Can Google Trends data provide information on consumer’s perception regarding hotel brands?

Usually, in a process of purchasing products at Amazon.in, several steps are required to purchase the products where the consumer begins to navigate through the advertisements, internet and word of mouth, then search the products on search bar to purchase the products. In general, the process requires the customer to be seated before beginning. This study aims to discuss the consumer's perception of online products purchasing and delivery services in Karur. The survey was conducted among 100 respondents who were selected based on purposive sampling. The research focuses on the study and analysis of data collected from all those users who already use online delivery services.

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A Study on Determining The Factors Impacting Consumer Perception Regarding The Online Food Delivery Apps in Guwahati

GIS Business ◽

10.26643/gis.v14i6.14324 ◽

2019 ◽

Vol 14 (6) ◽

pp. 521-542

Author(s):

Saroj Kumar Koiri ◽

Subhadeep Mukherjee ◽

Smriti Dutta

Keyword(s):

Fast Food ◽

Food Industry ◽

Food Preferences ◽

Food Delivery ◽

Consumer Perception ◽

Fast Food Industry ◽

The People ◽

Indian Food ◽

Consumer's Perception

Today, fast food industry is growing rapidly in India. It is getting adapted and also being upgraded according to Indian food requirements. Online food ordering apps and sites are developed in order to meet consumer’s expectations. With the changing food preferences and habits of the people, it is necessary to know what factors impact the consumer’s perception regarding online food delivery apps.

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Coronavirus Effects on the U.S. Unemployment: Evidence from Google Trends

SSRN Electronic Journal ◽

10.2139/ssrn.3559860 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hakan Yilmazkuday

Keyword(s):

Google Trends ◽

The U.S

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An Epidemic Avian Influenza Prediction Model Based on Google Trends

Letters in Organic Chemistry ◽

10.2174/1570178615666180724103325 ◽

2019 ◽

Vol 16 (4) ◽

pp. 303-310 ◽

Cited By ~ 14

Author(s):

Yi Lu ◽

Shuo Wang ◽

Jianying Wang ◽

Guangya Zhou ◽

Qiang Zhang ◽

...

Keyword(s):

Linear Regression ◽

Avian Influenza ◽

Prediction Model ◽

Multiple Linear Regression ◽

Google Trends ◽

Health Providers ◽

Human Beings ◽

Success Rates ◽

New Model ◽

Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

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COVID-19 Related Web Search Behaviors: An Analytical Study of Google Trends-based Query Volumes in Indonesia (Preprint)

10.2196/preprints.23650 ◽

2020 ◽

Author(s):

Vita Widyasari ◽

Karisma Trinanda Putra ◽

Jiun-Yi Wang

Keyword(s):

Web Search ◽

Public Awareness ◽

Relevant Information ◽

Community Response ◽

Google Trends ◽

P Value ◽

Basic Information ◽

First Case ◽

The Government ◽

Economic Anxiety

BACKGROUND The volume of search keywords on Google can be used as a reference to an ongoing online trend during COVID-19 pandemic. OBJECTIVE This study was aimed to estimate the responsiveness and public awareness in early days of the COVID-19 outbreak in Indonesia using Google Trends relative search volumes (RSV). METHODS Sixty terms or keywords forming six topics included in the analysis were basic information, prevention, government policy, socio-economic, anxiety, and other issues related to COVID-19. All these keywords were checked for surveillance purposes between January 1 and May 4, 2020. The Python programming language was used for data mining from Google Trends databases. Correlation analysis was conducted to examine the correlations between the incidence of COVID-19 and the search terms. RESULTS Community response and awareness in the six topics were associated with the number of COVID-19 cases (r range between 0.570-0.825, P-value<.005). Before the first case announced in Indonesian, the prominent topics were basic information and other issues. One month after the first case, all topics experienced an increase in RSV. In the phase of outbreak, socio-economic and anxiety got much more attentions. CONCLUSIONS The government should consider to optimize the internet as a media for timely delivering most relevant information and dynamically respond massive queries, and improve health communications to increase public awareness and intention to prevent the disease.

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Public Awareness of Preventive Measures against COVID-19: A Google Trends Analysis (Preprint)

10.2196/preprints.25068 ◽

2020 ◽

Author(s):

Alex Mok ◽

Oliver Oi Yat Mui ◽

Kwan Pui Tang ◽

Chi-Fai NG ◽

Sunny Hei Wong ◽

...

Keyword(s):

Public Health ◽

Temporal Trend ◽

Preventive Measures ◽

Public Awareness ◽

Google Trends ◽

Online Searching ◽

Effective Measure ◽

Heat Maps ◽

Hand Sanitizer ◽

Incidence Data

BACKGROUND The 2019 coronavirus pandemic (COVID-19) has led to increase in global awareness of related public health preventive measures. The public awareness can be reflected by online searching trends of major search engines, namely Google Trends. OBJECTIVE This study aims to interpret online searches of COVID-19 related public health preventive measures and to identify possible correlations between early search trends and progression of the pandemic. METHODS Search data from five queries “Mask”, “Hand Washing”, “Social Distancing”, “Hand Sanitizer”, and “Disinfectant” were extracted from Google Trends (GT) in the form of Relative Search Volumes (RSV). Global incidence data of COVID-19 was obtained from January 1st to June 30th 2020. Subsequently, the data were analyzed and illustrated in forms of a global temporal RSV trend diagram, a geographical RSV distribution chart, scatter graphs comparing regional RSV with average daily cases; and heat-maps comparing temporal trend of RSV with average daily cases. RESULTS Global temporal trend revealed multiple surges in RSV, which were temporally associated with certain COVID news events. Geographical distribution showed differences of query interests among regions. Although scatter graphs failed to illustrate strong correlations between regional RSV and average daily cases, the heat-maps were able to demonstrate patterns of early RSV peaks in countries with lower average daily cases, for queries “Mask”, “Hand Sanitizer”, and “Disinfectant”, upon incorporating with the temporal element into analysis. CONCLUSIONS Early public awareness of multiple preventive measures was observed in countries with lower daily average cases. Public health authorities may look into early public awareness as an effective measure for future disease control.

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