Scientometric Analysis of Technology & Innovation Management Literature

The management of technology and innovation has become an attractive and promising field within the management discipline. Therefore, much insight can be gained by reviewing the Technology & Innovation Management (TIM) research in leading TIM journals to identify and classify the key TIM issues by meta-categories and to identify the current trends. Based on a comprehensive scientometric analysis of 5,591 articles in 10 leading TIM specialty journals from 2005 to 2014, this research revealed several enlightening findings. First, the United States is the major producer of TIM research literature, and the greatest number of papers was published in Research Policy. Among the researchers in the field, M. Song is the most prolific author. Second, the TIM field often plays a bridging role in which the integration of ideas can be grouped into 10 clusters: innovation and firms, new product development (NPD) and marketing strategy, project management, patenting and industry, emerging technologies, science policy, social networks, system modeling and development, business strategy, and knowledge transfer. Third, the connectivity among these terms is highly clustered and a network-based perspective revealed that six new topic clusters are emerging: NPD, technology marketing, patents and intellectual property rights, university-industry cooperation, technology forecasting and roadmapping, and green innovation. Finally, chronological trend analysis of key terms indicates a change in emphasis in TIM research from information systems/technologies to the energy sector and green innovation. The results of the study improve our understanding of the structure of TIM as a field of practice and an academic discipline. This insight provides direction regarding future TIM research opportunities

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Sentiment Analysis of Spanish Words of Arabic Origin Related to Islam: A Social Network Analysis

With the arrival of Muslims in 711 till their expulsion in the 1600s, Arabic language was present in Spain for more than eight centuries. Although social networks have become a valuable resource for mining sentiments, there is no previous research investigating the layman’s sentiment towards Spanish words of Arabic etymology related to Islamic terminology. This study aim at analyzing Spanish words of Arabic origin related to Islam. A random sample of 4586 out of 45860 tweets was used to evaluate general sentiment towards some Spanish words of Arabic origin related to Islam. An expert-predefined Spanish lexicon of around 6800 seed adjectives was used to conduct the analysis. Results indicate a generally positive sentiment towards several Spanish words of Arabic etymology related to Islam. By implementing both a qualitative and quantitative methodology to analyze tweets’ sentiments towards Spanish words of Arabic etymology, this research adds breadth and depth to the debate over Arabic linguistic influence on Spanish vocabulary

Horse racing prediction using graph-based features.

This thesis presents an applied horse racing prediction using graph based features on a set of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph based features and with graph based features. This thesis can be explained in 4 main parts. Collect data from a horse racing website held from 2015 to 2017. Train data to using predictive models and make a prediction. Create a global directed graph of horses and extract graph-based features (Core Part) . Add graph based features to basic features and train to using same predictive models and check improvements prediction accuracy. Two random horses were picked that are in same races from data and tested in systems for prediction. With graph based features, prediction of accuracy better than without graph-based features. Also We tested this system on 2016 and 2017 Kentucky Derby. Even though we did not predict top three results from 2017 Kentucky Derby, in 2016 Kentucky Derby, we predicted top four position

Research Trends in Massive Open Online Course (MOOC) Theses and Dissertations: Surfing the Tsunami Wave

Massive Open Online Courses (MOOCs) have attracted a great deal of attention by higher education and private enterprises. MOOCs have evolved considerably since their emergence in 2008, all the while given rise to academic discussions on MOOC impact, design and reach. In an effort to understand MOOCs more comprehensively, this study analyzes theses and dissertations (N = 51) related to MOOCs and published between 2008 and 2015, identifying research trends from these academic documents. Theses and dissertations within this research scope were gathered through a comprehensive search in multiple academic databases. For the purposes of the study, the research employed a systematic review approach. In order to reveal trends in research themes, emphasize theoretical/conceptual backgrounds, research designs and models, first a document analysis was used to collect data and this was followed by a content analysis. Our research findings indicate that MOOC research is generally derived from education, engineering and computer science, as well as information and communication technology related disciplines. Qualitative methodology linked to a case study research model is most common, and the theoretical/conceptual backgrounds are usually distance education related. Remarkably, nearly half of the studies didn’t benefit from any theoretical or conceptual perspectives. In sum, this study presents an evaluation regarding research trends derived from MOOC theses and dissertations, and provides directions for future MOOC research

Approach and Preliminary Results for Early Growth Technology Analysis

Even experts cannot be fully aware of all the promising developments in broad and complex fields of technology, such as renewable energy. Fortunately, there exist many diverse sources of information that report new technological developments, such as journal publications, news stories, and blogs. However, the volume of data contained in these sources is enormous; it would be difficult for a human to read and digest all of this information - especially in a timely manner. This paper describes a novel application of technology mining techniques to these diverse information sources to study, visualize, and identify the evolution of promising new technologies - a challenge we call 'early growth technology analysis.' For the work reported herein, we use as inputs information about millions of published documents contained in sources such as SCIRCUS, Inspec, and Compendex. We accomplish this analysis through the use of bibliometric analysis, consisting of three key steps: 1. Extract related keywords (from keywords in articles) 2. Determine the annual occurrence frequencies of these keywords 3. Identify those exhibiting rapid growth, particularly if starting from a low base. To provide a focus for the experiments and subsequent discussions, a pilot study was conducted in the area of 'renewable energy,' though the techniques and methods developed are neutral to the domain of study. Preliminary results and conclusions from the case study are presented and are discussed in the context of the effectiveness of the proposed methodology

Predicting the Brand Popularity from the Brand Metadata

Social networks have become one of the primary sources of big data, where a variety of posts related to brands are liked, shared, and commented, which are collectively called as brand metadata. Due to the increased boom in E/M-commerce, buyers often refer the brand metadata as a valuable source of information to make their purchasing decision. From the literature study, we found that there are not many works on predicting the popularity of the brand based on the combination of brand metadata and comment’s thoughtfulness analysis. This paper proposes a novel framework to classify the comment’s as thoughtful favored or disfavored comment’s, and later combines them with the brand metadata to forecast the popularity of the brand in near future. The performance of the proposed framework is compared with some of the recent works w.r.t. thoughtful comment’s identification accuracy, execution time, prediction accuracy and prediction time, the results obtained are found to be very encouraging