Although collecting personal information about consumers is crucial for firms and marketers, understanding of when and why consumers accept or reject information collection remains limited. The authors conceptualize a privacy calculus that represents a consumer’s trade–off of the valence and uncertainty of the consequences of the collection, storage, and use of personal information. For example, usage-based car insurance requires drivers to share data on their driving behavior in exchange for a discount (certain benefit) but at the risk of third parties intercepting location data for malicious use (uncertain disadvantage). Building on this conceptualization, the authors develop the privacy calculus (PRICAL) index. They empirically confirm the validity of the items (Study 1) and the index as a whole (Study 2). The PRICAL index is generally applicable and improves the explanation of behavioral intentions (Study 2) and actual behavior (Study 3), compared with currently used constructs (e.g., privacy concern, trust). Overall, the PRICAL index allows managers to understand consumers’ acceptance of information collection regarding financial, performance, psychological, security, social, and time-related consequences, which the authors demonstrate using the top five most valuable digital brands (Study 4).

尽管收集有关消费者的个人信息对公司和营销人员至关重要，但对消费者何时以及为何接受或拒绝信息收集的了解仍然有限。作者概念化了一种隐私计算，它代表了消费者在收集、存储和使用个人信息的后果的效价和不确定性之间的权衡。例如，基于使用情况的汽车保险要求驾驶员分享其驾驶行为数据以换取折扣（一定的利益），但存在第三方拦截位置数据以进行恶意使用的风险（不确定的劣势）。在此概念化的基础上，作者开发了隐私计算 (PRICAL) 索引。他们凭经验证实了项目（研究 1）和整个指数（研究 2）的有效性。与目前使用的结构（例如，隐私问题、信任）相比，PRICAL 指数普遍适用并改进了对行为意图（研究 2）和实际行为（研究 3）的解释。总体而言，PRICAL 指数使管理者能够了解消费者对有关财务、绩效、心理、安全、社会和时间相关后果的信息收集的接受程度，作者使用前五个最有价值的数字品牌证明了这些信息（研究 4）。