This study integrates theories of task complexity and cognitive stopping rule to understand how complexity of information environment impacts uncertainty, effectiveness and efficiency of consumers’ decision process. Using the reviews provided by an online retailer, we develop an e-commerce environment with three levels of complexity: high with raw textual reviews, medium with attribute-level review summaries and low with web personalization strategy based on attribute preferences extracted from online reviews. In a controlled lab experiment, users took buying decisions under different levels of complexity. Our analyses of clickstream data showed that users’ effectiveness and efficiency were the highest in review based personalized environment. However, between groups who received summarized and textual reviews, the latter demonstrated apparently higher effectiveness and efficiency in decision making, which went against our anticipation. Further investigation showed that users simplified decision process when exposed to raw reviews. These results further inform reviews-based personalization strategy in e-commerce.

本研究将任务复杂性理论和认知停止规则理论相结合，以了解信息环境的复杂性如何影响消费者决策过程的不确定性、有效性和效率。使用在线零售商提供的评论，我们开发了一个具有三个复杂级别的电子商务环境：原始文本评论的高，属性级评论摘要的中和基于从在线评论中提取的属性偏好的网络个性化策略的低。在受控实验室实验中，用户在不同的复杂程度下做出购买决定。我们对点击流数据的分析表明，在基于评论的个性化环境中，用户的有效性和效率是最高的。然而，在接受总结和文本评论的群体之间，后者在决策方面表现出明显更高的有效性和效率，这与我们的预期背道而驰。进一步的调查表明，用户在接触原始评论时简化了决策过程。这些结果进一步为电子商务中基于评论的个性化策略提供了信息。