Understanding the Influence of Wireless Communications and Wi‐Fi Access on Customer Loyalty: A Behavioral Model System

Reyes-Menéndez, Ana; Palos-Sánchez, Pedro R.; Saura, José Ramón; Martín‐Velicia, Félix A.

doi:10.1155/2018/3487398

Cited by 38 publications

(26 citation statements)

References 96 publications

(109 reference statements)

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“…The results contribute to the literature on psychological and behavioral profiling of different social and demographic groups, as well as to the research on UGC opinion [59]. Furthermore, we also propose a novel method, a psychological text-mining analysis technique that can be meaningfully used in further research to study other population cohorts and online communities as well as to topic discovery according to the online chatting on social media.…”

Section: Discussionmentioning

confidence: 72%

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Saura

Debasa

Reyes-Menéndez

2019

Journal of Open Innovation: Technology, Market, and Complexity

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The millennial generation plays a leading role in today’s connected world in which exists a confluence of numerous technologies and the internet in science, economy and innovation. This study aimed to identify the key factors that characterize the millennial generation within the online chatter on Twitter using an innovative approach. To this end, we analyzed the user generated content (UGC) in the social network (SNS) Twitter using a three-steps knowledge-based method for information management. In order to develop this method, we first used latent Dirichlet allocation (LDA), a state-of-the-art thematic modeling tool that works with Python, to analyze topics in our database. The data were collected by extracting tweets with the hashtag #Millennial, #Millennials and #MillennialGeneration on Twitter (n = 35,401 tweets). Secondly, sentiment analysis with a support vector machine (SVM) algorithm was also developed using machine-learning. Applying this method to the LDA results resulted in the categorization of the topics into those that contained negative, positive and neutral sentiments. Thirdly, in order to gather the final results, data text mining techniques were used. The negative factors that characterize the behavior of this generation are depression, loneliness and real-world relationship. The positive factors are body image, self-expression, travelers and digital life and the neutral factors are self-identity and anxiety. Practical implications can be used by public actors, companies or policy makers that are focused on the millennial generation as a target. The study has important theoretical applications as the topics discovered can be used to test quantitative models based on the findings and insights extracted from the UGC sample.

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Section: Discussionmentioning

confidence: 72%

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Saura

Debasa

Reyes-Menéndez

2019

Journal of Open Innovation: Technology, Market, and Complexity

Self Cite

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“…Secondly, the hypotheses were verified with the PLS-SEM path modeling estimation by applying SmartPLS 3, which also determined the significant hypothesized predictors. The main reason to apply PLS-SEM is that it can manage the factor models and the models composed of estimating constructs, measure the structural models, and conduct the model's adjustment trials [62]. In the last step of the analysis, the strength of the influence of exogenous variables on the endogenous variables were verified by using NN and the significance of endogenous variables were verified by PLS-SEM.…”

Section: Assessment Of the Measurement Modelmentioning

confidence: 99%

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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“…However, in scientific sources, there is no agreement on these categories. In most classifications, factors were not grouped at all; nonetheless, there were attempts to allocate factors to: Customer [16], psychological state [19], emotional state [21], service provider [22,23], communications [24], situation [25], marketing mix [26,27], and environment [18].…”

Section: Factors Determining Customer Loyaltymentioning

confidence: 99%

Service Customer Loyalty: An Evaluation Based on Loyalty Factors

Vilkaitė-Vaitonė

Skačkauskienė

2020

Sustainability

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This study sheds light on customer loyalty based on three groups of factors (customer, service provider, and environment). Noting the diversity of the effects of customer loyalty factors, this research investigates the impact of every factor upon customer loyalty. This paper provides an innovative insight into how a variety of customer loyalty factors might be combined into a single measure of customer loyalty. Finally, this study examines phases of customer loyalty and identifies the factors that prevent a peak of customer loyalty. The factors determining customer loyalty explain why customers move across different loyalty phases over time. The results of the empirical testing confirmed the practical applicability of the suggested approach for evaluating customer loyalty based on these factors. An innovative approach to the evaluation of customer loyalty is essential for marketers because it will help them to evaluate loyalty in cases where data about customer behavior are not collected. The findings of the research contribute to a better understanding of which factors are a viable basis for increasing customer loyalty, specifically in the catering and beauty markets, and offer guidance to marketing managers on how to shift customers to more desirable loyalty phases.

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Understanding the Influence of Wireless Communications and Wi‐Fi Access on Customer Loyalty: A Behavioral Model System

Cited by 38 publications

References 96 publications

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

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

Service Customer Loyalty: An Evaluation Based on Loyalty Factors

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