This article conducts a meta-analysis to determine the effect of perceived risk on intention to purchase in e-commerce settings. The literature generally favors the negative relationship between the two variables; however, some studies have found this relationship to be not significant or even positive. Thirty-five studies were examined, representing 39 overall samples across multiple contexts with a total sample size of 13779 and overall weighted effect size (Pearson's r) for all studies of −0.362 (95% CI: −.432, −.286), a significant medium effect, with a stronger effect in B2C than B2B contexts. Major IS journals rarely report non-significant results and reflect publication bias, posing a problem for a comprehensive meta-analysis. Due to the reluctance to report or accept, studies showing non-significant results represent a significant challenge for IS research. Our aim is to encourage more meta-analysis in IS literature and utilize this methodology to analyze previously held theories.

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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The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential 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 identifies 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, time-aligned to COVID-19, 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 exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.