The implementation of augmented reality (AR) systems in production environments is associated with a variety of advantages, such as productivity gains, lower costs and reduced operating times. Despite these potential benefits, the lack of user acceptance due to issues such as privacy concerns constitutes a barrier to diffusion in workplace environments. In order to better understand the issues surrounding AR acceptance, we employed a conjoint study to empirically examine the trade-offs that future employees perceive when being involved in adopting such systems. Using a hierarchical Bayes estimation, we discover that functional benefits such as productivity gains and safety enhancement are the main adoption drivers. In contrast, future employees indeed perceive monitoring through head-worn AR devices as negative. However, a complementary cluster analysis indicates that not all respondents share a negative view of monitoring, and one third are likely to share their performance data with employers. We identify three groups with significantly different utility patterns. Furthermore, we monetize the value of privacy to determine compensation payments. The results may help employers, decision-makers, software solution providers as well as researchers in the information systems domain to better understand the factors surrounding acceptance of AR assistance systems. To the best of our knowledge, we are the first to address this issue using conjoint analysis.

在生产环境中实施增强现实（AR）系统具有多种优势，例如提高生产率，降低成本和减少操作时间。尽管有这些潜在的好处，但是由于诸如隐私问题之类的问题而导致用户缺乏接受感，这构成了在工作环境中传播的障碍。为了更好地理解围绕AR接受的问题，我们进行了一项联合研究，以经验方式检验未来员工参与采用此类系统时所要进行的权衡取舍。使用分层贝叶斯估计，我们发现诸如提高生产率和提高安全性之类的功能优势是主要的采用推动力。相比之下，未来的员工确实将通过头戴式AR设备进行的监控视为负面的。然而，互补的聚类分析表明，并非所有受访者都对监控持负面看法，三分之一的受访者可能会与雇主共享其绩效数据。我们确定了三类效用模式截然不同的群体。此外，我们将隐私权的价值货币化，以确定赔偿金。结果可能有助于雇主，决策者，软件解决方案提供商以及信息系统领域的研究人员更好地了解围绕AR辅助系统被接受的因素。据我们所知，我们是第一个使用联合分析解决此问题的人。我们确定了三类效用模式截然不同的群体。此外，我们将隐私权的价值货币化，以确定赔偿金。结果可能有助于雇主，决策者，软件解决方案提供商以及信息系统领域的研究人员更好地了解围绕AR辅助系统被接受的因素。据我们所知，我们是第一个使用联合分析解决此问题的人。我们确定了三类效用模式截然不同的群体。此外，我们将隐私权的价值货币化，以确定赔偿金。结果可能有助于雇主，决策者，软件解决方案提供商以及信息系统领域的研究人员更好地了解围绕AR辅助系统被接受的因素。据我们所知，我们是第一个使用联合分析解决此问题的人。