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Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

The purpose of this paper is to examine the joint effect of two composite characteristics –openness to experience (OE) and nostalgia proneness (NP) – on product involvement (PI) and whether brand loyalty (BL) is a result of this PI.

In accordance with this purpose, a model suggesting that OE dimension of the five-factor model and NP influences PI and PI influences BL was developed and tested. The data used in the study were obtained from 1,392 participants from the Thrace region of Turkey. The authors use a structural equation model to test and confirm hypothesis.

OE influences PI and hence BL. On the other hand, it has been concluded that NP has no significant influence on PI.

This is the first study to examine the influence of OE, one of the personality traits, and NP on BL. In this study, only OE, which is one of the five-factor personality traits, has been examined. Studies in the future may research the relationship between other personality traits and NP, PI and BL.

This paper provides managerial insights into why consumers’ personality traits and NP need to be taken into consideration in creating BL.

To the best of authors’ knowledge, the influence of OE and NP on BL has not been addressed in the current literature. Personality traits and NP are closely related to individuals’ behaviors as a consumer. Understanding the factors that influence consumer purchase decision processes is of crucial importance to managers and researchers alike. The paper is of great value for firms that consider enhance BL.