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This paper presents a prototype of the collaborative problem solving environment (PSE) for flood forecasting. Flood forecasting is a complex problem that requires cooperation of many scientists in different areas. To enable this cooperation in a manner comfortable to hydrometeorological experts, a part of the CrossGrid project is aimed towards developing a PSE, whose prototype is described here. The PSE consists of a cascade of simulation models, a storage system for computed and measured data and other used datasets, a Web‐based portal with collaboration tools and a powerful computation facility. The whole system is tied together by grid technology and is used to support a virtual organization of experts, developers and users.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

The purpose of this paper is to examine the application of intelligent agents (IAs) in organizational memory systems within the larger schema of knowledge management (KM) strategies. This includes targeted roles of IAs in relation to institutional memory approaches.

A conceptual exploration of related sections of the Grundspenkis seven-layer intelligent enterprise memory framework that serves to identify, retain, deliver and reuse information for future utilization is conducted. Applications of IAs in multiple industries are presented to illustrate the conceptual model in practice.

This paper identifies arising roles that IAs perform in information search, retrieval and analysis in the organizational memory formation process and extensions that have emerged in a non-linear bi-directional form. These layered roles include obtaining and reapplying important information as part of extended human–machine cognition.

While exploratory and conceptual in nature, this research paper discusses IAs as possible components in the advancement of organization memory.

By analyzing the application of IAs in different industries, across select layers of a KM structure, groundwork is laid for both descriptive research (i.e. where and how artificial intelligence is being used in those industries) and prescriptive practice (i.e. how other industries can benefit from such assistance and what patterns of implementation to expect).

This study explores the role IAs play in helping knowledge workers gather, retain and find relevant information and how KM strategies may assist organizational memory.

Strategic agility is a fuzzy concept that has become crucial to cope with environmental uncertainty and instability; hence, more in-depth studies are highly needed. The aim of this paper is to shed light on the still diffuse research area of strategic agility by clarifying its scope and concept, as well as identifying the different topics that have been examined thus far. Finally, the intent of this paper is to show the existing gaps in the literature to provide scholars with a clear roadmap for future research.

Bibliometric and content analyses are used in this study to review the most impactful papers in strategic agility between 1996 and 2021. Citation and mapping analyses are conducted through SciMAT software, and a dynamic approach is adopted by assessing and discussing the evolution of strategic agility throughout five different periods.

This study reveals that strategic agility is a research line that has neither gained consensus nor reached maturity and that it is linked to several thematic areas or topics. The study offers a complete understanding of the state of the art of strategic agility over time and underscores its main future research lines.

This study presents a complete map of the strategic agility research thus far by using novel bibliometric techniques. This approach is especially interesting because it allows for identifying the dynamic relationships among themes within the topic over five different periods.

策略靈活性是一個模糊概念。這個概念對應付環境的不確定性和不穩定性至為重要, 因此, 我們極須對其作更深入之研究。目前, 對策略靈活性的研究範圍仍很分散。本文擬為這研究範圍提供解說, 方法是透過闡釋策略靈活性的範疇和概念, 及確定至今曾被探討過的課題。最後、本文擬顯示目前文獻中的研究缺口, 以為學者提供一個未來研究的清晰藍圖 。

研究利用文獻計量分析法與內容分析法, 去審視1996年至2021年期間研究策略靈活性最有影響力的文章, 透過SciMAT可視化軟件進行引用文獻及繪圖分析, 亦採用動態方法, 去評估及討論橫跨五個不同時期策略靈活性的演變。

研究顯示、策略靈活性為一既無共識, 也未臻成熟的研究線; 研究亦顯示、策略靈活性與多個專題領域及主題相關連。本研究使我們對策略靈活性隨著時間推移的最新理念得到全面的理解, 研究亦強調了策略靈活性未來主要的研究線。

本研究透過新穎的文獻計量分析法, 提供了一個策略靈活性研究發展至今的完整藍圖。這方法至為有趣, 因其能確定橫跨五個不同時期、在同一課題下各個主題間的動態關係。

This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature.

The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction.

Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction.

This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

The purpose of this paper is to provide a methodology for automatic annotation of a multimedia collection of intangible cultural heritage mostly in the form of interviews. Assigned annotations provide a way to search the collection.

Annotation is based on automatic extraction of metadata and is conducted by named entity and topic extraction from textual descriptions with a rule-based approach supported by vocabulary resources, a compiled domain-specific classification scheme and domain-oriented corpus analysis.

The proposed methodology for automatic annotation of a collection of intangible cultural heritage, applied on the cultural heritage of the Balkans, has very good results according to F measure, which is 0.87 for the named entity and 0.90 for topic annotation. The overall methodology enables encapsulating domain-specific and language-specific knowledge into collections of finite state transducers and allows further improvements.

Although cultural heritage has a significant role in the development of identity of a group or an individual, it is one of those specific domains that have not yet been fully explored in case of many languages. A methodology is proposed that can be used for incorporating natural language processing techniques into digital libraries of cultural heritage.

In organizations, individual user’s compliance with business processes is important from a regulatory and efficiency point of view. The restriction of users’ choices by implementing a restrictive information system is a typical approach in many organizations. However, restrictions and mandated compliance may affect employees’ performance negatively. Especially when users need a certain degree of flexibility in completing their work activity. The purpose of this paper is to introduce the concept of directive explanations (DEs). DEs provide context-dependent feedback to users, but do not force users to comply.

The experimental study used in this paper aims at investigating how DEs influence users’ process compliance. The authors used a laboratory experiment to test the proposed hypotheses. Every participant underwent four trials for which business process compliance was measured. Two trial blocks were used to cluster the four trials. Diagrammatic DEs were provided in one of the trial blocks, while textual DEs were provided in the other. Trial blocks were counterbalanced.

The results of the experiment show that DEs influence a user’s compliance, but the effect varies for different types of DEs. The authors believe this study is significant as it empirically examines design characteristics of explanations from knowledge-based systems in the context of business processes.

This study is certainly not without limitations. The sample used for this study was drawn from undergraduate information systems management students. The sample is thus not representative of the general population of organizations’ IT users. However, a student sample adequately represents novice IT users, who are not very familiar with a business process. They are particularly suitable to study how users react to first-time contact with a DE.

The findings of this study are important to designers and implementers of systems that guide users to follow business processes. As the authors have illustrated with a real-world scenario, an ERP system’s explanation can lack details on how a user can resolve a blocked activity. In situations in which users bypass restricted systems, DEs can guide them to comply with a business process. Particularly diagrammatic explanations, which depict actors, activities, and constraints for a business process, have been found to increase the probability that users’ behavior is business process compliant. Less time may be needed to resolve a situation, which can result in very efficient user-system cooperation.

This study makes several important contributions to research on explanations, which are provided by knowledge-based systems. First, the authors conceptualized, designed, and investigated a novel type of explanations, namely, DEs. The results of this study show how dramatic the difference in process compliance performance is when exposed to certain types of DEs (in one group from 57 percent on the initial trial to 82 percent on the fourth trial). This insight is important to derive design guidelines for DE, particularly when multimedia material is used.

Students entering Zayed University are expected to become active participants in their learning. However, the majority of these students have come from a public education system that is recognized to focus on teacher-centered passive learning. Students may be unprepared for this transition. This paper reports on a case study of changes in performance and motivation for students transitioning from passive learning to active learning.

Three students from the public education system were followed through two consecutive courses employing increasing active learning. Methods included observations, surveys, and interviews. Results indicate that the initial transition from passive learning to active learning has a negative impact, mainly due to inadequate preparation. However, subsequent development of skills through exposure results in improvement to the extent that motivation and performance exceed high school levels. It is concluded that the transition from active learning has the capacity to greatly improve student achievement if properly managed.

The purpose of this paper is to evaluate the results of inspections carried out by the State Veterinary Administration (SVA) of Czech Republic (CR) for the occurrence of chemical contaminants in animal products before and after CR entered the European Union (EU).

Data was collected from e-databases of the SVA from 1999–2016 and sorted into categories (game animals and fish; livestock; food and raw material of animal origin) and time periods (one before entry and two after entry of CR to the EU). Analyses of the samples were categorized as “positive samples” (any presence of contaminants) and “samples above the MRL” (presence of contaminants exceeding the maximum residue levels).

Results showed a significant decrease in the number of positive findings of contaminants during the monitored years 1999–2016, especially after CR entered the EU. Most encouragingly, the number of samples that exceeded the MRL was less than 1 percent from all the tested samples of animal origin and, after entry to the EU, in one category (food and raw materials of animal origin) it was even less than 0.1 percent. Findings of banned substances indicate continued environmental contamination in CR; however, this remains a problem in most of Europe due to their extensive use in the past and slow degradation.

This paper provides an overview of the occurrence of chemical contaminants and their levels in food of animal origin in view of the changing legislative requirements before and after CR entered the EU.

Instructors at Francis Marion University developed a recycling course in an attempt to satisfy the students’ goals of increasing campus awareness about sustainability and recycling, and the teachers’ goals of using problem‐based learning approaches in class. Students enrolled in the course designed their own experiment, completed the experiment and presented the results at several national meetings. The focal point of the experiment was student apartments, where some students were provided with recycling bins, some were not, and some were provided with both bins and education about the importance of recycling. Results show that students living in campus apartments significantly reduced their waste stream when given recycling bins and some education about recycling. Although ANOVA tests showed that while the presence of recycling education did not result in significantly more recycling, students who received bins (opportunity) recycled more as time went on. Positive student feedback indicated the success of using project‐based learning to teach sustainability.