating 50 nodes and 171 arcs provides a synoptic view of bilateral relations among the institutions, but it is quite complex. We have therefore developed a block model of the network to assess the macro level features of cooperation links among the institutions. The block model indicates the isolation and marginality of certain clusters (or blocks) of institutions.

Factor Analysis of the Stanford-Binet Fourth Edition for Ages 2 Years Through 23 Years

The factor structure of the Standford-Binet Fourth Edition was investigated using the collaboration matrix from the standardization sample. The results revealed the large first factor that resemble...