Applying SVM to build supplier evaluation model - comparing likert scale and fuzzy scale

Hsu, Chun F.; Chang, Betty; Hung, Hsu-Feng

doi:10.1109/ieem.2007.4419140

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…The superior performances of SVM can be seen in various applications such as pattern recognition, regression estimation, time series predication and text classification. Application of SVM in the context of business data mining can be seen only in the recent literatures such as analysis of marketing data [7], consumers' credit scoring [14], forecasting of demand load in supply chain [4], supplier evaluation [12,13,30,33], default prediction of SMEs [17], and bankruptcy prediction [35].…”

Section: Methodsmentioning

confidence: 99%

“…In the previous literatures, attributional features including financial data, product and service quality, technological capabilities, compatibility, and etc. are included in the analysis to evaluate suppliers [12,13,30,33] by introducing the implications of previous literatures in supply chain management [5,6,8,9,20,23,29,34]. Although supplier candidates are usually evaluated by quality, cost, delivery, it is hard to know them in advance before actual transactions is contracted and launched.…”

Section: Introductionmentioning

confidence: 99%

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Finding business partners and building reciprocal relationships - A machine learning approach

Mori

Kajikawa

Kashima

2011

First International Technology Management Conference

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Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers, customers and outsources and build reciprocal relationships among them, while it simultaneously offers many opportunities. In this contribution, we propose a new computational approach to find business partner candidates based on firm profiles and transactional relationships among them. We employ machine learning techniques to build prediction models of customer-supplier relationships. We applied our approach to Japanese firms and compared our prediction results with the actual business data. The results showed that our approach successfully found plausible candidates and reciprocity among them whose accuracy is about 80%. Using machine learning approach, we have the accuracy of predicting a customer-supplier relation of 84%, and the accuracy of predicting a reciprocal customer-supplier relation is about 75-79%. These results show that our approach can be a new powerful tool to develop one's own business in the complicated, specialized and rapidly changing business environments of recent years.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding business partners and building reciprocal relationships - A machine learning approach

Mori

Kajikawa

Kashima

2011

First International Technology Management Conference

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“…Chang and Yeh (2002) and Kurtulmusoglu et al (2016) developed fuzzy multi-criteria analysis model to measure service quality of websites of airline companies and Hu (2009) developed to measure service quality of travel websites. Hsu et al (2007) evaluated suppliers in supply chain by using fuzzy scale. adjusted fuzzy assessment method for field of marketing research.…”

Section: Fuzzy Likert Scalementioning

confidence: 99%

An Approximation of Trade Show Exhibitors’ Behavioral Intentions in Fuzzy Environment

Işın

Atalay

Alagöz

et al. 2019

International Journal of Health Management and Tourism

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The study has two objectives mainly to estimate exhibitors' satisfaction with selfperformance, the organizers, and visitors to a trade show, which determines the behavioral intentions of exhibitors, thus providing the organizers with feedback on the perceptions and behavioral intentions of exhibitors and to use the fuzzy Likert scale method to overcome the shortcomings of the Likert scale and enhance its use in the social sciences. The sample included 396 managers who had participated in international trade shows. Fuzzy logic was adopted using linguistic terms to evaluate the satisfaction and intentions of the exhibitors to construct the regression model. Variables relating to satisfaction with self-performance, the organizers, and visitors were found to have a positive effect on the behavioral intentions of the exhibitors. Using fuzzy logic, the discrete ordinal variable was transformed into a continuous variable and its semantic meaning was preserved.

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“…Mathematically proficient researchers opted for mathematical modeling to rescale ordinals into continuous intervals using methods such as Markov Chain Monte Carlo, Snell method, Fuzzy Logic, and Item Response Theory (IRT; Granberg-Rademacker, 2010;Harwell & Gatti, 2001;Hsu et al, 2007;Wu, 2007). Wu (2007) applied Snell's scaling procedure to convert data from 4-point and 6-point Likert scale to numerical scores.…”

Section: Approaches Offered By Researchersmentioning

confidence: 99%

Generation of an Interval Metric Scale to Measure Attitude

Yusoff

Janor

2014

Sage Open

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This article discusses issues of scales in measuring attitude, demonstrates how a metric scale can be generated based on three main features, and presents results from a repeated measurement survey to verify the generated scale. The design of the generated metric scale is introduced and named Ruler and Option (RO). The population for repeated measurement survey was 1,870 bachelor students from a public university. Two sets of questionnaire (identical items), one with 7-point Likert scale and another with RO scale, were distributed to a sample of 595 bachelor students chosen using stratified random sampling method. Data were analyzed descriptively using SPSS version 20 and structural equation modeling using AMOS version 21. Results showed that data from RO scale performed better than data from 7-point Likert scale in terms of number of items per construct, factor loadings, squared multiple correlations, higher ratio of degrees of freedom to number of parameters, and higher reliability coefficients. In terms of validity coefficients, measurement models from both data sets attained almost the same level of discriminant and construct validity. Further studies are recommended to elicit the strength and weakness of RO scale to identify the situations where it is most suitable.

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Applying SVM to build supplier evaluation model - comparing likert scale and fuzzy scale

Cited by 16 publications

References 25 publications

Finding business partners and building reciprocal relationships - A machine learning approach

Finding business partners and building reciprocal relationships - A machine learning approach

An Approximation of Trade Show Exhibitors’ Behavioral Intentions in Fuzzy Environment

Generation of an Interval Metric Scale to Measure Attitude

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