Interpersonal relations — a theory of interdependence

Even if m-learning is spreading rapidly in many regions of the world, research addressing the driving factors of m-learning adoption is in short supply Built on the Technology Acceptance Model, this paper proposes a hypothesized model of m-learning adoption Employing structural equation modeling technology, the model was assessed based on the data collected from 230 participants using a survey questionnaire Results indicate that perceived near-term/long-term usefulness and personal innovativeness have significant influence on m-learning adoption intention, while perceived long-term usefulness significantly affects the perceived near-term usefulness Personal innovativeness is a predictor of both the perceived ease of use and perceived long-term usefulness as well Of all variables, the perceived long-term usefulness contributes to the most influential predictor of m-learning adoption The model accounts for approximately 608% of the variance of behavioural intention The results indicted that offing high-quality contents complying with students' future targets is key to the success of m-learning in China Both theoretical and practical implications are discussed

Factors driving the adoption of m-learning: An empirical study

Ease of use and usefulness are believed to be fundamental in determining the acceptance and use of various, corporate ITs. These beliefs, however, may not explain the user’s behavior toward newly emerging ITs, such as the World-Wide-Web (WWW). In this study, we introduce playfulness as a new factor that reflects the user’s intrinsic belief in WWW acceptance. Using it as an intrinsic motivation factor, we extend and empirically validate the Technology Acceptance Model (TAM) for the WWW context. # 2001 Elsevier Science B.V. All rights reserved.

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Beyond the Usefulness and Ease of Use : Extending the TAM for a World - Wide - Web Context

Predicting the drivers of behavioral intention to use mobile learning

This paper presents a new set of features for land-line customer churn prediction, including 2 six-month Henley segmentation, precise 4-month call details, line information, bill and payment information, account information, demographic profiles, service orders, complain information, etc. Then the seven prediction techniques (Logistic Regressions, Linear Classifications, Naive Bayes, Decision Trees, Multilayer Perceptron Neural Networks, Support Vector Machines and the Evolutionary Data Mining Algorithm) are applied in customer churn as predictors, based on the new features. Finally, the comparative experiments were carried out to evaluate the new feature set and the seven modelling techniques for customer churn prediction. The experimental results show that the new features with the six modelling techniques are more effective than the existing ones for customer churn prediction in the telecommunication service field.

Customer churn prediction in telecommunications

Mobile data services are entering into all aspects of people's life and are expected to be an important revenue source of telecom carriers. Nevertheless, the acceptance pace of mobile data services is slower than the expected level projected by the telecommunication industry. This research investigated the reason that people use mobile data services in China. Combined with subscribers' experience factors, an extended technology acceptance model (TAM) for mobile data services was presented. The model was tested using data collected from 802 mobile subscribers. The findings of the study indicate that mobile voice service and innovation experience of mobile data services affect subscribers' consumption intention greatly; subscribers' perceived ease-of-use and brand experience influence subscribers' attitude towards mobile data services largely. Promotion of mobile data services is most effective when it is promoted with perfect voice service experience. Additionally, our findings suggest that mobile service carriers need to enhance their investments in mobile data services' R&D. Collaboration among the value chain members of mobile data services will promise cutting-edge new products and services. Using system methodology, a mobile data service infusion system model was constructed to explain the general adoption rules of mobile data services. The adoption of mobile data services in mobile logistics area was also discussed. Copyright © 2009 John Wiley & Sons, Ltd.

An extension of technology acceptance model: Analysis of the adoption of mobile data services in China

Review of relationships among variables in TAM

Only with subscribers' contractual records and bill details in the investigated fixed-line services provider, we construct several churn prediction models, applying different data mining technologies. Comparing the models with predictors of duration of service use, payment type, amount and structure of monthly service fees and changes of the monthly service fees, we find that duration of service use is the most predictive variable. Then payment type and other predictors of amount and structure of monthly service fees, especially the predictors within the latest 3 months, are also effectual. We then build different decision tree models by reducing the amounts of monthly bill details for predicting, to find out how the prediction performance will evolves. The result shows that with reduction of early monthly data, the model performance declines slightly. However, the processing data size and runtime of models decrease significantly. Thus, we suggest that using relatively fewer but latest monthly data to detect likely churner would be both effective and efficient.

Case Study on CRM: Detecting Likely Churners with Limited Information of Fixed-line Subscriber

This chapter proposes an integrated methodological system of telecommunication customer detainment management, including telecommunication customer churn prediction and strategy formulation of customer detainment management. The formulation of churn customer detainment management strategy includes customer detainment value assessment, customer detainment level determination, enterpriseattribution approach based on customer detainment strategy analysis, evaluation and implementation, and so forth. Future research in this field is discussed at the end of this chapter.

Telecommunication Customer Detainment Management

Integration of different theories and expansion of research areas are the main trends in the research domain of IS adoption. Classical TAM structure has been largely expended by newly added variables. Prior studies [1] have analyzed relationships among variables in TAM and found the stability of classical structure, but what about relationships between new variables and classical structure? We selected 30 articles from the main international journals for analyses. It is found that, SE, SN and PBC are used mostly in extended TAM. The relations between SE, PBC and TAM are consistently significant, but the integration of SN into TAM is not so ideal. In our review scale, this relation is inconsistent. Other variables and relations are also discussed in this article. The conclusions of this article will provide guidance for future researches about extended TAM model building.

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Yuanquan Li

Papers

An extension of technology acceptance model: Analysis of the adoption of mobile data services in China

Review of relationships among variables in TAM

Case Study on CRM: Detecting Likely Churners with Limited Information of Fixed-line Subscriber

Telecommunication Customer Detainment Management

A Review on the Relationship Between New Variables and Classical TAM Structure