A SEM-neural network approach for predicting antecedents of m-commerce acceptance

Liébana‐Cabanillas, Francisco; Marinković, Veljko; Kalinić, Zoran

doi:10.1016/j.ijinfomgt.2016.10.008

Cited by 402 publications

(366 citation statements)

References 67 publications

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“…A three-step statistical data analysis was performed as suggested by Liébana-Cabanillas et al [60] and Chong [61]. First, to evaluate the validity of the conceptualized research model, the value of the Cronbach's alpha coefficient was applied to determine the reliability of model variables.…”

Section: Assessment Of the Measurement Modelmentioning

confidence: 99%

“…The analysis examined the network with one to ten hidden nodes, as there is no heuristic method to identify the number of hidden nodes in a NN [67]. The nodes' number in one hidden layer was set to 2, and the activation function was set to sigmoid function in both hidden and output layers, following recommendations of Chong et al [22] and Liébana-Cabanillas et al [60]. As for increasing the effectiveness of training, both inputs and outputs were normalized to the range [0, 1] [60].…”

Section: Neural Network Analysismentioning

confidence: 99%

“…The nodes' number in one hidden layer was set to 2, and the activation function was set to sigmoid function in both hidden and output layers, following recommendations of Chong et al [22] and Liébana-Cabanillas et al [60]. As for increasing the effectiveness of training, both inputs and outputs were normalized to the range [0, 1] [60]. Figure 2 shows the NN architecture for this study.…”

Section: Neural Network Analysismentioning

confidence: 99%

“…Conversely, the standard deviation of training model was 0.002, while the standard deviation of the testing model was 0.004. The value of RMSE was low, indicating that it is an accurate and reliable prediction [22,60,69]. Table 7 presents the sensitivity analysis performance.…”

Section: Neural Network Analysismentioning

confidence: 99%

See 3 more Smart Citations

A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

Zhang

Bao

et al. 2019

Sustainability

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As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers' purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers' purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

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Section: Assessment Of the Measurement Modelmentioning

confidence: 99%

Section: Neural Network Analysismentioning

confidence: 99%

Section: Neural Network Analysismentioning

confidence: 99%

Section: Neural Network Analysismentioning

confidence: 99%

See 2 more Smart Citations

A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

Zhang

Bao

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Mohd Suki and Mohd Suki [52] studied the factors influencing the intention to use a flight ticket booking application on a mobile device and found that perceived usefulness was the most influential factor. Liébana-Cabanillas et al [53] studied the antecedents of the intention to adopt m-commerce and found that customer involvement and customization were the strongest leading factors. Likewise, Hur et al [39] studied the antecedents of the information sharing intentions of tourists through social networks and found that the information-seeking, entertainment, and relationship maintenance motives positively influenced the sharing intention.…”

Section: Communication Factors Affecting the Travelers Intention To Umentioning

confidence: 99%

Communication Factors Affecting Tourist Adoption of Social Network Sites

Gebsombut

2019

Sustainability

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Smart tourism technologies (STTs) are technological media that tourists apply in various stages of the tourism decision-making process. The purpose of this study was to explore how the communication elements of social network sites (SNSs), as a part of STTs, enhance tourists' motivation and usage intention. A structural framework based on communication elements and the uses and gratification theory with regard to SNSs usage was developed and investigated. An online survey was employed for the data collection, and structural equation modeling was used in the hypotheses analysis. The findings indicated that Internet self-efficacy, information quality, and systems quality trigger the information-seeking motive while service quality and source credibility positively determine the relationship maintenance motive. The information-seeking motive, entertainment motive, relationship maintenance motive, and Internet self-efficacy positively influence the intention to use SNSs for trips. New findings were found in terms of the relationship between the motives. The information-seeking motive and relationship maintenance motive influence the entertainment motive. Moreover, the relationship maintenance motive influences the information-seeking motive.Sustainability 2019, 11, 4198 2 of 13 their travel experience. Moreover, increasing tourist satisfaction through ICT has become a tourist destination's aim [8] because tourism services in all travel stages affect tourists' evaluation [9]. Gretzel et al. [3] mentioned that ICT smart systems assist travelers in their travel decision-making processes including (1) predicting tourists' needs and providing recommendations before the trip; (2) presenting information and services for tourists' on-site experiences; and (3) allowing tourists to share their travel experiences, which also provide other tourists with information for their decision-making processes. Furthermore, smart tourists provide information that is important for businesses who search for meaningful tourism experiences [3] because businesses can adjust themselves according to customers' needs.However, few studies on STTs have focused on the motives to use the technologies and relationship between the motives. Due to the various combinations of ICT technologies, it may be complicated to study STTs as a whole. Thus, this study focused on social network sites (SNSs), which tourists could gather and provide travel information on the same platform. SNSs can help businesses achieve a sustainable relationship with their key customers, as they provide two-way communication, especially cultural products in which many tourism products are cultural-based. Hence, SNSs are suitable for generating the rapid circulation of cultural products on the Internet [10]. Since tourists may use social network platforms for their travel decision-making process, it is, therefore, necessary to gain a better understanding of the relationship between communication elements, tourists' motivation, and their intention to use the media for their trip. Thu...

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Doing good for society! How purchasing green technology stimulates consumers toward green behavior: A structural equation modeling–artificial neural network approach

et al. 2022

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Many countries have recognized the urgent need to address environmental problems, such as air pollution, waste disposal, global warming, and natural resource depletion, through the application of green technology. ANT Forest is one such technological initiative that has gained academic attention for its potential to minimize adverse environmental impacts and promote sustainable green behavior by involving people in eco-friendly activities. We built an integrated framework to understand users' continuance intention (CI) toward ANT Forest based on the expectation-confirmation model (ECM) and the task-technology fit model (TTFM). Using structural equation modeling (SEM), we analyzed survey data from 353 ANT Forest users. We then included the SEM results as components of an artificial neural network (ANN) to understand users' CI toward ANT Forest. The results from the SEM analysis revealed a series of sequential associations: (a) green habit as an individual characteristic and perceived entertainment as a technology characteristic significantly affect perceived green task-technology fit (GTTF), (b) perceived GTTF strongly and positively influences confirmation and CI, (c) confirmation is positively associated with users' satisfaction and delight, (d) delight significantly impacts satisfaction, and (e) perceived usefulness (PU) and satisfaction are strong determinants of CI. An ANN analysis further confirmed these findings. The study discusses managerial implications along with future research directions.

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A SEM-neural network approach for predicting antecedents of m-commerce acceptance

Cited by 402 publications

References 67 publications

A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

Communication Factors Affecting Tourist Adoption of Social Network Sites

Doing good for society! How purchasing green technology stimulates consumers toward green behavior: A structural equation modeling–artificial neural network approach

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