Search results

In today’s rapidly evolving work landscape, the design of office spaces is a crucial concern for organizations. Companies are redefining offices as collaboration hubs to entice employees back to in-person work. However, the understanding of how employees choose their workspaces, especially for collaborative activities, and how this should inform office design is lacking. Workers’ collaborative activity patterns can help better understand workspace choice behavior (WCB). In two studies, this paper aims to explore which characteristics of collaborative activities to consider when reshaping offices.

Data collected in a cross-sectional study design at a research institution (n = 285) and a university (n = 352) were used for confirmatory factor analyses and regression analysis.

The first study shows that collaborative activities can be classified into three distinct types: coordinative activities (planned and formal), deep collaboration (planned and complex) and spontaneous communication (informal and short encounters). The second study revalidates this classification and reveals patterns impacting WCB. Frequency and location preference of spontaneous communication and work environment satisfaction are strong predictors of on-site work. Personal characteristics like gender, age, managerial position or commute time are less consequential than assumed.

The results pinpoint guidelines for office designers and leaders in shaping effective workspaces and policies.

This paper provides new insights into classifying collaborative activities and personal characteristics, activity characteristics and environmental factors influencing WCB.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

This study aims to analyze the implications of the recent European Union Regulation 2023/1542 on the circular economy and stakeholder strategies within the electric vehicle lithium-ion battery (EV-LIB) sector. It aims to explain the policy intentions, recommend practical strategies for stakeholders and examine how the new regulation exerts pressure on stakeholders to transition from older directives to more sustainable practices and operational standards, while also highlighting policy gaps.

The research employs a dual-method approach, combining text analysis of EU legislation with semi-structured interviews of industry stakeholders. This methodology allows for a comprehensive understanding of the regulatory impacts by integrating legislative intent with practical, on-the-ground insights from key players in the EV-LIB sector.

Our findings show that the three aggregated dimensions of operational sustainability, R&D and new technologies and collaborative dynamics are the key dynamics underlying the intended outcomes. The findings also highlight the policy’s historical development, the stakeholder categories, the implications for each and practical recommendations in responding to the policy requirements. Additionally, the findings identify policy gaps, such as weak incentives and broad economic operator classifications, with examples from international markets. The regulation creates proactive stakeholders driving innovation and collaboration and reactive ones adapting to changes, where static implicit implications may affect their viability by imposing unequal burdens.

To the best of the authors’ knowledge, this paper is the first to analyze the new EU Regulation 2023/1542, offering novel insights into the strategic responses required by stakeholders to adapt to the regulatory pressures. By focusing on the latest regulatory framework and its practical implications, the study bridges the gap between policy and practice, providing valuable guidance for industry players navigating the evolving regulatory environment.

• (1)

  EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

• (2)

  Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

• (3)

  R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

• (4)

  Emergence of Proactive vs Reactive stakeholder dynamics.

• (5)

  The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

Emergence of Proactive vs Reactive stakeholder dynamics.

The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.

First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.

The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.

This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

This paper introduces the problematic of the SBP modeling. Our objective is to provide a conceptual analysis related to the concept of SBP. This facilitates, on the one hand, easier understanding by business analysts and end-users, and one the other hand, the integration of the new specific concepts relating to the SBP/BPM-KM domains into the BPMN meta-model (OMG, 2013).

We propose a rigorous characterization of SBP (Sensitive Business Processes) (which distinguishes it from classic, structured and conventional BPs). Secondly, we propose a multidimensional classification of SBP modeling aspects and requirements to develop expressive, comprehensive and rigorous models. Besides, we present an in-depth study of the different modeling approaches and languages, in order to analyze their expressiveness and their abil-ity to perfectly and explicitly represent the new specific requirements of SBP modeling. In this study, we choose the better one positioned nowadays, BPMN 2.0, as the best suited standard for SBP representation. Finally, we propose a semantically rich conceptualization of a SBP organized in core ontology.

We defined a rigorous conceptual specification for this type of BP, organized in a multi-perspective formal ontology, the Core Ontology of Sensitive Business Processes (COSBP). This reference ontology will be used to define a generic BP meta-model (BPM4KI) further specifying SBPs. The objective is to obtain an enriched consensus modeling covering all generic concepts, semantic relationships and properties needed for the exploitation of SBPs, known as core modeling.

This paper introduces the problem of conceptual analysis of SBPs for (crucial) knowledge identification and management. These processes are highly complex and knowledge-intensive. The originality of this contribution lies in the multi-dimensional approach we have adopted for SBP modeling as well as the definition of a Core Ontology of Sensitive Business Processes (COSBP) which is very useful to extend the BPMN notation for knowledge management.

This study aims to analyze the distribution of novelty among scholarly papers in the field of library and information science (LIS) in China. Specifically, this study explores the distribution of novelty of papers in various journals, research topics and different periods. It is possible to understand the characteristics of LIS research in China and what factors have influenced it.

This paper collects articles published in Chinese library science journals indexed by the Chinese Social Sciences Citation Index from 2000 to 2022. The BERTopic model is used based on abstracts of the papers and to obtain the topic of each paper. Based on the combination innovation theory of reference pairs cited by focal papers, novelty scores of all papers are calculated. Next, this paper analyzes the novelty of papers under different topics. Finally, this paper analyzes the differences in author collaboration patterns across various topics, aiming to explain how these differences relate to the novelty of papers from a collaborative perspective.

This study shows that archival research topics have lower novelty than papers on journal evaluation and patent technology in Chinese LIS. Research papers in this field are gradually becoming more novel over time. Papers on different topics and with varying degrees of novelty exhibit distinct author collaboration patterns, with low-novelty topics more frequently featuring solo authorship, while high-novelty topics tend to involve a higher percentage of inter-institutional collaboration.

This study investigates the novelty characteristics of research papers on different topics in the field of LIS in China. The authors’ contribution includes visualizing research hotspots and trends in the field and analyzing authors’ collaboration patterns at the level of research topics, thereby providing new perspectives on the factors affecting the novelty of these papers.

The purpose of this paper is to identify the areas and logic of integration of different stakeholders using different methods and to analyse their applicability and challenges in practical projects. The main aim is to describe how these different methods impact value creation.

Action design research was carried out in a large hospital construction project where the first author acted as an “involved researcher” and the second author acted as an “outside researcher”. Two workshops were organised to evaluate the direct and indirect challenges and benefits of the applied four methods and to explain how different methods enable value creation.

All the studied methods provide good results in terms of usability and commitment to the aims of the project, thus delivering the direct benefits expected. Process, people and tools logic works well in this case project when applying the methods properly. Significant evidence was provided on secondary deliverables of the methods, and all analysed methods had a significant impact in the area of leading people, clarifying what “focus on people” means and how it is enabled.

Focus on people can be achieved through different operative methods if applied in the right way. It is necessary to select the most suitable methods based on all the direct and indirect deliverables.

This case project offered a platform to analyse integration methods in a real-life project using the collaborative contract method. The authors were able to participate in the analysis by taking action from the very beginning of the project in terms of training, learning, continuous development and coaching of these methods and evaluating the applicability.

Effective management of risk and knowledge is critical to ensure the success of industry–university collaboration (IUC) projects. However, the intricate dynamics through which these factors influence the performance of IUC projects have yet to be fully investigated. The purpose of this study is to explore the interplay between risk management and knowledge management capabilities and their impact on IUC project performance.

A model was constructed and evaluated through the examination of a sample of 188 collaborative innovation projects located in the United Arab Emirates (UAE), utilizing structural equation models (SEM) and hierarchical regression analysis.

The findings indicate that social system risk, technical system risk and project management risk have a negative impact on the performance of university–industry collaboration (UIC) projects, while cultural, technical and structural knowledge management capabilities can mitigate the negative impact of these risks on the performance of IUC projects.

The study concludes with three recommendations aimed at improving the management of UIC projects, including the establishment of a distinct and precise management strategy, the deployment of a comprehensive and systematized management methodology and the adoption of a balanced management framework.

The originality and value of this study lie in its exploration of the interplay between risk management and knowledge management capabilities in IUC projects. While previous studies have examined either risk management or knowledge management in IUC projects separately, this study provides a comprehensive analysis of both factors and their combined impact on project performance. The study also contributes to the literature by highlighting the specific risks and knowledge management capabilities that are most relevant to the context of IUC projects in the UAE. The practical recommendations offered by the study can help project managers and stakeholders to improve the success of collaborative innovation projects.