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The purpose of this study is to explore the competitive source of Chinese firms in an industrial sector of complex product systems. It helps to reveal the organizational innovation developed by Chinese firms in coping with international competition and technological challenges.

The study uses a qualitative method of research. The evidences are mainly collected through interviews, field observation and document analysis.

A pattern of engineer-centered organization is the source of competitiveness of Nanrui (NR) Electric (NREC) in this study. The firm equips its front project teams, and now its overseas branches with developmental human resources and authorizes them the power of decision-making to leverage R&D projects. It is an emerging challenge to the traditional multi-national companies (MNC) pattern, and enables the Chinese firms to build their capabilities on context-based knowledge.

As a single-case study paper, there are limitations about the external validity of its argument. Through the in-depth discussion of the NREC case, this paper aims to generate some clues for future study in the relevant academic community, which can be a useful step to formal theorizing and modeling. That is why the authors develop the paper on a single case. As future directions of research, comparative studies covering more cases not only within the power system control and protection industry but also among different complex technology products industrial sectors are really needed.

For innovative firms from developing countries like China, they need to develop institutional arrangements to incentivize engineers in the frontline, which may help them to build competence upon successful interaction with customers. During the era of globalization, such a pattern may generate special competitiveness over giant multi-nationals or global production networks (GPNs).

The research provides an instructive case on the Chinese rise in industrial sectors of complex product systems. Its findings can not only provide enlightenment for industrial catch-up in developing countries through organizational innovation but also help to initiate a reconsideration of the traditional theorizing of MNC and GPN.

This study aims to assess “learning opportunities” provided to undergraduate students, from level three to six, in higher education (HE). A knowledge gap was identified within the current practice relating to learning opportunities for built environment (BE) students in HE. The study focussed on the themes under section two of the national student survey (NSS): how students explore ideas or concepts in-depth, bring information and ideas together from different topics and apply the learned content in a real-life context. The study aimed to provide recommendations for enhancing “learning opportunities” to the BE students within HE.

Data collection focussed on section two of NSS “learning opportunities” and documentary analysis, and a qualitative survey were adopted for this study. A documental analysis of 334 mid-module reviews was carried out. The qualitative data was collected from level three to level six students and academics from architecture, construction management, civil engineering and quantity surveying disciplines representing BE context. A sample of 40 students and 15 academics, including a Head of school, a Principal lecturer, Subject leads and lecturers, participated in interviews as part of a qualitative survey. In total, 12 drivers were developed using the data obtained through literature, documental analysis and interviews. These drivers were analysed using manual content analysis to identify their influence on the specified themes under NSS section two and circulated amongst academics to be ranked by identifying its influence to promote learning opportunities to BE students in HE.

This study highlighted 12 drivers which promote learning opportunities in HE within BE curriculum. Findings established that topics should be explained with more real-life or industry-orientated concepts such as simplification integrated into module delivery. Contrary to the literature, the use of physical materials (i.e. handouts and whiteboard) in addition to a virtual learning environment for detailed explanations were considered effective in exploring concepts. During the current COVID-19 pandemic, context-based learning needs to be promoted by integrating videos of practical implementation for better understanding. The study recognised that lab, fieldwork and tutorials were essential to apply what students have learned in BE curricula to a real-life context.

This study identified current learning approaches and provided recommendations to improve the BE students learning experience in HE. They identified 12 drivers that would significantly help academics and academic institutions to understand how learning opportunities should be facilitated in the BE curriculum to enhance student performances in HE.

Traditional assessment is based on perceiving and measuring knowledge possession and practical performance. This kind of assessment provides limited information about the capability of the learner to develop as a professional and to learn at work. In a typical skill test situation the teachers estimate how well the students know knowledge content and the work supervisors appraise how they perform in practice. However, reflective and social knowing are weakly assessed because of the problems with tacit and potential knowledge. Context‐based assessment requires that situational and contextual factors of knowing, and the social, reflective, cognitive processes of learning are considered carefully. The article reports a construction of a set of assessment criteria and piloting it in a field of vocational education. Carefully defined observation units and optimal compiled criteria are the key factors for understanding and improving the assessment practices.

The conditions that domain analysis becomes an academic school of information science (IS) are mature. Domain analysis is one of the most important foundations of IS. The purpose of this paper is to analyze and discuss metatheoretical and theoretical issues in the domain analytic paradigm in IS.

This paper conducts a systematic review of representative publications of domain analysis. The analysis considered degree theses, journal articles, book chapters, conference papers and other materials.

Domain analysis maintains that community is the new focus of IS research. Although domain analysis centers on the domain and community, theoretical concerns on the social and individual dimensions of IS are inherent in it by its using sociology as its important approach and socio-cognitive viewpoint. For these reasons domain analysis can integrate social–community–individual levels of IS discipline as a whole. The role of subject knowledge in IS is discussed from the perspective of domain analysis. Realistic pragmatism that forms the philosophical foundation of domain analysis is argued and the implications of these theories to IS are presented.

The intellectual evolving landscape of domain analysis during a quarter century is comprehensively reviewed. Over the past twenty-five years, domain analysis has established its academic status in the international IS circle. Being an important metatheory, paradigm and methodology, domain analysis becomes the theoretical foundation of IS research. This paper assesses the current state of domain analysis and shows the contributions of domain analysis to IS. It also aims to inspire further exploration.

External knowledge is generally believed to be of prime importance to small to medium‐sized enterprises (SMEs). However, a review of the literature shows that no empirical research has looked at knowledge management issues at the inter‐organizational level in SMEs. This paper seeks to report on an empirical investigation with UK SMEs in the service sector to identify their needs and practices regarding inter‐organizational knowledge transfer, and thus provide empirical evidence to support the above belief.

A two‐tier methodology (i.e. using both questionnaire survey and interview approaches) is deployed to address the main research objectives. A questionnaire survey of SMEs is carried out to investigate their current inter‐organizational knowledge transfer situation and managers' perception on various relevant issues. Then 12 face‐to‐face interviews with SME managers are conducted to further validate key findings drawn from the questionnaire survey.

The empirical evidence collected from the survey and interviews confirms the general belief that external knowledge is of prime importance for SMEs, and demonstrates that SMEs have very strong needs for external knowledge and inter‐organizational knowledge transfer.

The findings provide very strong underpinning for further theoretical research on inter‐organizational knowledge transfer in SMEs. However, this study has certain limitations: its results may not be applicable to other industrial sectors or the same sector in other countries; or to micro or large companies; nor does it involve cross‐cultural issues.

By adopting a two‐tier research methodology, this study provides more reliable understanding and knowledge on SMEs' inter‐organizational knowledge transfer needs and practices, and fills the gap that exists in the empirical investigations on the subject.

This paper aims to propose a statistical and context-aware feature reduction algorithm that improves sentiment classification accuracy. Classification of reviews with different granularities in two classes of reviews with negative and positive polarities is among the objectives of sentiment analysis. One of the major issues in sentiment analysis is feature engineering while it severely affects time complexity and accuracy of sentiment classification.

In this paper, a feature reduction method is proposed that uses context-based knowledge as well as synset statistical knowledge. To do so, one-dimensional presentation proposed for SentiWordNet calculates statistical knowledge that involves polarity concentration and variation tendency for each synset. Feature reduction involves two phases. In the first phase, features that combine semantic and statistical similarity conditions are put in the same cluster. In the second phase, features are ranked and then the features which are given lower ranks are eliminated. The experiments are conducted by support vector machine (SVM), naive Bayes (NB), decision tree (DT) and k-nearest neighbors (KNN) algorithms to classify the vectors of the unigram and bigram features in two classes of positive or negative sentiments.

The results showed that the applied clustering algorithm reduces SentiWordNet synset to less than half which reduced the size of the feature vector by less than half. In addition, the accuracy of sentiment classification is improved by at least 1.5 per cent.

The presented feature reduction method is the first use of the synset clustering for feature reduction. In this paper features reduction algorithm, first aggregates the similar features into clusters then eliminates unsatisfactory cluster.

The purpose of this paper is to present an ontology‐based approach of context‐sensitive computing for the optimization of flexible manufacturing systems (FMS).

A context‐sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology‐based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA‐based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported.

Continuous improvement of the factory can be enhanced utilizing context‐sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.

The performance of context‐sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade‐off should be in between the extent of context processing and the required outcome of the support applications.

This paper includes the practical implications of context‐sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS.

Reported results provide a modular approach of context‐sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

A challenging task for context‐aware pervasive systems is reasoning about context in uncertain environments where sensors can be inaccurate or unreliable and inferred situations ambiguous and uncertain. This paper aims to address this grand challenge, with research in context awareness to provide feasible solutions by means of theoretical models, algorithms and reasoning approaches.

This paper proposes a theoretical model about context and a set of context verification procedures, built over the model and implemented in a context reasoning engine prototype. The verification procedures utilize beneficial characteristics of spatial representation of context and also provide guidelines based on heuristics that lead to resolution of conflicts arising due to context uncertainty. The engine's reasoning process is presented and it is shown how the proposed modeling and verification approach contributes in tackling the uncertainty associated with the reasoning task. The paper experimentally evaluates this approach with a distributed simulation of a sensor‐based office environment with unreliable and inaccurate sensors.

Important features of the model are dynamic aspects of context, such as context trajectory and stability of a pervasive system in given context. These can also be used for context verification as well as for context prediction. The model strength is also in its generality and its ability to model a variety of context‐aware scenarios comprising different types of information.

The paper describes a theoretical model for context and shows it is useful not only for context representation but also for developing reasoning and verification techniques for uncertain context.

Query-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary.

This approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered.

The proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction.

The interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.