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The purpose of this paper is to examine the evolution of collaboration among researchers in Library Hi Tech based on the co-authorship network analysis.

The Library Hi Tech publications were retrieved from Web of Science database between 2006 and 2017. Social network analysis based on co-authorship was analyzed by using BibExcel software and a visual knowledge map was generated by Pajek. Three important social capital indicators: degree centrality, closeness centrality and betweenness centrality were calculated to indicate the co-authorship. Cohesive subgroup analysis which includes components and k-core was then applied to show the connectivity of co-authorship network of Library Hi Tech.

The results indicated that around 42 percent of the articles were written by single author, while an increasing trend of multi-authored articles suggesting the collaboration among researchers in librarian research field becomes popular. Furthermore, the social network analysis identified authorship network with three core authors – Markey, K., Fourie, I. and Li, X. Finally, six core subgroups each included six or seven tightly connected researchers were also identified.

This study contributed to the existing literature by revealing the co-authorship network in librarian research field. Key researchers in the major subgroup were identified. This is one of the limited studies that describe the collaboration network among authors from different perspectives showing a more comprehensive co-authorship network.

The purpose of this paper is to present the knowledge structure based on the articles published in Library Hi Tech. The research hotspots are expected to be revealed through the keyword co-occurrence and social network analysis.

Data sets based on publications from Library Hi Tech covering the time period from 2006 to 2017 were extracted from Web of Science and developed as testbeds for evaluation of the CiteSpace system. Highly cited keywords were analyzed by CiteSpace which supports visual exploration with knowledge discovery in bibliographic databases.

The findings suggested that the percentage of publications in the USA, Germany, China, and Canada are high. Further, the most popular keywords identified in Library Hi Tech were: “service,” “technology,” “digital library,” “university library,” and “academic library.” Finally, four research issues were identified based on the most-cited articles in Library Hi Tech.

While keyword plays an important role in scientific research, limited studies paid attention to the keyword analysis in librarian research. The contribution of this study is to systematically explore the knowledge structure constructed by the keywords in Library Hi Tech.

The purpose of this study is to provide a nuanced understanding of the marketing placebo effect (MPE) of products with reduced sugar labeling and how it forms certain perceptual underpins (perceived healthiness (PH) and perceived tastiness (PT)), with the potential effect of product category and social class in consideration.

The proposed model is tested using a sample of 822 participants by employing partial least squares structural equation modeling (PLS-SEM). Hypothetical relationships among MPE, PH, PT, purchase intention (PI) and social class are examined for both hedonic and utilitarian products.

The results highlight the positive role of MPE in leveraging consumer PI through the parallel mediation of PH and PT. The positive effect of MPE on PH and PT was more pronounced for the utilitarian product. In addition, social class negatively moderated the relationship between PH and PI only in the case of the utilitarian product.

This paper contributed to the MPE literature in the food industry by challenging the conventional intuition of “Unhealthy = Tasty” and highlighting the potential of perceived food healthiness to positively influence perceived food tastiness under the effect of MPE. An upper social class would attenuate the positive effect of perceived food healthiness on PI.

Nowadays, the agricultural business environment is expended to the whole world. Transaction records in point of sales and customer relationship management (CRM) systems can be large-scale data for long-established global chain businesses. Thus, the purpose of this paper is to using a proposed data mining approach to discover valuable markets/customers of urban coffee shop industry (retailer) in current environment of Taiwan, which can implement the industry's data-driven marketing strategy on a CRM system.

In this research approach, Ward's method, C5.0 decision tree and a proposed model are applied for discovering valuable markets and mining useful customer rules.

These found markets and discovered rules can be applied on marketing information or CRM system for identifying valuable customers and target markets.

In this study, the CRM system can be the media for the data-driven marketing strategy in environment of Taiwan. The approach of this research can be applied on other businesses for their data-driven marketing strategies as well.

Online customer relationship management (CRM) is an important issue for implementing digital marketing of electronic commerce or social commerce. The purpose of this study is to establish valuable markets for discovering customer knowledge from data-driven CRM systems for enhancing growth rates of businesses. Airline or travel agency industries are online businesses in the world. Therefore, the industries in Taiwan will be an empirical case for this study.

This research applied a procedure with an applied proposed model for establishing valuable markets from data-driven CRM systems. However, the study used a proposed customer value model (recency, frequency and monetary [RFM]; RFM model-based), the analytic hierarchy process (AHP) procedure and a proposed equation for estimating customer values.

For enhancing the data-driven CRM marketing of the industries, in this research, the market of air travelers can be partitioned into eight markets by the proposed model. As well, the markets can be ranked by the AHP procedure. Furthermore, the travelers’ customer values can be estimated by a proposed customer value equation.

Via the applied proposed procedure, online airlines, travel agencies or other online businesses can implement the research procedure as their data-driven marketing strategy on their online large-scale or Big Data customers’ databases for enhancing sales rates.

The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research.

Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems.

The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules.

However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

The position/force control of the robot needs the parameters of the impedance model and generates the desired position from the contact force in the environment. When the environment is unknown, learning algorithms are needed to estimate both the desired force and the parameters of the impedance model.

In this paper, the authors use reinforcement learning to learn only the desired force, then they use proportional-integral-derivative admittance control to generate the desired position. The results of the experiment are presented to verify their approach.

The position error is minimized without knowing the environment or the impedance parameters. Another advantage of this simplified position/force control is that the transformation of the Cartesian space to the joint space by inverse kinematics is avoided by the feedback control mechanism. The stability of the closed-loop system is proven.

The position error is minimized without knowing the environment or the impedance parameters. The stability of the closed-loop system is proven.