THE INFLUENCE OF CUSTOMERS RESISTANCE INNOVATION TOWARDS CUSTOMERS ATTRITION BASED ON FSQCA: A CASE STUDY OF SMARTPHONE APP

Minyi Chen,Yang Sun,Helen (Huifen) Cai,Cheng Hua,Jiaolong Zhang 글로벌지식마케팅경영학회 2018 Global Marketing Conference Vol.2018 No.07

Introduction The study chooses smartphone applications which have update experience as the study case to analysis if it will cause customer attrition. Application updates aims to make up the bugs which may cause bad effects on using, while in fact, most changes will be resisted by customers. We adapt three elements to collect data about the attitude of customers toward applications update, the conclusion illustrates, customers will resist changes in some conditions and cause customers attrition. Therefore, application developers should focus on the personal emotional reaction and cognitive evolution as well as predict what may influence the customers normal use after the applications update to make correct update decisions. In order to define the relationship between variable and result., we take smartphone application which had experienced update as examples and choose fsQCA as the methodology to analysis. Literature Review Resistance to change is a psychological process, Brehm(1966)claims customers resist to change origins from their believe that they have rights to control free, when this rights be challenged, this resistance behavior will happen due to they want to protect their own rights. Some scholars distinguished the concept of resistance to change: when receptors in the low involvement or limited cognize, their resistance behaviors is likely to be considered as the passive resistance; otherwise, the behaviors would be recognized as active resistance (Nabih,1997). Bagozz and Lee(1999) think that functional barrier is an important factors which influence the degree of innovation receptance, when customers think products after update cannot meet their function needs will cause resistance to change. Resistance always occur passively, bemuse of the disturb of the former habits of using (Ram &Sheth 1989), and former studies also indicate that prejudice towards changes is one of significant factors. Because changes are uncertainty, Fiske and Taylor(1991)claims that uncertainty can make people hate changes and Whitson and Galinsky(2008)consider aversion will be the strong driver of resistance to change to reduce uncertainty and risk. In terms of a company, the cost of maintaining a old customer is much lower than these of develop a new user, Actually, customer attrition is response of entrepreneur revenue and the vital section in development of companies. The famous american honesty management scholar Frederic Reichheld’s research indicates that when the extension of the commercial relationship of entrepreneur and customers, companies can acquire fatter profit through the relationship. Conclusions This study chooses fsQCA as methodology to define the relationship between resistance to change and customer attrition instead of using structural equation modeling. Sometimes, even customers have right cognitive evolution towards changes, when they find their efficiency be influenced by it, they also resist to change; while adverse emotional reaction also has an adverse effect on customer retention though they build positive cognitive evolution about changes. there is a remain circumstance of lossing of customers that when customers hate changes and they find their efficiency be influenced by update, the honesty of this product will also decrease. This three condition all contribute to customers attrition which should be paid more attention by companies.

Assist Your Study at Home: Design, Implementation and Evaluation of the ULS System

Mianxiong Dong,Kaoru Ota,Feilong Tang,Minyi Guo,Zixue Cheng 보안공학연구지원센터 2008 International Journal of Smart Home Vol.2 No.1

With the great progress of technologies, computers are embedded into everywhere to make our daily life convenient, efficient and comfortable. Further more, most of them are small, powerful, intelligent and invisible. In a ubiquitous computing environment, support methods for learning are more effective than some traditional ways, such as WBT (Web-Based Training) and e-learning. In this paper, we proposed a support method which aims to encourage a learner to acquire his/her learning habit based on Behavior Analysis. The supports to the learner are provided through a scheduler system, a Ubiquitous Learning Scheduler (ULS). In our design, the learner’s situations are collected by sensors and analyzed by comparing them to his/her learning histories. Based on this information, supports are provided to the learner in order to help him/her forming a good learning style. Our Research results revealed that ULS not only benefited learners to acquire their learning habits but also improved their self-directed learning styles.

Intelligence@Lab: A Smart Space for Researchers Based on Sensor Networks in Pervasive Computing Environment

Mianxiong Dong,Kaoru Ota,Feilong Tang,Minyi Guo,Zixue Cheng 보안공학연구지원센터 2008 International Journal of Smart Home Vol.2 No.3

With the rapid progress on the Internet Technology, many projects have been proposed in terms of remote education system such as WBT (Web-Based Training) and E-learning. Such systems can help learners for studying by themselves based on their own learning history in a virtual world. However, learners usually spend time in the real world during studying, exercising, and the other activities. It seems the systems do not give enough support to learners in the real world. Ubiquitous technologies make it possible to provide services to learners anytime and anywhere in the real world. It means learners can receive more meticulous, individually-targeted support in ubiquitous environments. In this paper, we propose a ubiquitous environment to support college students, professors, and visitors in a laboratory to encourage positive research activities. Actions of people in the Ubiquitous Laboratory (U-Lab) are individually recorded by sensor networks and analyzed to provide suitable supports. Based on these collected information, U-Lab provides services such as to grasp a precise research progress and share research information among students and professors. We also propose a method to advise students to improve their research activities.

Erratum: Analysis of Tau Protein Expression in Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Different Molecular Subtypes of Breast Cancer

Chuqian Lei,Ciqiu Yang,Bin Xia,Fei Ji,Yi Zhang,Hongfei Gao,Qianqian Xiong,Yufeng Lin,Xiaosheng Zhuang,Liulu Zhang,Teng Zhu,Minyi Cheng,Mei Yang,Kun Wang 한국유방암학회 2020 Journal of breast cancer Vol.23 No.2

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Analysis of Tau Protein Expression in Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Different Molecular Subtypes of Breast Cancer

Chuqian Lei,Ciqiu Yang,Bin Xia,Fei Ji,Yi Zhang,Hongfei Gao,Qianqian Xiong,Yufeng Lin,Xiaosheng Zhuang,Liulu Zhang,Teng Zhu,Minyi Cheng,Mei Yang,Kun Wang 한국유방암학회 2020 Journal of breast cancer Vol.23 No.1

Purpose: Tau is a microtubule-associated protein that can be found in both normal and abnormal breast cells. Whether the expression of Tau protein can predict the response to neoadjuvant chemotherapy (NACT) is still unclear. In this study, we assessed the role of Tau protein expression in predicting a pathological complete response (pCR) to NACT for different subtypes of breast cancer. Methods: Four hundred and sixty-eight eligible patients were retrospectively recruited in our study. The relationship between clinicopathologic factors, including Tau protein expression, and pCR in different subtypes was evaluated using logistic regression analysis. Correlation between Tau and disease-free survival (DFS) and overall survival (OS) was performed using Kaplan–Meier analysis. Results: The expression of Tau protein was negatively correlated with pCR, especially in triple-negative breast cancer (TNBC). No significant difference was observed in the luminal human epidermal growth factor receptor-2 (HER2)-negative subtype and HER2-positive subtype. Patients with pCR were associated with better DFS and OS (p < 0.05). However, Tau protein expression had no association with either DFS or OS (p > 0.05). Conclusion: Tau protein expression can predict pCR before NACT in TNBC, but there was no correlation between Tau expression and DFS or OS.