Mapping knowledge structure of artificial intelligence research in Bangladesh based on co-word analysis

Purpose: This article aims to map the knowledge structure of artificial intelligence (AI) in Bangladesh through detecting the interdisciplinarity and topic hotspots in the light of co-word analysis. Methodology: This study adopted bibliometric analysis of publications collected from the Web of Science (WoS) database. The WoS database was searched and 1557 publications were found. 1359 papers were selected for final analysis after eliminating duplicates. Co-occurrence words matrix, keyword clusters, hot topics were mapped using co-word analysis. The results were mapped, clustered and presented by VOSviewer.Results: The result showed a rapidly increasing publication trajectory with 12 sub-domain cluster under the AI knowledge domain in Bangladesh. It also identified that AI, machine learning, classification, neural network, deep learning, artificial neural network, convolutional neural network, support vector machine and data mining are hot topics during the period of studied time. However, the findings also suggest that many research areas in the research domain of AI of Bangladesh is still nascent.Limitation: VOSviewer often avoid having overlapping terms when multiple terms are positioned very close to each other. So, the overlapping terms remain invisible sometimes.Practical implications: This study may have potential usefulness in uncovering the AI research fields’ intellectual structure within a discipline and also to anticipate future innovation pathways of AI field in Bangladesh.Originality: Bibliometric methods to explore the research trend and growth of AI research field as a ‘knowledge base’ in Bangladesh is one of the first attempts.

A Bibliometric and Visual Analysis of Global Urban Resilience Research in 2011–2020: Development and Hotspots

Urban resilience (UR), which promotes the implementation of resilient cities, has received widespread attention. The purpose of this study is to visualize the knowledge background, research status, and knowledge structure of relevant literatures by using a Citespace based scientometrics survey. The results show that UR is an increasingly popular topic, with 2629 articles published during the study period. (1) The most prolific publications and journals involved in the flourishment of UR research were identified by co-citation. The United States was the most productive contributor, with numerous publications and active institutions. Journal of Cleaner Production, Sustainability, International Journal of Disaster Risk Reduction were the three most cited journals. (2) Co-occurrence analysis was employed to determine the highly productive keywords, and subject categories in the UR domain, including “environmental science & ecology”, “environmental sciences, “science & technology”, “environmental studies”, “green & sustainable science & technology”, and “water resources”. (3) The diversity of highly cited authors in different countries and regions confirmed the evolution of UR studies. (4) Furthermore, the classification of UR knowledge was performed in the form of clusters and knowledge structure to achieve ten distinct sub-domains (e.g., Urban floods and stormwater management, Urban ecosystem services, Urban landscapes, and Trauma). This study provides an overview of UR research and research topics so that future researchers can identify their research topics and partners.

A personalized programming exercise recommendation algorithm based on knowledge structure tree

Personalized exercise recommendation is an important research project in the field of online learning, which can explore students’ strengths and weaknesses and tailor exercises for them. However, programming exercises differs from other disciplines or types of exercises due to the comprehensive of the exercises and the specificity of program debugging. In order to assist students in learning programming, this paper proposes a programming exercise recommendation algorithm based on knowledge structure tree (KSTER). Firstly, the algorithm provides a calculation method for quantifying students’ cognitive level to obtain their knowledge needs through individual learning-related data. Secondly, a knowledge structure tree is constructed based on the association relationship of knowledge points, and a learning objective prediction method is proposed by combining the knowledge needs and the knowledge structure tree to represent and update the learning objective. Finally, KSTER imports a matching operator that calculates cognitive level and exercise difficulty based on learning objectives, and makes top-η recommendation for exercises. Experiments show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. The comparison experiments with real-world data demonstrate that KSTER effectively improves students’ learning efficiency.

The Development of Extended Reality in Education: Inspiration from the Research Literature

Education is an important driving force for sustainable social development. Emerging technologies such as extended reality (XR), including augmented reality (AR), virtual reality (VR) and mixed reality (MR), have been widely used. Recently, a large number of theoretical and empirical studies on the use of XR in the field of education for sustainable development have emerged. This paper uses bibliometric analysis to analyze the publication and citation trends of articles, prolific authors, institutions and countries, influential works, current topics, emerging trends, and knowledge structure to explore the overall productivity and XR research trends in the field of education for the period 1991–2021. Future development directions are also considered. On the basis of bibliometric analysis, this paper puts forward suggestions for the application of XR in the field of education for sustainable development.

Exploring consumer non-knowledge in the agrifood context and its effects on behaviour

PurposeConsumer knowledge has been one of the most studied variables in marketing due to its strong influence on consumer behaviour. Knowledge level has traditionally been measured through objective knowledge and the number of correct answers in a battery of items about product characteristics. The authors argue that this analysis could be complemented with other information, that is, the structure of non-knowledge. The main objective of this work is to explore the nature and explanatory potential of this new dimension on consumer behaviour in the agrifood context. The principal hypothesis is that, while they may have similar levels of objective knowledge, there are significant differences between the behaviour of consumers who have a predominant pattern of ignorance (tendency to answer “I don't know”) and those who are in error (tendency to give wrong answers).Design/methodology/approachThe present study draws on data derived from five case studies examining consumer knowledge about agrifood products (olive oils, Iberian ham and orange juice) and certain aspects of consumer behaviour. A sample of 4,112 participants was classified into two non-knowledge profiles: wrong, if most items answered incorrectly in a questionnaire were wrong; or ignorant, if most items answered incorrectly were “don't know”.FindingsThe results obtained supported the argument that complementing the study of consumer knowledge with an analysis of the structure of non-knowledge is worthwhile, as differences within the structure are associated with different patterns of consumer behaviour.Originality/valueIn the present study, it is proposed that the measurement of knowledge be complemented with an analysis of the consumer's non-knowledge structure (items not answered correctly), given its effects on behaviour, an aspect hitherto unconsidered in the literature. To do so, a new index is proposed.

New Collaboration through Artefact-Mediated Interaction with a Joint Knowledge Base

Collaboration is changing and increasingly emerging as what we call “New Collaboration”, a knowledge-based and community-oriented way of working together (especially digital, online collaboration). Unfortunately, organisations use only a small percentage of the potential of New Collaboration. One main reason for this is that they do not understand that New Collaboration is based on knowledge sharing and requires the individual knowledge of the collaborators to be integrated into a shared knowledge structure, a so-called Joint Knowledge Base (JKB). This concept of a Joint Knowledge Base as the tacit knowledge structure which is constructed, shared and maintained during collaboration, emerged during the course of our previous work and became more and more prominent as a key to collaboration. When a group interacts, the JKB functions as an interaction bridge, and this is why it is a key to collaboration. In this paper, we will revise and elaborate in more detail our concept of a JKB and explain its role in artefact-mediated interaction. First, we will explain the main characteristics of New Collaboration and summarise them based on a concise definition. Secondly, we will introduce the concept of a Joint Knowledge Base, explore the role of social negotiation in constructing it, define the JKB as a distributed knowledge structure, discuss the problem of obstacles which hinder its development and suggest how to solve it by means of gaining deeper insight into the complexity of the involved processes (communication, interaction). And next we will further develop this solution by introducing the concept of boundary artefacts and describing their implementation as tools for artefact-mediated interaction by means of a systematic approach. Finally, we will explain this systematic approach and show how boundary artefacts and artefact-mediated interaction work in practice during meetings performed on a commercially available collaboration platform where they contribute to the construction of a JKB.

Institutional pressure and open innovation: the moderating effect of digital knowledge and experience-based knowledge

Purpose When considering the influence of external social, technical and political environments on organizations’ open innovation behavior, especially in emerging markets, institutional theory is especially salient. This study aims to answer the question of how to integrate organizations’ external institutional pressures and internal knowledge structure to mitigate the challenges in the open innovation process. Design/methodology/approach This study uses a sample of 2,126 observations from the 2012 World Bank Enterprise Survey. A multivariate regression model is designed to explore the impact of external institutional pressure (i.e. coercive pressure, mimetic pressure and normative pressure) on open innovation, as well as the moderating effect of digital knowledge and experience-based knowledge. Findings The results show that institutional pressure has a positive role in promoting open innovation; digital knowledge weakens the positive relationship between institutional pressure and open innovation; experience-based knowledge strengthens the positive relationship between institutional pressure (especially coercive pressure) and open innovation. Originality/value This study combines institutional theory and knowledge management to enriches insights into open innovation in emerging markets. Beyond recognizing the inherent multidimensionality of the concept of institutional pressure, this study creates an integrated path for the legitimacy acquiring of enterprises through the knowledge structure design (i.e. digital knowledge and experience-based knowledge). It also deepens the institutional pressure to enable the implementation of digital knowledge to manage open innovation processes.