This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469 Canadian youths aged 16-24, we apply multigroup Partial Least Squares Structural Equation Modeling to compare males (N=241) and females (N=174) (total N = 415) across five privacy constructs: Perceived Privacy Risks (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy Protective Behavior (PPB). Results provide exploratory evidence of gender heterogeneity in selected pathways. The direct effect of PPR on PPB is stronger for males (Male: \b{eta} = 0.424; Female: \b{eta} = 0.233; p < 0.1), while the indirect effect of ATT on PPB via PSE is stronger for females (Female: \b{eta} = 0.229; Male: \b{eta} = 0.132; p < 0.1). Descriptive analysis of non-binary (N=15) and prefer-not-to-say participants (N=39) shows lower trust and higher perceived risk than the binary groups, motivating future work with adequately powered gender-diverse samples. Overall, the findings provide exploratory evidence that gender may moderate key privacy pathways, supporting more responsive transparency and control interventions for youth SVA use.

翻译：本文探究了性别如何塑造青少年智能语音助手生态系统中的隐私决策。基于对469名16-24岁加拿大青少年的调查数据，我们采用多组偏最小二乘法结构方程模型，在五个隐私构念上比较了男性（N=241）与女性（N=174）（总样本量N=415）：感知隐私风险、感知隐私收益、算法透明度与信任、隐私自我效能和隐私保护行为。研究结果提供了部分路径中性别异质性的探索性证据。感知隐私风险对隐私保护行为的直接效应在男性中更强（男性：β=0.424；女性：β=0.233；p<0.1），而算法透明度与信任通过隐私自我效能对隐私保护行为的间接效应在女性中更强（女性：β=0.229；男性：β=0.132；p<0.1）。对非二元性别（N=15）和不愿透露性别（N=39）参与者的描述性分析显示，其信任水平低于二元性别组而感知风险更高，这为未来采用充分统计效力的性别多样化样本开展研究提供了动力。总体而言，这些发现提供了性别可能调节关键隐私路径的探索性证据，支持为青少年使用智能语音助手制定更具响应性的透明度与干预措施。