Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling

Sharma, Sujeet Kumar

doi:10.1007/s10796-017-9775-x

Cited by 168 publications

(187 citation statements)

References 56 publications

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“…The input neurons of Model 3 are utilitarian value and hedonic value, and its output neuron is continuance intention. As no heuristic method for identifying the number of the hidden nodes in a neural network exists, our study examined the neural network by using one to ten hidden nodes, which is consistent with the procedure conducted by [15]. To avoid over-fitting, this study conducted ten-fold cross-validation, whereby 90% of the data was used for network training, and the remaining 10% was used for testing, i.e.…”

Section: Neural Network Analysismentioning

confidence: 95%

“…A neural network model connects input and output systems through neurons and can simulate the nervous system of the human brain for complex information processing, and has memory and continuous learning capabilities [12]. Neural network analysis can not only detect linear relationships, but can also identify non-linear relationships [15]. At the same time, neural networks can learn and tolerate noise samples through the training process [12].…”

Section: Neural Network Analysismentioning

confidence: 99%

“…But because of its black box characteristics, the neural network method is unsuitable for validating theories [11,12]. Therefore, the combination of SEM and neural network modelling can make use of the advantages of each of the two approaches [15]. The present study provides not only a theoretical understanding of citizens' mobile government microblog usage behavior, but also presents practical insights for government agencies and officials for increasing the sustainability of mobile government microblog services.…”

Section: Introductionmentioning

confidence: 98%

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Sustainability of Government Social Media: A Multi-Analytic Approach to Predict Citizens’ Mobile Government Microblog Continuance

Yang

Zeng

2018

Sustainability

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Attracting citizens and facilitating their continued usage is critical for the sustainable development of mobile government microblog services. Drawing on the stimulus–organism–response (SOR) framework and the literature related to perceived value, this study investigates the factors that affect citizens’ continuance intention vis-a-vis mobile government microblogs from a value-based perspective. A structural equation modeling (SEM)-neural network combined method was used to test the proposed model by using data collected from 301 mobile government microblog users in China. The SEM analysis shows that social influence, perceived interactivity, and perceived mobility positively affect citizens’ utilitarian value and hedonic value, which further affects their continuance intention. The significant factors obtained from the SEM are used as input for a neural network analysis to calculate their relative impacts. The results of the neural network analysis showed that perceived mobility is the most important factor influencing utilitarian value, while social influence is the most significant factor affecting hedonic value. The normalized importance of utilitarian value on continuance behaviors is larger than that of hedonic value.

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Section: Neural Network Analysismentioning

confidence: 95%

Section: Neural Network Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Sustainability of Government Social Media: A Multi-Analytic Approach to Predict Citizens’ Mobile Government Microblog Continuance

Yang

Zeng

2018

Sustainability

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“…In academia, although many previous studies have devoted to understanding mobile government social media adoption and use behaviors, most of them explain its usage from a technical-center point of view [2,5,6]. Theories such as the technology acceptance model [7] and its extension [8] have been adopted to explain citizens' mobile government microblog use behaviors. Little research has explored Sustainability 2019, 11, 6887 2 of 15 the impacts of social factors, such as social interaction and social anthropomorphic cues, on citizens' continuance usage of mobile government microblog.…”

Section: Introductionmentioning

confidence: 99%

Sustainability of Government Microblog in China: Exploring Social Factors on Mobile Government Microblog Continuance

Yang

Pan

et al. 2019

Sustainability

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The sustainable development of mobile government social media depends citizens’ continued use. Based on the Stimulus-Organism-Response framework and social response theory, the present study investigated the impacts of perceived similarity and anthropomorphic cues on citizens’ mobile government microblog continuance. A research model of mobile government microblog continuance was developed and empirical tested by using dataset collected from 428 mobile government microblog citizens in China. The results of structural equation modeling demonstrated that perceived similarity (including external similarity and internal similarity), and anthropomorphic cues (including social interaction value, visual appearance, and identity attractiveness), have positive influences on both cognitive and affective involvement, which further determinate mobile government microblog continuance. Considering the path coefficient and significant levels, the impact from affective involvement on mobile government microblog continuance is stronger that from cognitive involvement.

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“…It is estimated that 44 times more data generation would take place, from 2009 to 44 zettabytes of data by 2020 (CSC 2017). In addition, the exponential growth of mobile telephony (Sharma, 2017), cloud computing and 4G networks have created many more social media touchpoints. As a result, customers are found to be connected to smart devices (smartphones, tablets, smartwatches, Cortana, Siri and Alexa etc.)…”

Section: Introductionmentioning

confidence: 99%

Analysis of Factors that Influence Customers’ Willingness to Leave Big Data Digital Footprints on Social Media: A Systematic Review of Literature

2017

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Big data has been discussed extensively in existing scholarly works but scant consideration is given to customers' willingness to generate and leave big data digital footprints on social media, especially in the light of the profusely debated issue of privacy and security. The current paper endeavours to address this gap in the literature by developing a conceptual framework. In doing so, this paper conducts a systematic review of extant literature from 2002 to 2017 to identify and analyse the underlying factors that influence customers' willingness to leave digital footprints on social media. The findings of this review reveal that personal behaviour (intrinsic psychological dispositions), technological factors (relative advantage and convenience), social influence (social interaction, social ties and social support) and privacy and security (risk, control and trust) are the key factors that influence customers' willingness to generate and leave big data digital footprints on social media. The conceptual framework presented in this paper advances the scholarship of technology adoption and use and provides useful direction for future empirical research for both academics and practitioners.

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Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling

Cited by 168 publications

References 56 publications

Sustainability of Government Social Media: A Multi-Analytic Approach to Predict Citizens’ Mobile Government Microblog Continuance

Sustainability of Government Social Media: A Multi-Analytic Approach to Predict Citizens’ Mobile Government Microblog Continuance

Sustainability of Government Microblog in China: Exploring Social Factors on Mobile Government Microblog Continuance

Analysis of Factors that Influence Customers’ Willingness to Leave Big Data Digital Footprints on Social Media: A Systematic Review of Literature

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