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Some aspects of technology behaviour remain unclear, such as the generation of technology use and engagement. So, this study aims to address the following question: does engagement with technology drive the use of technology?or does the use of technology create the engagement with technology?

Based on the uses and gratifications theory, this study compares three alternative competing models that explain technology behaviour on a sample of 715 individuals, using the selection criteria proposed by Mathieson. A comprehensive analysis and comparison of three structural competing models on technology behaviour, namely, “use-and-engagement”, “use-to-engagement” and “engagement-to-use”, are presented.

Findings show that the “use-and-engagement” model provides a better explanation of technology behaviour and is superior to predict technology behaviour, suggesting that both technology engagement and use could be considered as consequences.

This study’s major contribution is the empirical examination of three structural competing models and the selection of the best explaining model of technology behaviour.

Algunos aspectos del comportamiento tecnológico permanecen sin aclarar, como la creación del uso e implicación hacia la tecnología. Así que abordamos la siguiente pregunta: ¿La implicación con la tecnología impulse su uso?, o ¿es el uso de la tecnología el que impulse la implicación?.

Basándonos en la Teoría de los Usos y Gratificaciones se han comparado tres modelos alternativos que compiten entre sí para explicar el comportamiento tecnológico, en una muestra de 715 individuos utilizando el criterio de selección propuesto por Mathieson. Se presenta un análisis y una comparación exhaustive de tres modelos estructurales competitivos sobre el comportamiento tecnológico, que son “uso-e-implicación”, “uso-para-la implicación” e “implicación-para-el uso”.

Los resultados muestran que el modelo “uso-e-implicación” proporciona la mejor explicación del comportamiento tecnológico y es superior para predecir el comportamiento tecnológico, lo que sugiere que tanto la implicación como el uso de la tecnología podrían considerarse como consecuencias.

Nuestra principal contribución es el análisis empírico de tres modelos estructurales competitivos y la selección del mejor de ellos para explicar el comportamiento tecnológico.

技术行为的某些方面仍然不清楚, 例如技术使用和参与的产生。因此, 我们意在解决以下问题：对技术的参与是否推动了技术的使用,还是技术的使用创造了技术的参与？

基于 “使用与满足 “理论, 我们使用马蒂森提出的选择标准, 在715人的样本上比较了三种解释技术行为的替代竞争模型。即我们对三个关于技术行为的结构性竞争模型, “使用和参与”、“使用到参与 ”和 “参与到使用 ”进行了综合的分析和比较。

研究结果显示, “使用和参与 “模型更好的解释了技术行为, 并且其优于预测技术行为, 这表明技术参与和使用都可以被认为是后果。

我们的主要贡献是对三个结构性竞争模型进行了实证检验, 并选择了对技术行为的最佳解释模型。

There are differences in the motivations underlying technology behaviour in each generational group; and there may be variances in the way each generational group uses and gets engaged with technology. In this context, this study aims to address the following questions: “Does generational cohort influence technology behaviour?” and if so: “What are the main motivations underlying Millennials and Generation X technology behaviour?”.

For this purpose, based on the uses and gratifications theory this study examines technology behaviour through multi-group structural equation modelling, drawing on a sample of 707 millennials and 276 Generation X individuals

Research findings indicate that millennials mostly use and get engaged with technologies for entertainment and hedonic purposes; while Generation X individuals are mainly driven by utilitarian purposes and information search. Further, research findings indicate the moderating role of generational cohort in the use of technologies.

This study provides empirical evidence of the main differences and motivations differences driving technology behaviour of millennials and Generation X individuals.

The purpose of this study is to segment mobile wallet users using a finite mixture partial least squares (FIMIX-PLS) approach and evaluate the unobserved heterogeneity across segments.

Partial least square structural equation modeling (PLS-SEM) using a convenience sample of 744 responses was used to analyze the measurement, structural model and hypotheses testing. To examine unobserved heterogeneity and identify user segments, FIMIX-PLS technique was employed. To generate more precise recommendations, importance-performance map analysis (IPMA) was performed with attitude as the target variable.

A structural equation model revealed that except perceived ease of use (PEOU) all other dimensions, namely perceived usefulness (PU), lifestyle compatibility (LC), facilitating conditions (FC), trust and security significantly influences attitude which, in turn, determines intention. The FIMIX-PLS technique resulted in four segments – The Rationalist, Early Adopters, Late Adopters and The Innovators.

The paper provides segment specific and between segment differences to derive implications. Identification of relevant predictors and segments will help academicians, marketing researchers and practitioners in gaining further understanding of the mobile wallet adoption. The findings of the paper can guide mobile wallet providers to frame appropriate strategies and offerings pertaining to the obtained segments.

The paper builds upon Technology Acceptance Model (TAM) to propose an integrated model to explain adoption behaviors associated with mobile wallet. To the best of the authors' knowledge, this is one of the first empirical attempts using FIMIX-PLS technique to assess precursors of adoption and substantiates the perceived value-attitude-intention linkage to identify heterogeneity among mobile wallet users.