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1. Introduction
In the era of big data, individuals are inundated with vast amounts of information on a daily basis. However, due to limitations in information processing capacity, making optimal decisions can be challenging, if not impossible, for most people (Lachman et al., 2015). This challenge is exacerbated by the “garbage in, garbage out” effect, where low information quality further constrains decision-making capabilities (Kilkenny and Robinson, 2018). Recognizing the pivotal role of information quality in fostering efficient communication and decision-making, the evaluation of information quality has emerged as a critical issue (Lee et al., 2002). Nowhere is this problem more critical than in the healthcare domain, given the profound implications of medical errors. Studies have underscored that high information quality is fundamental to successful communication, enabling physicians to make precise judgments and enhancing patients’ satisfaction (Brown et al., 2019). Wang and Strong's (1996) information quality framework has served as a valuable guide for evaluating information quality. Numerous studies across various domains have been conducted based on this framework, demonstrating its utility (e.g. McKinney et al., 2002; Wixom and Watson, 2001; Zhu and Gauch, 2000). However, research indicated that this classical model requires expansion and adjustment to address new contexts (Lee et al., 2002), particularly with the recent orientation toward digitization (Maass et al., 2018). In response to this need, this paper introduced information normalization as a novel dimension of information quality and examined its role in communication within the context of online healthcare consultation.
The concept of information normalization can be traced back to Bailey and Pearson's (1983) study on computer user satisfaction. In their research, they identified 39 factors of computer-output information that influenced user satisfaction, encompassing aspects such as timeliness, completeness, format of output, and relevancy. Specifically, the factor of format was described as “the material design of the layout, and the display of the output contents”. While not formally termed as such, subsequent researchers recognized this concept as the precursor to information normalization (DeLone and McLean, 1992; Doll and Torkzadeh, 1988; Wang and Strong, 1996). Over time, scholars have extensively discussed and expanded upon the factor of format, enriching the model by incorporating dimensions such as concise representation, clarity, understandability, interpretability, and consistency (DeLone and McLean,...