Collaborative Capabilities Research Articles

Organizational Culture and Big Data Analysis as antecedents to Swift Trust in Civil- Military relations during National Disaster Management

This study investigates the inter-relational effects of Big Data Analytics (BDA) and Swift Trust (ST) on Collaborative Performance (CP) during disaster management, framed within the Chaos and Complexity Theory. It focuses on both formal and informal organizational cultures, exploring the mediating role of organizational culture on the ST-CP relationship and the moderating role of BDA on the relationships between ST, organizational culture, and CP. The study highlights the critical importance of BDA capabilities for effective collaboration, providing insights for policymakers and administrative officials. Additionally, it underscores the significance of ST creation for military and civil leadership to achieve desired collaborative outcomes. Disasters, characterized by natural chaos, necessitate swift decision-making and resource management through predictive analysis.

Specialized complementary assets and disruptive innovation: digital capability and ecosystem embeddedness

PurposeWith the widespread penetration of digital technologies, disruptive innovation is not developed by a single firm but is increasingly achieved by an ecosystem. However, limited research has examined the mechanisms involved in achieving disruptive innovation in the context of digitalization and ecosystems. To address this gap, we explore the impact of three dimensions of specialized complementary assets (SCAs) within the innovation ecosystem, human capital SCA (HCSCA), production SCA (PSCA) and marketing SCA (MSCA), on disruptive innovation in core firms through the mediation of digital capability, comprising digital operation capability (DOC) and digital resource collaborative capability (DRCC). Furthermore, innovation ecosystem embeddedness is examined as a moderator between digital capability and disruptive innovation.Design/methodology/approachSurvey data were collected from 234 core firms in China’s high-tech industry. Hierarchical regression, AMOS, and PROCESS tools were used to examine the data.FindingsThe results reveal the following: (1) HCSCA and PSCA positively affect disruptive innovation, while MSCA is negatively correlated with disruptive innovation. (2) Digital capability mediates the relationship between HCSCA and disruptive innovation, as well as PSCA and disruptive innovation. However, it suppresses the negative impact of MSCA on disruptive innovation. (3) Innovation ecosystem embeddedness strengthens the influence of DOC on disruptive innovation, but weakens the influence of DRCC on disruptive innovation.Originality/valueThe findings advance the knowledge of disruptive innovation, SCAs within the innovation ecosystem, digital capability and innovation ecosystem embeddedness. They also provide practical insights into the effective implementation of disruptive innovation.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

We care: A User-Focused Sentiment Analysis Framework for Social Media Interactions

This paper addresses the sentiment analysis of WhatsApp chats, focusing on the challenges and opportunities presented by conversational data. The introduction outlines the prevalence of sentiment analysis in social media platforms and the significance of accurate sentiment categorization. The literature review highlights previous work in sentiment analysis, emphasizing methods for categorizing sentiment polarity and feature selection. The proposed system introduces a comprehensive approach to sentiment analysis tailored to WhatsApp chats, considering the unique characteristics of conversational data. Key features include user-centric inclusion, nuanced extraction of sentiment, context-aware analysis, and effective handling of conversational nuances. The method section details the proposed system's emphasis on real-time sentiment tracking, user-specific sentiment scores, and integration with WhatsApp's API for broader applications. The results section summarizes the positive outcomes of the proposed method, including user-centric inclusion, nuanced extraction, context-aware illustration, and effective content handling. The future scope section discusses potential enhancements such as real-time sentiment alerts, multilingual support, detailed sentiment analysis, improved user feedback systems, personalized sentiment metrics, and collaboration capabilities. The conclusion highlights the significance of the "WeCare" sentiment analysis project in improving our understanding of digital conversations, particularly within the context of WhatsApp chats. The project's commitment to user-centric design, adaptability, and ethical considerations positions it as a dynamic tool for enhancing digital interactions and well-being. Finally, acknowledgments recognize the contributions of the development team and academic mentor.

Deploying fuzzy analytic hierarchy process and sensitivity analysis to identify and prioritize the barriers faced by early‐career social science researchers during research collaborations

AbstractResearch is about an individual's intellectual acumen and rationality, and inter‐researcher collaboration capability magnifies the outcomes. Despite common belief, there exist fundamental asymmetries in the goals, orientations and expectations among the research collaborators. Seldom studied in‐depth and empirically validated, the challenges and barriers faced by early‐career researchers (ECRs) in social sciences are crucial to understand. In this study, we aim to identify, categorize and rank the order of priority of the research collaboration barriers and their sub‐barriers. This analysis highlights which barrier is likely to impact the research collaboration outcome more as compared to other barriers, as seen through the pairwise comparison. We contribute to the literature on research collaborations by providing a much‐needed assessment of the barriers faced by ECRs in social sciences to develop a nuanced understanding of the dynamics within collaboration research. Key barriers identified and explored in this research relate to research design, interpersonal and interprofessional relationships, team‐level dynamics, spatial and temporal factors, cultural differences and institutional variables. The fuzzy AHP tool was used to prioritize and rank the barriers, check the consistency ratio and then perform the sensitivity analysis. Research design barriers emerged as the most challenging barrier to research collaborations, implying researchers place huge emphasis on the compatibility of research goals, objectives, paradigms and perspectives. The study provides insights for individuals/institutions leading and managing research collaborations into improving the collaborative dynamics in social sciences.

Publication Trends and Hot Spots of ChatGPT's Application in the Medicine.

This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which "digital health"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

Exercises for developing civil‐military collaboration capability: A constructive alignment analysis

AbstractFollowing the Cold War, Sweden adopted a doctrine promoting perpetual peace, resulting in significant reductions in military defense capabilities and the dismantling of civil defense infrastructure. However, geopolitical events such as the Russian invasion of Georgia in 2008 and the annexation of Crimea in 2014 prompted Sweden to reassess its security posture in 2015, leading to the reconstruction of total defense, and an emphasis on collaboration between the civilian and military defense sectors. Exercises have been instrumental in fostering civil‐military collaboration capability. This study aims to investigate whether these exercises are effective in enhancing capability for civil‐military collaboration. Data collection involved participant observation during seven exercises and was analyzed using constructive alignment (CA). Findings indicate that the exercises did not yield discernible learning outcomes, and this is attributed to the Swedish principles of responsibility, deficiencies in CA, contextual support, and entrenched defensive routines.

Unpacking the impact of entrepreneurial learning on business model innovation in internet startups: Mediating roles of digital capabilities

While researchers recognize the central role of business model innovation (BMI) in the success of digital startups, empirical research systematically exploring the pathways of BMI in the digital era for such startups remains scarce. This study employs the dynamic capability theory to construct a theoretical framework termed the “learning capability model” to examine the influence of entrepreneurial learning on BMI. We collected questionnaires from 386 Internet startups and utilized hierarchical regression analysis to explore the impact of digital capabilities on these startups. We first identified two different types of entrepreneurial learning: exploratory learning and exploitative learning. Subsequently, we tested for their effects on BMI. The empirical results indicate that both exploratory and exploitative learning significantly impact BMI. However, exploitative learning exerts a greater influence than exploratory learning. These tests revealed that digital sensing capabilities, digital operational capabilities, and digital resource collaboration capabilities have a partial mediating effect on the relationship between entrepreneurial learning and BMI. Among these, digital operational capabilities exert the most significant mediating effect, while digital sensing capabilities exhibit weaker mediating effects. This study contributes to our understanding of digital capabilities and provides practical insights for Internet startups.

Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution.

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism.

Designing a smart classroom with advanced Audiovisual equipment and soft collaboration capabilities for Higher Education

This is a technology-enabled classroom design which reflects a thoughtful integration of cutting-edge audiovisual solutions to enhance the learning environment. The inclusion of automated audio-visual solutions indicates a commitment to creating a seamless and efficient learning experience through technology. The incorporation of a voice lift application ensures clear and audible communication for the instructor, addressing potential challenges in large classroom settings. The use of Dante-based communication over Ethernet signifies a high-quality and reliable audio distribution system, enabling efficient communication within the classroom. The inclusion of features catering to individuals with disabilities demonstrates a commitment to creating an inclusive and accessible learning environment. The beamforming microphone array system with multiple elements is a sophisticated technology that allows for effective tracking of the active participant. This ensures that the audio focus adapts to the speaker's location. The automatic and steerable coverage type enhances the adaptability of the microphone array system, automatically focusing on the speaker. This feature contributes to an immersive and dynamic learning experience. The support for Power over Ethernet (PoE) simplifies the installation and management of AV devices in the classroom. It reduces the need for additional power outlets and cables, streamlining the overall setup. Overall, this design prioritizes advanced audiovisual technologies to create a unique, state-of-the-art, and inclusive technology-enabled classroom. The focus on clear communication, adaptability, and ease of use demonstrates a commitment to providing an optimal learning environment for both faculty and students

HydroRTC: A web-based data transfer and communication library for collaborative data processing and sharing in the hydrological domain

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities.

The Effectiveness of the Problem Based Learning Model in Improving Collaborative Capabilities and Student Learning Outcomes in Class XI SMA

One of the skills that must be possessed by students in the 21st century is collaborative ability. Collaborative skills are abilities possessed by individuals in the form of groups to exchange thoughts or ideas and work together to achieve the expected goals. Increasing collaborative skills in students has a positive impact, namely producing more knowledge than just completing work independently and students can exchange ideas to be able to solve problems encountered in the learning process obtained.The efforts made in this study by applying the problem based learning (PBL) learning model to be able to improve collaborative abilities and learning outcomes learners. The use of the PBL learning model can improve scientific attitudes and student learning outcomes. The PBL model can improve learning outcomes and students' communication skills. The problem-based learning model can assist teaching staff in undergoing the learning process because it can improve student learning outcomes. Based on the background that has been formulated, the purpose of conducting this research is to determine the effectiveness of the PBL model in improving collaborative abilities and student learning outcomes in class XI reproductive system material.

Empowering employees for digital transformation in manufacturing enterprises: A case study

Purpose: This study explores how manufacturing enterprises can effectively promote digital transformation through employee empowerment with ability, motivation, and opportunity empowerment at different stages.Design/methodology/approach: Drawing on ability, motivation, and opportunity (AMO) theory, this qualitative research takes the Jiangsu Shangshang Cable Group as a typical case to discuss manufacturing enterprises’ evolving employee empowerment model. Gioia structured data analysis method was adopted to analyse the data.Findings/results: In the stage of information technology (IT) application, the main task of employee empowerment is strengthening the employees’ abilities (‘can do’) and motivation (‘want to do’) by training their IT knowledge and skills, changing their mindset towards IT application, and letting them experience the efficiency of the latter. In the intelligence manufacturing stage, training employees to do better is the core content of employee empowerment. Enterprises can strengthen employees’ intelligent manufacturing skills, practice data-based performance appraisal and salary management, and let employees participate in intelligent manufacturing. In the stage of digital transformation of the whole process, employees are required to ‘do their best in the long run’. Enterprises could improve employee collaboration capabilities, build growth mechanisms, and involve every employee in innovation.Practical implications: In the digital transformation process, manufacturing enterprises could empower employees according to the different goals and requirements in the three stages of IT application, intelligence manufacturing, and digital transformation of the whole process. It has reference value for manufacturing enterprises to achieve digital transformation.Originality/value: This study explores a three-dimensional evolving employee empowerment model to promote manufacturing enterprises’ digital transformation.

Migration of legacy Java desktop applications to collaborative Web

The migration of legacy applications to more advanced technology platforms has been a recurring theme in the academic and business sectors. This transformation is driven by the desire to overcome the limitations of traditional desktop applications and integrate advanced remote access and collaboration capabilities. Essential features not only for technology upgrade but also to enable organizations to take advantage of remote collaboration.The COVID-19 pandemic has accelerated this need, forcing many organizations to adopt remote work and online collaboration modalities, forcing them to transform their information systems. There are various methodologies and techniques, such as conversion, redeployment and wrapping, that offer different approaches to facilitate this migration by minimizing the effort required during the process. This paper specifically studies the migration of Java Swing applications to web environments, particularly those that incorporate collaborative functionalities. Several solutions that follow different migration strategies have been evaluated, performing a more detailed analysis of the Vaadin platform as an efficient tool to perform this migration, with limited effort and special attention to the inclusion of new collaborative functionalities. Finally, a migration procedure is defined that has been validated through two case studies.

Collaborative Capability and Competitiveness of Local Pharmaceutical Manufacturing Firms in Kenya

Collaborative Capability and Competitiveness of Local Pharmaceutical Manufacturing Firms in Kenya

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Fostering effective governance through intragovernmental networks: a case of a justice, crime prevention and security cluster network

South Africa’s crime challenge remains despite substantial public sector efforts. As wicked problems, including crime, often transcend the conventional capacities of public authorities to address them effectively, the primary aim of this research was to uncover the underlying dimensions that contribute to the effective functioning of an intragovernmental collaboration network. This research adopted a case study approach to delve into the structure and efficacy of the South African Justice, Crime Prevention, and Security Cluster. The chosen methodology and insider access to senior management produced rich descriptions not previously available to offer a conceptual understanding of balances and trade-offs required during a mandated and directed collaboration. Findings offer enhanced understanding, contributing to overcoming traditional barriers in public administration networks and reforming traditional hierarchical structures to offer meaningful governance alternatives or improvements to existing structures. Specifically, this study reveals gaps in the intragovernmental network and offers insights for governance and performance enhancements. Findings emphasize the importance of fostering a collaborative culture based on performance and accountability. The study underscores the need for more robust network directives, clarifying roles and responsibilities, and enhancing leadership attributes. It stresses the importance of efficient coordination, collaborative capability, and shared purpose. The study also suggests cluster-oriented budgeting and greater involvement of key stakeholders, including civil societies, research institutions, and academic establishments. These findings contribute to improving government-led networks in addressing complex challenges.

The Covid-19 Pandemic and The Future of Work.

The "future of work" refers to workplace modifications projected to occur following large scale events impacting workforce health and safety such as pandemics, technological advancements, or economic shifts. This paper examines the influence of the COVID-19 pandemic on the future of work. The electronic databases PubMed, Google Scholar, and Scopus were explored using search terms such as "post-pandemic," "working force," and "future of work" to retrieve papers published between 2000 and 2023. The main characteristics of the future of work modification revealed are blurring of work-home boundaries, increase in telework and the gig economy, demand for artificial intelligence, smart working, quiet hiring, diversity and inclusion, and algocratic governance. Needed skills include virtual collaboration capability, cross-cultural competency, computational thinking, news media literacy, transdisciplinarity, sense making, and focusing on mental health and well-being. Organizations are remodeling workforces to meet employee, employer, regulatory, and market expectations to adapt to future workplace needs.

The Effects of Multi-Level Diversity on Team Innovation: The Role of Collaborative Capability

Objective: This study examines the effects of surface-level, deep-level, and functional-level diversity on team innovation. This study also examines the moderation effect of the collaborative capability toward diversity and team innovation. Design/methodology/approach – The sample for this research was 175 creative team members who were then integrated into 38 creative teams of radio and television broadcasting institutions. Data collection was done using a survey method. Findings – This study indicates that surface-level and deep-level diversity negatively impact team innovation, while functional-level diversity has a positive. In addition, the study found that collaborative capability has been moderated on surface-level diversity and team innovation. However, collaborative capability does not moderate the effect of deep- and functional-level diversity on the innovation team. Originality/value – This research contributes to the team innovation literature by examining the influence of three characteristics of team diversity consisting of surface level, deep level, and functional comprehensively on team innovation, as well as the moderating role of collaboration capability as a contextual factor. Practical/Policy implication: From a practical perspective, this study has important implications for how practitioners in creative teams overcome surface and deep-level diversity challenges and take advantage of the functional-level diversity of members in creative teams. This research also increases understanding of the value of diversity in the context of team innovation.