消费者的个性、生活方式和人口特征如何预测SVOD类型和SVOD平台的消费

Anthony Palomba
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引用次数: 1

摘要

流媒体视频点播(SVOD)服务由数字媒体内容创作和内容分发组成,提供了大量可在各种不同技术平台上播放的类型内容。此外,这些数字服务还配备了收集消费者数据和信息的设备。然而,这些服务并没有捕捉到广泛的消费者人口统计、生活方式或个性信息。为了解决这一差异,收集消费者的完整人口统计、个性和生活方式等外部信息有助于提高SVOD消费者行为知识。本研究考察了消费者的人口统计、生活方式和个性如何预测SVOD类型消费和SVOD平台消费。执行并传播了一项调查,以收集这些维度上的消费者信息。建立了多元线性回归和结构方程模型来解释方差。消费者的人口统计、生活方式和个性信息可以预测SVOD类型消费和SVOD平台消费。原创性/价值媒介选择和特质理论尚未被用于理解消费者媒介消费背后无法解释的差异,而通常被用于预测消费者之间的媒介消费差异。这些发现表明,虽然收集和分析数字消费者接触点是必要的,但营销人员不应忽视容易获得的消费者数据,其中大部分数据决定了消费选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How consumers' personalities, lifestyles and demographics predict SVOD genre and SVOD platform consumption
PurposeStreaming video on demand (SVOD) services are comprised of digital media content creation and content distribution that provide a vast array of genre content playable on an assortment of different technology platforms. Additionally, these digital services are equipped to collect data and information on consumers. However, these services do not capture extensive consumer demographics, lifestyles or personalities information.Design/methodology/approachTo resolve this discrepancy, collecting external information such as complete demographics, personalities and lifestyles of consumers can be useful in advancing SVOD consumer behavior knowledge. This study examined how consumer demographics, lifestyles and personalities may predict SVOD genre consumption and SVOD platform consumption. A survey was executed and disseminated to collect consumer information across these dimensions. Multiple linear regressions and a structural equation model were formed to explicate variance.FindingsConsumer demographics, lifestyles and personalities’ information do predict SVOD genre consumption and SVOD platform consumption.Originality/valueMedia selection and trait theory have not been applied to understanding unexplained variance behind consumer media consumption, and are often used to predict media consumption variance among consumers. These findings illustrate that, while digital consumer touchpoints are necessary to collect and analyze, marketers should not lose sight of easily-obtainable consumer data, much of which dictates consumption choices.
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