Can the Formation of Human-computer Interaction Perception Promote Customers' Adoption Behavior: From the Perspective of Media Richness Theory
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摘要: 移动网络技术和人工智能越来越多地应用到新零售中，如何利用人机交互设计发挥智能服务技术对顾客采纳行为的积极影响至关重要。从媒介丰富度理论视角出发，系统分析新零售情境中人机交互感知的形成机理及其对顾客采纳行为的影响，通过线上线下混合调查方法，对386份新零售购物者样本进行实证分析。结果发现：信息丰富度和社会互动需求均对人机交互感知和顾客采纳行为产生了正向影响；人机交互感知在信息丰富度和社会互动需求对顾客采纳行为的影响中发挥了积极的中介作用；信息素养和数字技能正向调节了信息丰富度和社会互动需求对人机交互感知的影响。研究结论扩展和丰富了现有人机交互、顾客采纳行为以及营销理论研究的边界及相关文献，对新零售企业改善人机交互设计以增强顾客关系具有一定启示作用。Abstract: When mobile network technology and artificial intelligence are more and more applied to new retail, it is important for us to know how to use human-computer interaction design to give full play to the positive impact of intelligent service technology on customers' adoption behavior. From the perspective of media richness theory, this study systematically analyzes the formation mechanism of human-computer interaction perception and its impact on customers' adoption behavior in the new retail context. Through the empirical analysis of 386 samples of new retail shoppers with the online and offline mixed survey method, the results show that information richness and social interaction needs both have a positive impact on human-computer interaction perception and customers' adoption behavior; human-computer interaction perception plays an active intermediary role in the impact of information richness and social interaction needs on customer adoption behavior; moreover, information literacy and digital skills positively regulate the impact of information richness and social interaction needs on human-computer interaction perception. The findings of this study expand and enrich the boundaries and relevant literature of existing human-computer interaction, customers' adoption behavior and even marketing theory, and have important enlightenment for new retail enterprises to improve human-computer interaction design to enhance customer relationship.
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表 1 样本描述性统计
人、% 变量 类别 人数 占比 变量 类别 人数 占比 性别 男 174 45.1 个人月收入 3 000元以下 210 54.4 女 212 54.9 3 000~5 000元 85 22.2 年龄 18岁以下 4 1.0 5 001~8 000元 31 0.8 18~26岁 233 60.4 8 001~15 000元 40 10.4 27~35岁 141 36.5 15 000元以上 20 5.2 36~40岁 6 1.6 线上购物年龄 1年以内 10 2.6 40岁以上 2 0.5 1~2年 43 11.1 受教育程度 初中 1 0.3 2~3年 59 15.3 高中或中专 9 2.3 3年以上 274 71.0 大专 149 38.6 从事的职业 个体工商户 2 0.5 大学本科 194 50.3 公司职员 145 37.6 硕士及以上 33 8.5 政府机关或事业单位职工 21 5.5 购物频率 经常 314 81.3 学生 207 53.6 不经常 72 18.7 其他 11 2.8 
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表 2 验证性因子分析结果
变量 测项 因子载荷 信息丰富度
Cronbach's α=0.826
CR=0.832
AVE=0.626该购物网站上的商品信息容易理解 0.906*** 该购物网站上的商品信息客观准确 0.765*** 该购物网站上的商品信息丰富多样(如文字、图片和视频等) 0.687*** 社会互动需求
Cronbach's α=0.786
CR=0.800
AVE=0.580与该购物网站上的其他顾客或员工进行交流和互动让我感到愉快 0.628*** 我喜欢与帮助过我的其他顾客或员工互动 0.670*** 我相信我可以做到和其他顾客或员工的互帮互助 0.947*** 人机交互感知
Cronbach's α=0.911
CR=0.900
AVE=0.528我能方便地通过线上技术与商家进行沟通和交易 0.749*** 该购物网站能让我在服务交流中获益 0.630*** 该购物网站的设置可以满足我个性化的购物需求 0.746*** 该购物网站能使我搜索和获得我真正需要的商品 0.785*** 该购物网站提供的技术很容易操作 0.824*** 使用该购物网站服务的过程清晰易懂 0.848*** 该购物网站对我的请求命令反应很快 0.753*** 该购物网站很快就处理了我提出的问题 0.685*** 信息素养
Cronbach's α=0.871
CR=0.829
AVE=0.549我想要主动获取该购物网站主流的商品信息 0.727*** 我能熟练使用该购物网站的各类信息工具(如买家秀和商品评价) 0.756*** 我能够利用好该购物网站的信息解决问题 0.867*** 我能够对该购物网站的商品信息进行正确的分析和评估 0.824*** 数字技能
Cronbach's α=0.771
CR=0.873
AVE=0.633我能熟练地使用该购物网站的链接分享、导航按钮、菜单和搜索功能 0.933*** 我能熟练地在该购物网站上进行商品管理(如商品收藏) 0.766*** 我经常给他人发送或转发该购物平台的商品信息 0.689*** 顾客采纳行为
Cronbach's α=0.813
CR=0.822
AVE=0.536我会定期使用该购物网站的服务 0.745*** 我会频繁使用该购物网站的服务 0.786*** 我会强烈建议别人使用该购物网站的服务 0.685*** 我打算以后也继续使用该购物网站的服务 0.709*** 注：***表示p < 0.001。表 4同。
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表 3 AVE均方根及潜变量相关系数矩阵
变量 均值 标准差 1 3 4 5 6 7 信息丰富度 5.509 0.976 (0.791) — — — — 社会互动需求 5.168 0.892 0.461 (0.762) — — — 人机交互感知 5.458 1.066 0.664 0.651 (0.727) — — 信息素养 5.528 0.947 0.587 0.567 0.583 (0.741) — 数字技能 5.153 0.979 0.489 0.488 0.612 0.606 (0.796) — 顾客采纳行为 5.085 1.073 0.429 0.490 0.580 0.549 0.561 (0.732) 注：括号内为对应变量的AVE均方根。
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表 4 假设检验结果
假设 具体路径 标准路径系数 t值 结论 H1 信息丰富度→人机交互感知 0.462*** 12.564 支持 H2 信息丰富度→顾客采纳行为 0.258*** 5.336 支持 H3 社会互动需求→人机交互感知 0.438*** 11.895 支持 H4 社会互动需求→顾客采纳行为 0.371*** 7.657 支持 H5 人机交互感知→顾客采纳行为 0.582*** 14.015 支持
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表 5 中介效应的检验结果
效应类型 具体路径 效应值 标准误 t值 p值 95%置信区间CI LLCI ULCI 直接效应 信息丰富度→顾客采纳行为 0.086(ns) 0.061* 1.416 0.158 -0.034 0.206 社会互动需求→顾客采纳行为 0.196 0.054* 3.612 0.000 0.089 0.303 中介效应 信息丰富度→人机交互感知→顾客采纳行为 0.386 0.054* — — 0.289 0.499 社会互动需求→人机交互感知→顾客采纳行为 0.297 0.046* — — 0.212 0.392 注：*为“Boot SE”，ns代表不显著，下表同。
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表 6 信息素养对中介效应的调节分析
变量 效应类型 具体路径 高信息素养 低信息素养 效应值 p值 LLCI ULCI 效应值 p值 LLCI ULCI 信息丰富度 直接效应 信息丰富度→顾客采纳行为 0.090 (ns) 0.150 -0.021 0.223 0.023 (ns) 0.255 -0.044 0.150 中介效应 信息丰富度→人机交互感知→顾客采纳行为 0.451 — 0.329 0.585 0.329 — 0.230 0.460 社会互动需求 直接效应 社会互动需求→顾客采纳行为 0.231 0.000 0.113 0.393 0.097 0.044 0.076 0.119 中介效应 社会互动需求→人机交互感知→顾客采纳行为 0.348 — 0.240 0.466 0.255 — 0.170 0.360
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表 7 数字技能对中介效应的调节分析
变量 效应类型 具体路径 高数字技能 低数字技能 效应值 p值 LLCI ULCI 效应值 p值 LLCI ULCI 信息丰富度 直接效应 信息丰富度→顾客采纳行为 0.092 (ns) 0.098 -0.011 0.153 0.045 (ns) 0.198 -0.032 0.109 中介效应 信息丰富度→人机交互感知→顾客采纳行为 0.459 — 0.337 0.590 0.328 — 0.230 0.451 社会互动需求 直接效应 社会互动需求→顾客采纳行为 0.250 0.000 0.118 0.404 0.089 0.050 0.032 0.111 中介效应 社会互动需求→人机交互感知→顾客采纳行为 0.366 — 0.261 0.478 0.240 — 0.152 0.344
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