• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.229-239
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• 2023

This study aims to explore the impact of algorithmic price discrimination on negative word-of-mouth (NWOM) through the lens of attribution theory. It also examines the mediating roles of intentional attributions and negative emotions, as well as the moderating effect of price sensitivity. For this study, 772 consumers who had purchased flight tickets completed a questionnaire survey, and the collected data were analyzed and tested using SPSS 27.0 and AMOS 24.0 software. The research findings reveal that algorithmic price discrimination has a significant positive impact on intentional attributions, negative emotions, and NWOM. Specifically, deliberate attributions and negative emotions mediate the relationship between algorithmic price discrimination and NWOM, while price sensitivity positively moderates the relationship between negative emotions and NWOM. Therefore, companies should consider disclosing algorithm details transparently in their marketing strategies to mitigate consumers' negative emotions and implement targeted strategies for consumers with different levels of price sensitivity to enhance positive word-of-mouth.

• Knowledge Management Research
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• v.25 no.3
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• pp.23-45
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• 2024

The enhancement of algorithmic management within platform companies and the expanding recognition of the 'employee status' of platform workers in various areas indicate a need for platform companies to broaden their comprehension of platform workers' job attitudes as an internal human resource. This study aims to explore variations in platform workers' job attitudes based on their work values in light of the evolving landscape of algorithmic management. Specifically, the research delves into discerning differences in platform workers' perceptions of job attitudes in relation to their work values. A multivariate analysis of variance(MANOVA) was employed in examining survey data from 177 riders affiliated with a delivery platform that implemented algorithmic management. The findings revealed that platform workers who prioritize work value (job interest), exhibited higher levels of perceived fairness in compensation and autonomy. Moreover, those who placed greater importance on work value (economic compensation), demonstrated elevated levels of work overload, perceived fairness in compensation, job burnout, and intentions to seek alternative employment opportunities.

• Journal of the Korean Chemical Society
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• v.55 no.6
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• pp.1042-1055
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• 2011

The purpose of this study was to compare the conceptual and algorithmic problem solving ability on Boyle's law and Charles's law according to cognitive levels and characteristics of students in middle school 1st grade students. For this study, questionnaire items of conceptual and algorithmic problem solving ability were developed. and the problem solving ability according to cognitive levels and characteristics of students was compared. The long-term memory effect in conceptual and algorithmic problem solving ability according to cognitive levels was investigated, and problem solving process were analyzed by questionnaire items. In the results of this study, conceptual problem solving ability was higher than algorithmic problem solving ability in all cognitive levels. There was statistically significant difference in concrete operational period and transitional period students. In comparison of the long-term memory effect in conceptual and algorithmic problem solving ability, formal operational period students had the long-term memory effect. There was no statistically significant difference in the conceptual and algorithmic problem solving ability according to private education among the characteristics of students. But there was statistically significant difference in the problem solving ability according to experiences of the scientific activities and hopes to related scientific careers. From results of analysis of problem solving process, it is known that the students had a tendency to just remember macroscopic phenomena and to solve the problems without understanding the concepts. Therefore, teaching and learning strategy is necessary to replace unscientific concepts by the scientific concepts through identifying students's unscientific concepts in advance.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1587-1592
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• 2003

There are three kinds of singularity in controlling redundant manipulators. Kinematic, algorithmic and representation singularities are those. If manipulators fall into any singularity without proper action to avoid it, the control system must go away from our desire, and we can meet a dangerous situation. Hence, we have to deal the singularities very carefully. In this paper, we describe an on-line solution for avoiding the occurrence of both algorithmic and kinematic singularities in task-priority based kinematic controllers of robotic manipulators. Representation singularity can be easily avoided by using proper representation algorithm, so, in this paper, we only consider kinematic and algorithmic singularities. The proposed approach uses a desired task reconstruction and a successive task projection in order to maintain the measure for singularity over a user defined minimum value. It shows a gain in performance and a better task error especially when working in proximity of singular configurations. It is particularly suitable for autonomous systems where an off-line trajectory control scheme is often not applicable. The advantage and performance of the proposed controller is verified by simulation works. And, the experiment with real manipulator is remaining for the future works.

• Journal of The Korean Association of Information Education
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• v.14 no.2
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• pp.189-197
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• 2010

This paper proposes elementary robot programming problems using the algorithmic thinking-based problem model with material factors in the elementary robot programming. And this paper proves the validity of developed robot programming problems in defining them as algorithmic thinking-based problems through experiments. The experimental results are analyzed in views of variety and effectiveness evaluation of answer algorithms and suitability of allocating degrees of difficulties to the developed robot programming problems. As a result of the experiment, we find that the developed problems has various answer algorithms and suitable degrees of difficulties for elementary school students.

• Journal of The Korean Association For Science Education
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• v.16 no.3
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• pp.278-285
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• 1996

High school students' achievements in algorithmic problems, and figure-formatted and textual-formatted conceptual problems concerning stoichiometry, gaseous state, and solution, were measured by the Chemistry Problem Solving Ability Test. The relationships among the achievement scores in the three types of problems and cognitive variables such as logical thinking ability, mental capacity, and field dependence/field independence were examined. The portion of variance of explanation for each achievement score was also studied by a multiple regression analysis. The results showed that logical thinking ability was significantly correlated with the achievement score in the algorithmic problems, and accounted for the significant portion of the variance of the score. Mental capacity accounted for the significant portion of the variance of the score in the figure-formatted conceptual problems. Although field dependence/field independence was significantly correlated with all the achievement scores, it did not significantly account for any scores in multiple regression analyses. However, the magnitudes of correlation coefficients among the achievement scores were higher than those between the achievement scores and cognitive variables. The best predictor for each score was also found to be one of the other achievement scores. Educational implications are discussed.

• School Mathematics
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• v.18 no.2
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• pp.257-275
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• 2016

The purpose of this study was to investigate the impact of multiple representations on students' understanding of fractions, decimals, and percent. The instructional approach integrating multiple representations was compared to traditional algorithmic instruction, a form of direct instruction. To examine and compare the impact of multiple representations instruction with traditional algorithmic instruction, pre and post tests consisting of five similar items were administered with 87 middle school students. Students' scores in these two tests and their problem solving processes were analyzed quantitatively and qualitatively. The quantitative results indicated that students taught by traditional algorithmic instruction showed higher scores on the post-test than students in the multiple representations group. Furthermore, findings suggest that instruction using multiple representations does not guarantee a positive impact on students' understanding of mathematical concepts. Qualitative results suggest that the limited use of multiple representations during a class may have hindered students from applying their use in novel problem situations. Therefore, when using multiple representations, teachers should employ more diverse examples and practice with multiple representations to help students to use them without error.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1989.04a
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• pp.71-76
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• 1989

Structural design, generally engineering design, is a complex process combinding design knowledges and analysis techniques. While analysis techniques can be automated in an algorithmic fashion, relatively little work has been done in the area of the design automation. An effect approach method for the automation of the engineering design may be a hybrid system, in which design knowledges, specification requirements and interpretations are represented using an expert system methodology and numerically intensive operations of the design process are implemented using an algorithmic language such as FORTRAN. The purpose of this paper is concentrated on the knowledge of K-CLIPS(KAIST-C Language Integrated Production System) used to design and implement this hybrid system. In K-CLIPS, some representation methods : frame, production rule, fact and user defined function are used to construct the knowledge base. The hierarchical knowledges could be expressed more naturally with a little number of rules than other plain production systems.

• The Mathematical Education
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• v.51 no.2
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• pp.95-114
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• 2012

This study examines the use of ill-structured problem to help the 4th graders' problem solving and reasoning. It appears that children with good understanding of problem situation tend to accept the situation as itself rather than just as texts and produce various results with extraction of meaningful variables from situation. In addition, children with better understanding of problem situation show AR (algorithmic reasoning) and CR (creative reasoning) while children with poor understanding of problem situation show just AR (algorithmic reasoning) on their reasoning type.