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Journal ArticleDOI

An integrated approach to renew software contract using machine learning.

02 Jan 2021-Vol. 4, Iss: 1, pp 14-25

AbstractContract renewal is critical to maintaining a company’s recurring revenue source. Therefore, there is a significant emphasis on setting up an efficient process for renewal. In this study, a machine...

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Journal Article
Abstract: The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single chapter cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

2,533 citations

Journal ArticleDOI
TL;DR: This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.
Abstract: Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

788 citations

Journal ArticleDOI
Abstract: This paper empirically studies determinants of decision by companies to offshore innovation activities. It uses survey data from the international Offshoring Research Network project to estimate the impact of managerial intentionality, past experience, and environmental factors on the probability of offshoring innovation projects. The results show that the emerging shortage of highly skilled science and engineering talent in the US and, more generally, the need to access qualified personnel are important explanatory factors for offshoring innovation decisions. Moreover, contrary to drivers of many other functions, labor arbitrage is less important than other forms of cost savings. The paper concludes with a discussion of the changing dynamics underlying offshoring of innovation activities, suggesting that companies are entering a global race for talent.

718 citations

Journal ArticleDOI
Abstract: Recently, researchers have paid increasing attention to the marketing strategy of customization. A key assumption is that customized products create higher benefits for customers than standard products because they deliver a closer preference fit. The prerequisite for this effect is the ability to obtain precise information on what customers actually want. But are customers able to specify their preferences that precisely? Several theoretical arguments raise doubts about this, implicitly challenging the value of customization. The authors conduct two studies in which they find that products customized on the basis of expressed preferences bring about significantly higher benefits for customers in terms of willingness to pay, purchase intention, and attitude toward the product than standard products. The benefit gain is higher if customers have (1) better insight into their own preferences, (2) a better ability to express their preferences, and (3) greater product involvement. This suggests that c...

497 citations

Journal ArticleDOI
TL;DR: This study compares various data mining techniques that can assign a ‘propensity-to-churn’ score periodically to each subscriber of a mobile operator and indicates that both decision tree and neural network techniques can deliver accurate churn prediction models.
Abstract: Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a ‘propensity-to-churn’ score periodically to each subscriber of a mobile operator. The results indicate that both decision tree and neural network techniques can deliver accurate churn prediction models by using customer demographics, billing information, contract/service status, call detail records, and service change log.

414 citations