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Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing.

For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing.

This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government.

Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.

The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it. Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses. However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection. The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19.

For the analysis, data were collected from Twitter. Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy. Tweets across countries of four continents were collected from March 2020 to August 2020. In total, 70,212 tweets were used for this study. Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent. Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing.

This study was conducted in two parts. Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing.” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing. Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral. Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public.

Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it. This study fills the gap.

Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing.

For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing.

The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing.

After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.

It has been eight months into the global pandemic health crises COVID-19, yet the severity of the crises is just getting worse in many parts of the world. At this stage, it is essential to understand and observe the general attitude of the public toward COVID crises and the major concerns the public has voiced out and how it varies across months. Understanding the impact that the COVID-19 crises have created also helps policymakers and health-care organizations access the primary steps that need to be taken for the welfare of the community. The purpose of this study is to understand the general public's response towards COVID-19 crises and the major issues that concerns them.

For the analysis, data were collected from Twitter. Tweets regarding COVID-19 crises were collected from February 1, 2020, to June 27, 2020. In all, 433,195 tweets were used for this study. Natural language processing (NLP), which is a part of Machine learning, was used for this study. NLP was used to track the changes in the general public's sentiment toward COVID-19 crises and LDA was used to understand the issues that shape the general public's sentiments the crises time. Using Python library Wordcloud, the authors further derived how the primary concerns regarding COVID crises various from February to June of the year 2020.

This study was conducted in two parts. Study 1 results showed that the attitude of the general public toward COVID crises was reasonably neutral at the beginning of the crises (Month of February). As the crises become severe, the sentiments toward COVID increasingly become negative yet a considerable percentage of neutral sentiments existed even at the peak time of the crises. Study 2 finds out that issues including the severity of the disease, Precautionary measures need to be taken, and Personal issues like unemployment and traveling during the pandemic time were identified as the public's primary concerns.

The research adds value to the literature on understanding the major issues and concerns, the public voices out about the current ongoing pandemic. To the best of the authors’ knowledge, this is the first study with an extended period of timeframe (Five months). In this research, the authors have collected data till June for analysis that makes the results and findings more relevant to the current time.

This paper aims to propose a method to automate the tolerance analyses of mechanical assembly using STandard for the Exchange of Product model data-Application Protocol Part 242 (STEP AP 242) files derived from the 3-D computer-aided design (CAD) models.

Product manufacturing information and mating information available in ISO 10303 STEP AP242 files resulting from the 3-D CAD model of mechanical assembly are extracted. The extracted geometric attributes, geometric dimensioning and tolerancing (GD&T) and mating information are used to automatically generate assembly graph and mating edges required for the tolerance analyses of the mechanical assembly by using the matrix approach.

The feasibility of the proposed method is verified through two mechanical assembly case studies. The results of manual calculations and tolerance values computed by the automated method are very closely matching.

Tolerance analysis is an integral part of product development that directly influences the cost and performance of a product. Apart from the academic interest, the work is expected to have positive implications for the digital design and smart manufacturing industry that involve in the development of solutions for automation of design and manufacturing system functions.

The approach presented in the paper that aids the automation of tolerance analyses of mechanical assembly is an innovative application of the STEP AP 242 file. The automation of tolerance analyses would improve the productivity and efficiency of the product realization process.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

This study aims to use financial stability and health facilities of countries, to cluster them for making a more consensus environment for manifesting the status of Covid-19 in a justifiable manner. The scarcity of the categorisation of the countries of the world in a common platform, and the requirement of manifesting the pandemic status such as Covid-19 in a justifiable manner create the demanding requirement. This study mainly focusses on assisting to generate a liable manifesto to criticise the span of viral infection of the severe acute respiratory syndrome coronavirus-2 over the globe.

Data for this study has been gathered from official websites of the World Bank, and the world in data. The Louvain clustering method has been used to cluster the countries based on their financial strength and health facilities. The resulted clusters are visualised using Silhouette plots. The anomalies of the clusters had been used to quantify the pandemic situation. The status of Covid-19 has been manifested with the time series analysis through python programming.

The countries of the world have been clustered into seven, where developed countries divided into three clusters and the countries with transition economies and developing clustered together into four clusters. The time series analysis of recognised anomalies of the clusters assist to monitor the government responses and analyse the efficiency of used safety measures against the pandemic.

This study’s resulted clusters are highly valuable as a division of countries of the whole world for evaluating the health systems and for the regional levels. Further, the results of time series analysis are beneficial in monitoring the government responses and analysing the efficiency of used safety measures against the pandemic.

India has the biggest number of active users on social media platforms, particularly Twitter. The purpose of this paper is to examine public sentiment on COVID-19 vaccines and COVID Appropriate Behaviour (CAB) by text mining (topic modeling) and network analysis supported by thematic modeling.

A sample dataset of 115,000 tweets from the Twitter platform was used to examine the perception of the COVID-19 vaccination and CAB from January 2021 to August 2021. The research applied a machine-learning algorithm and network analysis to extract hidden and latent patterns in unstructured data to identify the most prevalent themes. The COVID-19 Vaccine Hesitancy Amplification Model was formulated, which included five key topics based on sample big data from social media.

The identified themes are Social Media Adaptivity, Lack of Knowledge Providing Mechanism, Perception of Vaccine Safety Measures, Health Care Infrastructure Capabilities and Fear of Coronavirus (Coronaphobia). The study implication assists communication strategists and stakeholders design effective communication strategies using digital platforms. The study reveals CAB themes as with Mask Wearing Issues and Employment Issues as relevant themes discussed on digital channels.

The themes extracted in the present study provide a roadmap for policy-makers and communication experts to utilize social media platforms for communicating and understanding the perception of preventive measures of vaccination and CAB. As evidenced by the increased engagement on social media platforms during the COVID-19-induced lockdown, digital platforms are indeed valuable from the communication perspective to be proactive in the event of a similar situation. Moreover, significant themes, including social media adaptivity, absence of knowledge-providing mechanism and perception of safety measures of the vaccine, are the critical parameters leading to an amplified effect on vaccine hesitancy.

The COVID-19 Vaccine Hesitancy Amplification Themes (CVHAT) equips stakeholders and government strategists with a preconfigured paradigm to tackle dedicated communication campaigns and assess digital community behavior during health emergencies COVID-19.

The increased acceptance of vaccines and the following of CAB decrease the advocacy of mutation of the virus and promote the healthy being of the people. As CAB has been mentioned as a preventive strategy against the COVID-19 pandemic, the research preposition promotes communication intervention which helps to mitigate future such pandemics. As developing, economies require effective communication strategies for vaccine acceptance and CAB, this study contributes to filling the gap using a digital environment.

Chan et al. (2020) recommended using social media platforms for public knowledge dissemination. The study observed that the value of a communication strategy is increased when communication happens using highly trusted and accessible channels such as Twitter and Facebook. With the preceding context, the present study is a novel approach to contribute toward digital communication strategies related to vaccination and CAB.

In promotional ads that contain both the Regular Price and Sale Price information, this paper aims to investigate whether changing the font sizes of these two prices has an effect on how consumers process the ad message. The authors use acquisition-transaction utility perspective to identify key differences in mechanisms invoked by the larger font size of the Regular Price vs that of the Sale Price.

Study 1 uses eye-tracking apparatus to study subjects’ responses to a full-color, realistic-looking ad for a digital camera. Study 2 uses a survey questionnaire to gauge subjects’ responses to a similar print ad. The font sizes of Regular Price and Sale price were manipulated while keeping all the other elements of the ad the same. Subjects were undergraduate students who participated in the study for a small incentive.

The authors find that making the relative font size of Regular Price bigger invokes a transaction-utility mechanism where customers’ attention is focused on the savings that can be had using the promotion. A bigger font size of Sale Price invokes an acquisition-utility mechanism that draws on the customers’ value consciousness.

The use of student subjects and only one product category in the experiments may limit the generalizability of the study’s findings.

In print ads, managers predominantly use a bigger font size for the Sale Price. This study suggests that the choice of a bigger font size should really be driven by the objective of the promotion: a bigger font size for Sale Price is advised if the objective is to take an acquisition utility approach, whereas a bigger font size for Regular Price is advised for using a transaction utility approach.

This study is the first attempt at using an acquisition-transaction utility perspective for understanding the use of relative font sizes in the context of price promotions.

In important interpersonal service interactions with a frontline employee (FLE), consumers at times fail to carry out their share of responsibility in the execution of the service, resulting in a situation of “consumer created emergency”. This might defeat the consumer's goal of availing the service (termed as consumer failure). This study explains the role of employee's hope in managing consumer failure in the situation of consumer created emergencies.

Hypotheses were tested in three experiments that simulated service emergency across a general printing service situation and a travel service situation.

The study shows that: (1) FLE hope has a positive effect on consumer satisfaction, and is mediated by the consumer's assumed effort by the FLE; (2) the effect of FLE hope on consumer satisfaction changes with changing levels of consumer hopefulness about the service outcome; (3) despite situation of consumer created emergency, consumer failure results in low consumer satisfaction due to attribution error and (4) external attribution by the FLE could not significantly rectify consumer's attribution error and hence could not alleviate consumer dissatisfaction.

The study suggests relevance and pathways of managing emotions and attributions of consumers and FLEs for superior performance outcomes.

The study theorizes and tests the role of hope, which is an important positive emotion during emergencies because frontline service settings have heretofore predominantly focused on managing negative traits and outcomes.