Determinants of Online Purchase Intention Among Young Consumers in Punjab

Kansra, Pooja; Oberoi, Sumit

doi:10.4018/ijsesd.292041

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…The probit model for limited dependent variables was selected to avoid this problem. Similar studies on determinants of purchase intention have also used the probit model, such as the one presented by Kansra and Oberoi (2022). However, the standard errors in models with binary dependent variables are not robust, so heteroscedasticity problems arise.…”

Section: Model and Methodsmentioning

confidence: 99%

Consumers’ purchase intention of rapid COVID-19 tests

Alejandro

García

Reich

2023

JEFAS

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PurposeThis research aims to analyse the variables related to the purchase intention of COVID-19 rapid tests in Monterrey, Mexico's metropolitan area.Design/methodology/approach The chosen method was probit regression. The results show that purchase intention depends on the consumer's perceived value and the perception of having a potential contagion and/or presenting symptoms related to the virus. Regarding limitations, the sampling method used in this investigation is a nonprobabilistic convenience approach delivered through a digital platform, which may not be the first option in other contexts.FindingsThe findings indicate that the probability of the purchase intention of rapid COVID tests increases when consumers perceive symptoms of the disease and when they have higher education or are female rather than concerning price or income, as suggested by classical demand theory.Research limitations/implicationsProbabilistic sampling was impossible due to the difficulty of collecting surveys during the COVID-19 pandemic. Instead, a nonprobabilistic sample of a representative random selection of different zip codes from the responses received was considered.Originality/valueThe originality of the paper is its contribution to consumer behaviour during the COVID-19 pandemic in a Latin American context.

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Section: Model and Methodsmentioning

confidence: 99%

Consumers’ purchase intention of rapid COVID-19 tests

Alejandro

García

Reich

2023

JEFAS

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“…The theoretical foundations of many papers found in depicting consumer's intention to purchase mostly tends to measure the influence of product quality, price, attitude, etc. some studies including demographic in their prediction such as (Chakraborty & Paul, 2022;Kansra & Oberoi, 2022;Yilmaz & Kahveci, 2022) who used gender and age. Even Deep Learning studies such as (Liu et al, 2021) considered gender in their prediction model.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

“…Even Deep Learning studies such as (Liu et al, 2021) considered gender in their prediction model. Regarding features of the product and online website, factors such as personal privacy, risk in giving card details, and tangibility of the product have been described as factors that negatively/positively influence consumers' intention to purchase (Kansra & Oberoi, 2022).…”

Section: Theoretical Foundationsmentioning

confidence: 99%

Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review

Alaros¹,

Marjani²,

Shafiq³

et al. 2022

Indonesian J. Sci. Technol

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Consumer/customer relationship management (CRM) can potentially influence business as it predicts changes in people’s perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers’ intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics.

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Determinants of Online Purchase Intention Among Young Consumers in Punjab

Cited by 2 publications

References 33 publications

Consumers’ purchase intention of rapid COVID-19 tests

Consumers’ purchase intention of rapid COVID-19 tests

Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review

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