Understanding the Determinants of Consumers' Switching Intentions in a Standards War

Lin, Tung‐Ching; Huang, Shiu-Li

doi:10.2753/jec1086-4415190105

Cited by 71 publications

(46 citation statements)

References 62 publications

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“…We grouped tweets into two technology-user classes: iPhone users and Android users, to explore potential behavioral aspects. Extant research supports such a classification of technology users for analyzing sentiment, psychological factors, individual differences and technology switching [24], [47]- [49]. We identified 1794 Twitter for iPhone users, and 621 Twitter for Android users, in our dataset and ignored smaller classes, such as users of web client technologies.…”

Section: ) Descriptive Analysis Of Tweetsmentioning

confidence: 99%

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

et al. 2020

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The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences-the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, timealigned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios-an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future. INDEX TERMS COVID-19, Coronavirus, feeling, reopen, textual analytics, sentiment analysis, Twitter, new normal I. INTRODUCTION T HE anxiety, stress, financial strife, grief, and general uncertainty of this time will undoubtedly lead to behavioral health crises.-Coe and Enomoto, McKinsey, on the COVID-19 crisis [1]. COVID-19 has become a paradigm shifting phenomenon across domains and disciplines, affecting billions of people worldwide, directly or indirectly. The current COVID-19 pandemic disaster has led to escalating emotional, mental health, and economic issues with significant consequences, and this presents a serious challenge to reopening and recovery initiatives [2]-[4]. Based on the classification of novel coronaviruses (nCoV), the 2019-2020 novel coronavirus is officially termed "Severe acute respiratory syndrome coronavirus 2", also called as "SARS-CoV-2" and "2019-nCoV"". The present study uses SCoV2 (short for SARS-CoV-2) to represent the virus, and COVID-19 to represent the resulting disease [5]. In the United States (US) alone, as of June 24, 2020, COVID-19 has infected well over 3.8 million people and has taken over 142,000 lives, and continues to spread and claim more lives [6]. Moreover, as a result of the "Lockdown"

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Section: ) Descriptive Analysis Of Tweetsmentioning

confidence: 99%

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

et al. 2020

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“…In prior PPM studies, social norm has been used as either a mooring factor (Lin & Huang, 2014) or a controlled factor (Bhattacherjee & Park, 2014). We tend to treat social norm as a mooring factor as it fits the definition of mooring effect.…”

Section: Pull and Mooring Effects: Social Influencesmentioning

confidence: 99%

Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market

Vassileva

Zhao

2017

Computers in Human Behavior

104

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“…Wang & Li (2016) proposes a PPM protecting intention influence model to explore factors that influence users adopting security measures behavior. In spite of all the related researches above, plenty of other studies have adopted PPM model into different research fields, such as Jung et al (2017), Hsieh et al (2012), Lin & Huang (2014). It shows that PPM model's variables (push, pull and mooring) differ across research context and researchers should consider the distinct financial behavior characteristics to investigate how push, pull, and mooring factors will influence microfinance behavior in rural areas.…”

Section: Literature Review Of Ppm Theorymentioning

confidence: 99%

Regional Identity’s Role in Cambodian Microfinance Adoption: Pushing, Pulling and Mooring Factors

Sun¹,

Dedahanov²,

Shin³

et al. 2019

ASS

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By sampling usage intention factors of users in rural Cambodia, the study shows their effects on usage intention by integrating PPM and Reinforcement Sensitivity theories. Most of the behavioral factors are adopted from previous research. Perceived risk, reward sensitivity and knowledge prove to have significant effect on usage intention whereas regional identity does not. Regional identity proves to have moderating and negative effects on relationships between reward sensitivity, perceived risk, knowledge, and usage intention. The research results reinforce major roles for both government and microfinance institutions in considering and providing insights for future policy, management and investigations.

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Understanding the Determinants of Consumers' Switching Intentions in a Standards War

Cited by 71 publications

References 62 publications

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market

Regional Identity’s Role in Cambodian Microfinance Adoption: Pushing, Pulling and Mooring Factors

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