Adopting technology by its intended users is one of the most important contributors to that technology's success. Therefore, the success of mobile learning (ML) depends on the students' acceptance of the method. Regarding this point, this quantitative research aims to identify factors that affect switching intention to adopt ML among university students in Indonesia based on migration theory, Push-Pull-Mooring (PPM) framework. A theoretical model was developed to examine the determinants that affect students' decision to use ML platforms. This study used an online survey questionnaire to obtain 616 valid responses. A comprehensive analysis of the influence factors of users switching behavior, including the moderating factors, was conducted using Structural Equation Modeling (SEM) and Amos software. The results confirmed that the push factor (learning convenience), pull factors (learning autonomy and enjoyment), and mooring factor (student innovativeness) are perceived as significant factors for accepting ML. Concerning the moderating factor, this study also revealed the significance of moderating factor experience in two causal effects of enjoyment and student innovativeness on the students' intention to switch using ML. Furthermore, based on the findings, several recommendations were suggested for the university policy-makers to develop effective strategic plans to get a competitive advantage. 

https://link.springer.com/content/pdf/10.1007/s10639-022-11410-z.pdf

Factors affecting university students switching intention to mobile learning: a push-pull-mooring theory perspective

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users’ continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took “Himalayan FM” as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users’ willingness to continue use, through mining user comment data on the knowledge payment platform; Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users’ willingness to continuous use, while perceived cost has a direct negative impact on users’ willingness to continue use; (2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users’ willingness to continue use indirectly; (3) users’ perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users’ willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users’ willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.

https://www.mdpi.com/2071-1050/14/15/8992/pdf?version=1658713369

Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform

Abstract Drone delivery is an emerging service at the early adoption stage. It is imperative to understand what it takes for the public to accept such emerging services. This study asks what mechanism influences people’s switching intention in e-commerce drone delivery services. A 7 points Likert scale questionnaire were developed after the two rounds of pretest and a total of 83 surveys were collected from a business school in USA. This study applies privacy calculus theory and technology anxiety in innovation to develop a research model. An empirical survey and structural equation modeling analysis with SmartPLS and a consistent PLS algorithm are used to understand the hedging effect of relative advantages of drone delivery services and technology anxiety on the switching intention of e-commerce consumers. Theoretically, this research adds the e-commence literature, suggesting that people appreciate drone delivery’s speed and environmental protection advantages. However, privacy risk severity and vulnerability are not significant predictors of technology anxiety, negatively impacting switching intention. This study also provided practical contribution to improve the service development of e-commerce company to deliver their products to their customers with the most efficient resources being used.

Understanding e-commerce customer behaviors to use drone delivery services: A privacy calculus view

ABSTRACT In the information age we live in, we are constantly threatened by being drowned in a huge flood of information. Information overload (IO) describes this state where information can no longer be adequately processed by an individual. However, the danger posed by IO to individuals as well as organizations can still not be assessed properly due to a missing integration of previous findings. In this quantitative meta-analysis, we analysed the data of 133.011 people within 117 studies, and overall, 330 effect sizes. We performed multi-level as well as robust variance estimation analyses and found, among other things, positive correlations between IO and information avoidance, stress states, burnout and fatigue, and negative correlations between IO and performance and satisfaction. Explorative subgroup analyses revealed different moderating effects based on different vocational settings. Overall, the results of this meta-analysis indicate a negative relationship between IO and peoples’ behaviour and experience, which call for an evaluation of the exchange and handling of information. Across a wide range of studies and contexts, this meta-analysis reveals that IO may provoke the information fatigue syndrome that has been poorly considered to date, leading to severe consequences in both work and home contexts.

Drowning in the flood of information: a meta-analysis on the relation between information overload, behaviour, experience, and health and moderating factors

Purpose This study explores the factors that promote university teachers' switching intention from a traditional classroom to a smart classroom based on the push–pull–mooring (PPM) framework to enrich the theoretical research on the smart classroom and provide a reference for smart classroom promotion. Design/methodology/approach The proposed conceptual framework was developed from a comprehensive review of the related literature. This study tested and validated the proposed framework using a partial least square structural equation model based on 269 valid questionnaires. Findings (1) Perceived inefficiency, inquiry-based learning, future expectation and technical self-efficacy had significant effects on switching intention, while low participation, perceived usefulness and habit had no significant effects on university teachers’ switching intention in the smart classroom. (2) In the process of decision-making, the course category significantly moderates the impact of perceived inefficiency and technical self-efficacy on switching intention, while the user experience of smart classrooms significantly moderates the impact of perceived inefficiency on switching intention. Originality/value This study explains university teachers' switching intention from a traditional classroom to the smart classroom, which enriches the application area of the PPM framework. 

Utilizing the push–pull–mooring framework to explore university teachers' intention to switch from traditional classrooms to smart classrooms in China

Online learning is gaining popularity, but users can easily find alternatives and switch between learning platforms. Reducing users switching behavior is a critical condition for the sustainable development of an online learning platform; therefore, it is necessary to investigate the influence factors of users switching behavior between different platforms to retain users and enhance the competitiveness of enterprises. Push-Pull-Mooring (PPM) theory is adopted to construct a structural equation model of customer switching behavior on online learning platforms and to explore the mechanism of user switching behavior between learning platforms. The model is tested with data collected from 313 online learning users. The results show that information overload and dissatisfaction, as push factors, significantly affect user switching behavior. Functional value and network externality as pull factors positively affect user switching behavior, switching cost, and affective commitment as mooring factors negatively correlate with switching behavior. Further, this study also revealed that there are obvious different influencing factors for different online learning platforms. Overall, this study provides some practical strategies for the online learning platform and can help them to gain a competitive advantage.

https://www.mdpi.com/2071-1050/13/13/7087/pdf

Jingru Wang

Papers

Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory