- Informacje o czasopiśmie
- Pierwsze wydanie
- 30 Mar 2017
- Częstotliwość wydawania
- 4 razy w roku
- Otwarty dostęp
Zakres stron: 78 - 104
This research attempts to examine the relationship between B2C interaction and customer loyalty in Business-to-Customer (B2C) context from a new perspective of the interactive tool.
The scale for B2C interactive tools is of seven dimensions: efficiency, security, fulfillment, mobility, community, cultivation, and customization. A model reflecting the influences of these attributes on customer loyalty is developed and empirically examined based on data collected from 265 B2C customers.
Results reveal that the fulfillment, mobility, community, and customization of B2C interactive tools can enhance customer loyalty directly and significantly. Efficiency and security, serving as the premise for possible purchase behavior, facilitate fulfillment. In addition, cultivation promotes the formation of customization, which directly strengthens customer loyalty.
Models considering individual-level indicators and combined with classic loyalty mechanisms in B2C context may lead to a deeper understanding of the tested effects of interaction on customer loyalty.
To strengthen B2C interaction and further cultivate loyal customers, making interactive tools more fundamental, flexible, and personalized is critical for B2C enterprises.
This study proposes a new perspective from interactive tools when measuring the relationship between B2C interaction and customer loyalty, and offers a useful theoretical lens and reasonable explanations for investigating customer loyalty in B2C e-commerce context.
- B2C e-commerce
- Interactive tool
- Customer loyalty
- Otwarty dostęp
Is Participating in MOOC Forums Important for Students? A Data-driven Study from the Perspective of the Supernetwork
Zakres stron: 62 - 77
Compared with traditional course materials used in the classroom, the massive open online course (MOOC) forum that delivers unlimited learning content to students has various advantages. Yet MOOC has also received criticism recently, notably the problem of extremely low participation rates in its discussion forums. This study aims to explore the correlation between forum activity and student course grade in MOOC, and identify more accurately the forum activity levels of participants and the quality of threads in MOOC.
We crawled students’ tests, final exams, exercises, discussions performance data and total scores from a course in Chinese College MOOC from May 2014 to August 2014. And we use the data to analyze the correlation between Forum Participation and Course Performance based on nonparametric tests as well as multiple linear regressions with the software of R. The study provides definitions and algorithms of super degrees based on the supernetwork model to help find high-quality threads and active participants.
A positive correlation between forum activity and course grade is found in this study. Students who participate in the forum have better performance than those who do not. Using the definitions and algorithms of super degrees in the supernetwork, forum activity levels of participants as well as the quality of threads they employ are identified.
Only limited representative forum participants and threads are used to analyze the activity level and significance of the MOOC forum. Also, the study only investigates one Chinese course on information retrieval. More data and more data sources could be helpful in better understanding the MOOC forum phenomenon.
As super degrees can reveal more latent information and recognize high-quality threads as well as active participants, these parameters can be used to assess needs to improve forum settings and alleviate the problem of low forum participation. The proposed super degrees can be applied in social network domains for further research.
Definitions and algorithms of super degrees are provided and used for forum analysis. Super degrees can be applied to find high-quality threads and active participants, which is beneficial to guide students to participate in these high-quality threads and have a better understanding of knowledge MOOC provides.
- Super degrees
- Otwarty dostęp
A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory
Zakres stron: 38 - 61
This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.
The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.
Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.
In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.
Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.
Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.
- Institutions collaboration
- Collaboration network
- Innovation chain
- Industrial chain
- Industry-university-research institutions
- Otwarty dostęp
Zakres stron: 20 - 37
This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles.
Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys.
In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-WordNet and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey quality.
The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing.
CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain field.
CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.
- Automatic survey system
- Citation content
- Clustering algorithms
- Label generation approaches
- Sentence extraction methods
- Otwarty dostęp
Zakres stron: 1 - 19
The main goal of this study is to discover the scientific evolution of Cancer-Related Symptoms in Complementary and Alternative Medicine research area, analyzing the articles indexed in the Web of Science database from 1980 to 2013.
A co-word science mapping analysis is performed under a longitudinal framework (1980 to 2013). The documental corpus is divided into two subperiods, 1980–2008 and 2009–2013. Thus, the performance and impact rates, and conceptual evolution of the research field are shown.
According to the results, the co-word analysis allows us to identify 12 main thematic areas in this emerging research field: anxiety, survivors and palliative care, meditation, treatment, symptoms and cancer types, postmenopause, cancer pain, low back pain, herbal medicine, children, depression and insomnia, inflammation mediators, and lymphedema. The different research lines are identified according to the main thematic areas, centered fundamentally on anxiety and suffering prevention. The scientific community can use this information to identify where the interest is focused and make decisions in different ways.
Several limitations can be addressed: 1) some of the Complementary and Alternative Medicine therapies may not have been included; 2) only the documents indexed in Web of Science are analyzed; and 3) the thematic areas detected could change if another dataset was considered.
The results obtained in the present study could be considered as an evidence-based framework in which future studies could be built.
Currently, there are no studies that show the thematic evolution of this research area.
- Cancer-related symptoms
- Complementary and Alternative Medicine
- Science mapping analysis
- Thematic evolution