An investigation of e-marketing and its effect on the consumer buying decision during COVID-19 pandemic in Aceh Province, Indonesia: A mediating role of perceived risk
Notice bibliographique
Résumé
Today, Coronavirus is a pandemic that has spread worldwide and causes many problems, including socio economic problems in society. Therefore, e-marketing has an essential function in acquiring new customers, generating leads, and generating revenue for your business by reaching customers interested in your products and services. A digital platform such as web marketing is online marketing to prospective leads and high-value consumers. Thus, the present study examined the usage of e-marketing as a model for buying decisions moderated by perceived risk during the covid-19 pandemic in Aceh Province, Indonesia. This quantitative study involved 325 respondents and was collected through a survey by filling out the questionnaires. Also, e-marketing is measured by perceived usefulness, perceived ease of use as exogenous variables. Therefore, perceived risk is a mediating variable, and the consumer buying decision is an endogenous variable. The data were analyzed using Structural Equation Modelling – Analysis of Moments Structure (SEM-AMOS). The results showed that e-marketing (perceived usefulness and ease of use) positively and significantly affects consumer buying decisions. This study also applied the Sobel test and indicated that perceived risk mediates perceived usefulness and ease toward consumer buying decisions. In conclusion, this study has successfully examined the relationship between e-marketing via perceived usefulness and perceived ease of use. Also, it proves that perceived risk plays a mediating role (partial mediator) in the relationship of perceived usefulness and ease of use on consumer buying decisions. This tested model has become a formulation, especially in marketing science, where it turns out that during the covid-19 pandemic, the buying decision model in the marketplace is a function of the perceived usefulness and perceived ease of use of consumers towards the marketplace, as well as the perceived risk of the marketplace as a partial mediator. So that marketplace manufacturers can drive consumer buying decisions by consumers by creating a strengthening of perceived usefulness and perceived ease of use for consumers, thereby affecting consumers' perceived risk and impacting their buying decisions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».