Enhancing store brand equity through relationship quality in the retailing industry: evidence from Vietnam

Vo, Duy Minh; Van, Nguyen Thi

doi:10.1080/23311975.2022.2149150

Cited by 2 publications

(1 citation statement)

References 124 publications

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“…2 Iss. 1 July 2024, pp: 18-25 be able to understand consumer buying habits and trends by analyzing product customizations, marketing strategies, and overall customer experience [9].…”

Section: Introductionmentioning

confidence: 99%

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Muta'alimah,

Zarry,

Kurniawan

et al. 2024

PREDATECS

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Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that 60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is performed to compare the results of the algorithms under study. Furthermore, this research was conducted to help offline retailers understand consumption patterns and trends that affect purchases. The algorithms analyzed in this study are K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Network (ANN). As a result, the ANN algorithm obtained the highest confusion matrix results with an Accuracy value of 96.38%, Precision of 100.00%, and Recall of 100.00%. Meanwhile, when the Naive Bayes algorithm was used, the lowest Accuracy value was 57.39%, the Precision value was 57.86%, and when the K-NN algorithm was used, the Recall value was as low as 92.00%. These results indicate that the ANN algorithm is better at classifying offline shopping image data than the K-NN and Naive Bayes algorithms

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“…2 Iss. 1 July 2024, pp: 18-25 be able to understand consumer buying habits and trends by analyzing product customizations, marketing strategies, and overall customer experience [9].…”

Section: Introductionmentioning

confidence: 99%

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Muta'alimah,

Zarry,

Kurniawan

et al. 2024

PREDATECS

View full text Add to dashboard Cite

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Measuring the Effects of Pressure on Consumer Impulse Buying Intention in Online Group Buying

Dzever

Zhao

2023

International Journal of Enterprise Information Systems

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Group buying organizations (GBO) have recently stepped up their well-established practice of employing super-low prices combined with limited product and service supply in a short transaction time span as a means of exerting pressure on consumers. The purpose of this research is to 1) identify and define three types of pressure that are triggered by online group-buying (OGB) promotions, 2) examine the effects of these three types of pressure on consumers' impulse buying behavior, and 3) investigate and produce knowledge about the mediating role of emotion in the relationship between pressure and impulse buying intention (IBI) of consumers. By integrating stimulus-organism-response (SOR) model and consumer impulse buying literature seen from the perspectives of marketing and enterprise information systems respectively, this research has identified three types of pressure (i.e., time pressure, quantity pressure, and price pressure) that influence the impulse buying behavior (IBB) of consumers regarding OGB. The research then examines the mediating role of emotion with reference to pleasure and arousal level. The results of a large-scale online survey combined with an analysis of a structural equation model demonstrate that the above-mentioned three types of pressure have different effects on IBI of consumers. Moreover, the research finds that this is achieved through different mediating mechanisms. Based on the results of the analysis, the authors have made some suggestions that marketers can utilize in developing effective OGB strategies. This research also provides the basis for enterprise information systems (EIS) to develop technologies that will allow organizations to better serve the needs of their OGB customers.

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Enhancing store brand equity through relationship quality in the retailing industry: evidence from Vietnam

Cited by 2 publications

References 124 publications

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Measuring the Effects of Pressure on Consumer Impulse Buying Intention in Online Group Buying

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