Search results

Business model innovation (BMI) constitutes a priority for managers across industries, but it represents a notoriously difficult innovation, with several challenges, many of which are cognitive in nature. The received literature has variously suggested that one way to overcome challenges to BMI, including cognitive ones, and support the cognitive tasks is using visual representations. Against this background, we aim at offering a contribution to the emerging line of inquiry at the nexus between business models (BMs), cognition and visual representations. Specifically, we develop a new method for visual representation of the BM in support of simplification of the cognitive effort and neutralisation of cognitive barriers. The resulting representation – a network-based representation, anchored on the activity-system perspective and offering complementarity and centrality/periphery measures – allows to visually represent an existing BM as a network (nodes and linkages) of interdependent activities and to express information related to the degree of centrality/periphery of single activities (nodes) with respect to the rest of a BM configuration. This information, we argue, is potentially very valuable in supporting the cognitive tasks involved in business model reconfiguration (BMR). We guide the reader to progressively appreciate how the development of the proposed method for visual representation is anchored to two main characteristics of BMR, namely the discovery-driven nature of BMR and the path-dependent nature of BMR. We offer initial insights on the cognitive value of such a type of representation in relationship to the simplification of the cognitive effort and the neutralisation of cognitive barriers in BMR.

Computer aided process planning (CAPP) is an effective way to integrate computer aided design and manufacturing (CAD/CAM). There are two key issues with the integration: design input in a feature‐based model and acquisition and representation of process knowledge especially empirical knowledge. This paper presents a state of the art review of research in computer integrated manufacturing using neural network techniques. Neural network‐based methods can eliminate some drawbacks of the conventional approaches, and therefore have attracted research attention particularly in recent years. The four main issues related to the neural network‐based techniques, namely the topology of the neural network, input representation, the training method and the output format are discussed with the current systems. The outcomes of research using neural network techniques are studied, and the limitations and future work are outlined.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

Project knowledge in the construction context needs to be disseminated within organisations to improve organisations’ performance and learning. Much of project knowledge is tacit, residing in social interactions in team relationships, and likely to be disseminated through social networks and processes within organisations. Social capital comprises both the network of strong personal relationships and the knowledge resources that may be mobilised through that network. The purpose of this paper is to explore the role of “networks” in disseminating project knowledge within the construction organisations in Sri Lanka.

The case study research method was used to investigate the research problem. The principal data collection technique used was semi-structured interviews. Content analysis, tables and cognitive maps were used to analyse data.

The findings revealed that strong network ties created with equal interaction of project and head office (H/O) individuals, frequent connectivity and much closer relationships enhance the flexibility of sharing project knowledge. The available invisible informal social networks within project and H/O individuals play a key role in disseminating project knowledge compared to business networks.

This research offers useful implications for construction organisation, in particular, to pay careful attention to nurture networks within their organisations in order to benefit from disseminating project knowledge widely within the organisations for effective re-use in future.

An indexing language is made more accessible to searchers and indexers by the presence of entry terms or near‐synonyms. This paper first presents an evaluation of existing entry terms and then presents and tests a strategy for creating entry terms. The key tools in the evaluation of the entry terms are documents already indexed into the Medical Subject Headings (MeSH) and an automatic indexer. If the automatic indexer can better map the title to the index terms with the use of entry terms than without entry terms, then the entry terms have helped. Sensitive assessment of the automatic indexer requires the introduction of measures of conceptual closeness between the computer and human output. With the tools described in this paper, one can systematically demonstrate that certain entry terms have ambiguous meanings. In the selection of new entry terms another controlled vocabulary or thesaurus, called the Systematized Nomenclature of Medicine (SNOMED), was consulted. An algorithm for mapping terms from SNOMED to MeSH was implemented and evaluated with the automatic indexer. The new SNOMED‐based entry terms did not help indexing but did show how new concepts might be identified which would constitute meaningful amendments to MeSH. Finally, an improved algorithm for combining two thesauri was applied to the Computing Reviews Classification Structure (CRCS) and MeSH. CRCS plus MeSH supported better indexing than did MeSH alone.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

The implications and influence of different cognitive map structures on decision-making, reasoning, predictions about future events, affect, and behavior remain poorly understood. To-date, we have not had the mechanisms to determine whether any measure of cognitive map structure picks up anything more than would be detected on a purely random basis. We report a Monte Carlo method of simulation used to empirically estimate parameterized probability outcomes as a means to better understand the behavior of cognitive map. Using worked examples, we demonstrate how the results of our simulation permit the use of exact statistics which can be applied by hand to an individual map or groups of maps, providing maximum utility for the collective and cumulative process of theory building and testing.

The construction business is widely recognised for its inherent complexity and dynamic nature, which stems from the nature of the job involved. The industry is often regarded as one of the most challenging industries globally in terms of implementing environmental, health and safety (EHS) practices. However, in the absence of EHS, the construction industry cannot be considered sustainable. Therefore, this study aims to identify the trends, knowledge gaps and implications of EHS research to enhance construction activities and knowledge.

The study adopted a science mapping approach involving bibliometric and scientometric analysis of 407 construction EHS publications from the Scopus database with the VOSviewer software. The study is based on journal articles from the Scopus database without restriction to any time range.

The main focus of construction EHS research identified in the study includes sustainability-related studies, risk-related, environmental issues, EHS management, integrated management systems studies, health and safety related and EHS in the construction process. Some emerging areas also identified include productivity, design, culture, social sustainability and machine learning. The most influential and productive publication sources, countries/regions and EHS publications with the highest impact were also determined.

Documents published in the Scopus database were considered for analysis because of the wider coverage of the database. Journal articles written in English language represent the inclusion criteria, whereas other documents were excluded from the analysis. The study also limited the search to articles with the engineering subject area.

The research findings will enlighten stakeholders and practitioners on the focal knowledge areas in the EHS research domain, which are vital for enhancing EHS in the industry.

To the best of the authors’ knowledge, this review-based study is the first attempt to internationally conduct a science mapping on extant literature in the EHS research domain through bibliometric and scientometric assessments.