Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model

Chou, Jui‐Sheng; Cheng, Min‐Yuan; Wu, Yuwei; Wu, Cheng-Chieh

doi:10.1016/j.ijproman.2012.02.003

Cited by 16 publications

(10 citation statements)

References 56 publications

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“…Furthermore, customers’ satisfaction was mainly result of their confirmations (Roca et al , 2006). Chou et al (2012) obtained the same results regarding individual differences every time by using enterprise resource planning (ERP). PU is also a significant factor affecting customers’ satisfaction and intention to purchase or repurchase (Kim, 2010).…”

Section: Introductionmentioning

confidence: 77%

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Building the long bridge between visitors and customers through online general reviews

Tsao

2019

OIR

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Purpose Many reviews on the internet are contributed their time to evaluate which are largely transparent generally; however, How are these information valuable and useful to visitors before making a purchasing decision? As the reason, there is a need to understand the distal path of visitors concern. The purpose of this paper is to evaluate the effects of both perceived useful and valuable motivations on visitors’ willingness to buy in the distal paths. Design/methodology/approach Web questionnaire survey. Findings Visitors’ confirmation was the beginning to impact the intention to buy through only one distal path, perceived value (PV) and satisfaction. Research limitations/implications Even though the author tried his best to design and implement thoughtfully for this research, there are still some limitations. First, the results could be influenced by self-selection bias. The sample of respondents surveyed in this study consisted of young adults (58.90 percent were between the ages 21 and 25 years old), the majority of whom (54.79 percent) were students. Therefore, in the context of this study, the visitors surveyed were young and able to easily access information online. The phenomenon has been, proved by Hsu (2013), found, it still needs to explore further. Second, perceived usefulness did not have a significant impact on satisfaction which consistent with Tao et al. (2009) result. The data represent the response’ feeling for now; it needs to be explored further. Third, it would also be worthwhile to investigate any obstacles, such as believable (Cheung et al., 2012) that might reduce the intentions to buy of visitors. Finally, recommendations and promotions have also been found to influence visitors’ further purchasing behavior, such as extra buy (Tsao, 2013). These phenomena are worth investigating in future research. Practical implications To inspire visitors, information of reviews can be posted by offering positive or negative suggestions (e.g. good or bad experience); managers should improve their products or services from the suggestions of reviews and need to consider the distinctive influences of various aspects of reviews when promoting products and devising e-marketing strategies. Marketers should recruit and filter reviews to write positive suggestions of their own products or service. IS practitioners should post the reviews might need to provide more detailed information (e.g. the reasons of like or dislike). The posted information from reviews should be accurate, particularly aiming at visitors who are motivated by reviews’ suggestions. All proposed measures are particularly important for visitors with sufficient reviews effects. Greater transparency of reviews might be achieved by more explicit reviews information (e.g. via like or dislike statements). Social implications The result provides a basis to generate concrete advice for owners about reviews concern to enhance visitors’ PV. It seems sensible to pursue two strategies: first, enhance the visitors’ PV to trigger motivation forward, and second, inspire the visitors to progress smoothly in the visiting stage. Originality/value The results of the expectation-confirmation theory applied to reviews recommendations how important to employ for enterprises. These reviews were either directly given by experienced users or at least validated with their help to visitors. Therefore, the value is building the distal bridge between visitors and customers through online general reviews.

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Section: Introductionmentioning

confidence: 77%

“…ECT has been employed widely in information-related domains (Thong et al , 2006; Roca et al , 2006; Liao et al , 2009), such as online brokerage (Bhattacherjee, 2001), e-learning system (Lee, 2010) and ERP (Chou et al , 2012). The importance of ECT is growing.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Building the long bridge between visitors and customers through online general reviews

Tsao

2019

OIR

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“…The third and final heuristic appraisal tests ML leverage for selection optimization. Others [97][98][99][100] have developed the Less Square project risk evaluation model (LS-SVM). The simulation shows that the anticipated SVM outcome is successful.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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“…Machine Learning (ML)-based methods for building prediction models have attracted abundant scientific attention and are extensively used in industrial engineering [1][2][3], design optimization of electromagnetic devices, and other areas [4,5]. The ML-based methods have been confirmed to be effective for solving real-world engineering problems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

2021

Self Cite

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Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

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Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model

Cited by 16 publications

References 56 publications

Building the long bridge between visitors and customers through online general reviews

Building the long bridge between visitors and customers through online general reviews

Software Project Management Using Machine Learning Technique—A Review

Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

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