The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM

The Technology Acceptance Model (TAM) has lately been utilized in a number of studies to investigate why people reject or adopt new technologies like mobile commerce or e-learning. However, several studies have found weaknesses in TAM’s ability to predict consumers’ purchase intention behavior. To compensate for TAM’s weaknesses, this study presents a model that integrates all of TAM’s components with the Value-Based Adoption Model (VAM). The perceived benefits and sacrifices were considered to provide a list of the implications for both researchers and e-learning service providers. Furthermore, the moderating role of e-word of mouth was utilized to examine the relationship between attitude, intention, perceived value, and intention toward e-learning, in order to match the current circumstances with the growing popularity of social networks. This study was conducted with a quantitative analysis by using data collected from survey 417 e-learning consumers. Except for perceived fee, which has a negative effect on perceived value, the results demonstrate that all hypotheses of latent correlations in TAM and VAM were strongly significant. Furthermore, attitude and perceived value have a significant role in determining consumer adoption of e-learning. Consumers’ perceived value will be driven by the high and low levels of the moderating influence of e-word of mouth, influencing their intention toward e-learning. Since e-learning is an effective sustainable education system, the result of this study can provide a good solution to facilitate e-learning in current and future conditions.

Integrative adoption model for personal health records: A structural equation modeling approach

The adoption of a personal health record (PHR) is a crucial element in quality healthcare, allowing patients to permit the storage of their health information to create a more inclusive, reliable health record. However, the embracing of PHRs has been slow compared to other healthcare-related systems due to the poor design and behavioral aspects. The objective of this research is to study user acceptance factors to identify a better design for PHR systems and to promote healthy behaviors that support individuals' performance. The study proposes an integrative adoption model for PHRs that integrates theoretical factors from the health belief model with the user acceptance determinants from the technology acceptance model and innovation diffusion theory. Using structural equation modeling with the R “Lavaan” package, the study tested the hypothesis relationships of the constructs. The data were captured from individuals through Amazon’s MTurk. Among the nine relationships studied, the research revealed six significant relationships that inform the final PHR integrative adoption model. The research provides great insights into the factors that influence individuals’ PHR adoption. The results introduce a novel integration model to the current body of knowledge. This model will contribute to a better theoretical understanding of the actual use of healthcare-related technologies and bring greater estimates of patient engagement in healthy activities.

Integrating TAM, VAM, PAM and Security Perception in The Intention of Fintech Service Usage

This study is meant to develop a TAM (Technology Acceptance Model) by integrating a VAM (Value-based Adoption Model), PAM (Pos Acceptance Model) and security perceptions. The frameworks of the theories are expected to deeper explain the perspective perceived by fintech services users in terms of security, perceived value, benefits, and satisfaction in using fintech. Survey through a quantitative approach combined with explanatory research in Madiun Residency. The results showed that all hypotheses proposed in this study were accepted. Security is the main requirement for people to continue or discontinue using fintech services. Perceived value is a comparative result influenced by the amount of sacrifice that is given and the perceived benefits. People who are satisfied and tends to assume that the fintech service is valuable, will most likely continue using the fintech service. Therefore, it can be concluded that TAM, VAM, PAM and security perceptions are a unified whole in understanding one's behaviour in adopting technology such as fintech services. Keywords-TAM; VAM; PAM; Security Perception; Fintech

Understanding mobile users' information adoption behaviour: an extension of the information adoption model

PurposeMobile messaging applications (MMAs) have surpassed top social media platforms. Recent and rapid use of MMAs has made it extremely difficult to ignore the existence of customer-to-customer (C2C) mobile information. This study, therefore, aims to expand the knowledge of customers' adoption behaviour of such information.Design/methodology/approachThrough applying and utilizing social support theory (SST) and the information adoption model (IAM), this study introduces a holistic theoretical model, explaining customers' adoption of information derived from MMAs and exploring the antecedents of IAM. Based on the data collected from 305 UK MMA users, this study empirically tests the research model using structural equation modelling estimation.FindingsThe results of this study reveal that social support is a key antecedent of information quality and credibility and support IAM in terms of its ability to explain MMAs' information adoption.Practical implicationsThe insights are valuable for businesses and marketers to understand customers' mobile communications and be socially support-oriented while developing marketing communication strategies.Originality/valueThe study integrates SST and IAM to improve the understanding of customers' information adoption behaviour. It is the first attempt that establishes that social support is a key antecedent of IAM.

An innovation adoption model of licensing-in for US start-ups

Using the Kauffman panel dataset of US start-ups, we analyse the key determinants oflicensing-in adoption. Licensing-in entails an intellectual property contract between the licensor(e.g. upstream established firm) and licensee (e.g. downstream start-up) aiming to bring aninnovation to market rapidly. Assuming maximizing of the owner’s managerial utility in thestart-up years, we explain licensing-in adoption through firm characteristics like size, R&D andcapital structure, as well as other IP types, and controls for year and regional fixed effects, usingpanel probit estimation with adjustments for sample selection bias and endogeneity. We findkey determinants of licensing-in to be owners’ equity, product (rather than service) sales andR&D spend; and then comment on their policy implications for business incubation.