This study aimed to explore the two aspects of the masstige mean score scale (MMSS) in the mobile payment system (MPS) context. The aspects included measuring the masstige value of MPS and validating MMSS as a measure while predicting the brand equity of prestigious mass brands. The research framework was developed by synthesizing the past literature on masstige marketing and brand equity theory. Responses were obtained using a survey of 438 mobile payment application users in India using a structured questionnaire. Masstige mean value of MPS brands was obtained through MMSS. Covariance-based structure equation modelling established the association between masstige value obtained through MMSS and overall brand equity (OBE). Non-compensatory data analysis method, artificial neural network (ANN), was used to predict the brand equity of prestigious mass brands with MMSS items as predictors. The analysis of masstige mean value indicated the existence of mass prestige for MPS brands. Furthermore, the path analysis indicated that the masstige value positively contributed to OBE. The ANN's predictive analysis established that MMSS items as predictors are 93% accurate in predicting brand equity for prestigious mass brands. The validation of the prestigiousness of the technology-based fintech MPS would be helpful to brand managers to target prestige-seeking consumers and charge a high price for premium and customized services. In addition, the methodology promulgated to predict CBBE of prestigious mass brands based on MMSS items would be helpful to managers in determining brand equity of prestigious mass brands in the emerging economies. This study is the first to empirically validate the MMSS as a measure of brand equity for prestigious mass brands. It would help future researchers propagate the use of MMSS in the study of brand equity in varied contexts; also, this study significantly contributes to the literature on masstige explaining the masstige value of MPS brands in emerging markets.

中文翻译：

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使用 Masstige 平均分量表衡量著名大众品牌的消费者品牌资产

本研究旨在探讨移动支付系统（MPS）背景下大众平均分量表（MMSS）的两个方面。这些方面包括衡量 MPS 的大众价值并验证 MMSS 作为一种衡量标准，同时预测著名大众品牌的品牌资产。该研究框架是通过综合以往有关大众营销和品牌资产理论的文献而制定的。我们使用结构化调查问卷对印度 438 名移动支付应用程序用户进行了调查，获得了答复。MPS品牌的Masstige平均值是通过MMSS获得的。基于协方差的结构方程模型建立了通过 MMSS 获得的大众价值与整体品牌资产 (OBE) 之间的关联。采用非补偿性数据分析方法——人工神经网络（ANN），以 MMSS 项目作为预测变量来预测知名大众品牌的品牌资产。大众均值分析表明MPS品牌存在大众声望。此外，路径分析表明，masstige 值对 OBE 有正向贡献。ANN 的预测分析表明，MMSS 项目作为预测因子在预测著名大众品牌的品牌资产方面的准确率高达 93%。验证基于技术的金融科技 MPS 的声誉将有助于品牌经理瞄准追求声望的消费者，并为优质和定制服务收取高价。此外，发布的基于MMSS项目预测知名大众品牌CBBE的方法将有助于管理者确定新兴经济体知名大众品牌的品牌资产。本研究首次通过实证验证 MMSS 作为知名大众品牌品牌资产的衡量标准。它将帮助未来的研究人员在不同背景下的品牌资产研究中推广 MMSS 的使用；此外，这项研究对解释 MPS 品牌在新兴市场中的大众品牌价值的大众品牌文献做出了重大贡献。