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数据分析与知识发现  2020, Vol. 4 Issue (11): 74-83     https://doi.org/10.11925/infotech.2096-3467.2020.0161
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
线索一致性对共享住宿平台用户购买决策的影响研究:房客文本信息和房源图片信息的交互效应*
池毛毛1,2(),潘美钰1,王伟军3
1华中师范大学信息管理学院 武汉 430079
2华中师范大学湖北省电子商务研究中心 武汉 430079
3华中师范大学青少年网络心理与行为教育部重点实验室 武汉 430079
Impacts of Cue Consistency on Shared Accommodation Bookings: Interaction Between Texts and Images
Chi Maomao1,2(),Pan Meiyu1,Wang Weijun3
1School of Information Management, Central China Normal University, Wuhan 430079, China
2E-commerce Research Center of Hubei Province, Central China Normal University, Wuhan 430079, China
3Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
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摘要 

【目的】 探索共享平台中不同主体的信息线索及其线索一致性对用户购买决策的影响机制。【方法】 以线索一致性理论为基础,从房客文本线索(UGC)和房东图片线索(MGC)角度构建模型,利用爬虫技术在共享住宿平台网站上获取相应房源数据,研究信息线索一致性对消费者购买决策的影响。【结果】 UGC的文本线索和MGC的暖色调图片显著正向影响用户购买决策,UGC与MGC的信息线索一致性程度对用户购买决策具有显著正向影响。【局限】 图片参数的提取还有待扩展,未来可以进一步对识别房源基础设施展开研究。【结论】 本研究关注平台双边的信息线索,为共享住宿平台和房东如何有效建立和利用信息线索提供参考和建议。

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池毛毛
潘美钰
王伟军
关键词 线索一致性图片信息文本信息共享住宿平台用户购买决策    
Abstract

[Objective] This paper examines the influences of clue consistency on users’ booking decisions on shared accommodations. [Methods] First, based on the Clue Consistency Theory, we constructed a research model from the perspective of User-Generated Content (UGC) and Marketer-Generated Content (MGC). Then, we conducted an empirical study on data collected from Xiaozhu.com - a well-known short-term renting website in China. Finally, we examined the impacts of clue consistency on renters’ purchase decisions. [Results] The purchase decision of tenants was positively correlated to the text clues of UGC and warm color pictures of MGC. Also, the information consistency between UGC and MGC posed significant positive impacts on purchase decisions. [Limitations] More image parameters need to be extracted in future research, which will help us identify home styles. [Conclusions] This study could help shared accommodation platforms and landlords improve their services.

Key wordsCues Consistency    Picture Information    Text Information    Shared Accommodation Platform    Purchasing Decisions
收稿日期: 2020-03-05      出版日期: 2020-12-04
ZTFLH:  G203  
基金资助:*本文系国家自然科学基金青年项目“电商平台演化对平台绩效的影响机理研究:基于复杂适应系统的视角”(71801104);国家自然科学基金面上项目“基于屏幕视觉热区的网络用户偏好提取及交互式个性化推荐研究”(71571084);中央高校基本科研业务费项目“互联网+转型背景下传统企业参与电商平台的价值实现研究”的研究成果之一(CCNU20TS024)
通讯作者: 池毛毛     E-mail: chimaomao@aliyun.com
引用本文:   
池毛毛,潘美钰,王伟军. 线索一致性对共享住宿平台用户购买决策的影响研究:房客文本信息和房源图片信息的交互效应*[J]. 数据分析与知识发现, 2020, 4(11): 74-83.
Chi Maomao,Pan Meiyu,Wang Weijun. Impacts of Cue Consistency on Shared Accommodation Bookings: Interaction Between Texts and Images. Data Analysis and Knowledge Discovery, 2020, 4(11): 74-83.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0161      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I11/74
Fig.1  研究模型
构念分类 变量 变量描述与解释
用户购买决策 出租量(RentV 下月历史成交量(T2)-上月历史成交量(T1)
文本线索 在线评论(WordC T1时刻房客评论中描述房源情况的积极词汇(如“明亮”“温馨”)数目
图片线索 色调(Hue T1时刻图片主色调是否为暖色调(虚拟变量)
明度(Bright T1时刻图片主色调的明度:B=Cmax,取自然对数。其中B为明度,Cmax是RGB色彩的最大值。
控制变量 验真标签(VeriF T1时刻是否有验真标签(虚拟变量)
出租价格(Price T1时刻出租的平均价格
房屋面积(Size T1时刻房屋面积(单位为m2
房源类型(Type T1时刻整套出租(Type1)、独立单间(Type2)、合住房间(Type3)(虚拟变量)
房东房源(HNO T1时刻房东当前可预订房源数
Table 1  模型构念及测度
变量 平均值 标准差 最小值 最大值
RentV 6.201 14.189 0 157
WordC 0.469 1.973 0 19
Hue 0.730 0.444 0 1
ln(Bright) 4.832 1.039 0 5.541
VeriF 0.454 0.498 0 1
Price 378.690 336.958 36 4 888
Size 58.794 49.059 10 700
Type3 0.004 0.066 0 1
Type2 0.102 0.303 0 1
Type1 0.894 0.308 0 1
HNO 6.700 7.827 0 54
Table 2  变量的描述性统计
变量 1 2 3 4 5 6 7 8 9 10 11
RentV 1
WordC 0.291** 1
Hue 0.032 -0.058 1
ln(Bright) 0.051* 0.140** 0.116** 1
VeriF 0.182** 0.366** 0.016 0.109** 1
Price -0.034 0.125** -0.002 -0.020 -0.017 1
Size -0.027 0.148** 0.000 -0.003 -0.098** 0.710** 1
Type3 0.021 -0.009 -0.014 -0.017 -0.012 -0.060** -0.026 1
Type2 -0.059** 0.001 -0.028 -0.082** -0.084** -0.145** -0.254** -0.022 1
Type1 0.054** 0.001 0.030 0.084** 0.085** 0.155** 0.255** -0.193** -0.977** 1
HNO 0.498** 0.127** -0.001 0.017 -0.026 0.012 0.011 -0.018 -0.085** 0.087** 1
Table 3  变量相关系数矩阵
自变量 因变量—用户购买决策
模型1 模型2 模型3 模型4 模型5 模型6 模型7
常数项 -0.873
(0.547)
-1.412**
(0.536)
-2.836*
(1.246)
-5.920**
(1.812)
-4.296*
(1.820)
-4.252*
(1.824)
-6.345**
(1.944)
控制变量 VeriF 5.371***
(0.489)
4.620***
(0.481)
4.571***
(0.485)
4.570***
(0.484)
4.630***
(0.481)
4.625***
(0.481)
4.558***
(0.481)
Price -0.002***
(0.001)
-0.002**
(0.001)
-0.002**
(0.001)
-0.002**
(0.001)
-0.002**
(0.001)
-0.002**
(0.001)
-0.002**
(0.001)
Size 0.385
(0.494)
0.144
(0.482)
0.108
(0.484)
0.094
(0.483)
0.124
(0.480)
0.120
(0.480)
0.102
(0.479)
Type2 -0.329
(0.787)
0.006
(0.767)
0.087
(0.770)
0.042
(0.770)
0.063
(0.765)
0.049
(0.766)
0.158
(0.765)
Type3 6.043
(3.868)
6.589?
(3.771)
6.770?
(3.772)
6.977?
(3.770)
7.031?
(3.744)
7.034?
(3.744)
6.955?
(3.738)
HNO 0.787***
(0.029)
0.781***
(0.028)
0.789***
(0.028)
0.792***
(0.028)
0.787***
(0.028)
0.787***
(0.028)
0.784***
(0.028)
主效应 WordC 1.400***
(0.122)
1.396***
(0.122)
1.389***
(0.122)
1.686***
(0.131)
1.755***
(0.228)
1.167***
(0.297)
Hue 1.140*
(0.539)
1.040?
(0.540)
0.926?
(0.537)
0.929?
(0.537)
1.192*
(0.543)
ln(Bright) 0.120
(0.232)
0.785*
(0.367)
0.458
(0.368)
0.444
(0.370)
0.895*
(0.398)
交互效应 Hue × ln(Bright) -1.101*
(0.470)
-0.907?
(0.468)
-0.891?
(0.470)
-1.361**
(0.494)
WordC × Hue -0.990***
(0.166)
-0.097
(0.260)
0.542
(0.333)
WordC × ln(Bright) -0.999***
(0.168)
0.269
(0.445)
WordC × Hue× ln(Bright) -1.479**
(0.481)
F值 148.353*** 112.933*** 120.215*** 108.935*** 103.627*** 94.971*** 88.688***
R2 0.261 0.265 0.302 0.303 0.313 0.313 0.315
Adj. R2 0.259 0.263 0.299 0.300 0.310 0.310 0.312
样本量 2 529 2 516 2 516 2 516 2 516 2 516 2 516
Table 4  模型估计结果
Fig.2  三方交互图
自变量 因变量—下一期房源出租量
模型8 模型9 模型10 模型11 模型12 模型13 模型14
常数项 -23.416*
(9.290)
-32.582***
(9.091)
-49.695*
(21.136)
-85.089**
(30.768)
-57.676?
(30.909)
-59.290*
(30.976)
-86.206**
(33.041)
控制变量 VeriF 113.54***
(8.298)
100.774***
(8.165)
100.351***
(8.222)
100.34***
(8.219)
101.36***
(8.166)
101.53***
(8.169)
100.67***
(8.171)
Price -0.033**
(0.011)
-0.020?
(0.010)
-0.021*
(0.010)
-0.021*
(0.010)
-4.316
(0.010)*
-0.021*
(0.010)
-0.021*
(0.010)
Size -0.065
(8.377)
-4.161
(8.174)
-4.656
(8.208)
-4.823
(8.206)
-4.316
(8.151)
-4.191
(8.153)
-0.021*
(0.010)
Type2 14.596
(13.350)
20.299
(13.023)
21.602?
(13.073)
21.084
(13.073)
21.440?
(12.985)
21.986?
(13.004)
23.399?
(13.006)
Type3 229.29***
(62.636)
238.540***
(61.064)
239.588***
(61.068)
241.73**
(61.065)
242.66***
(60.654)
242.60***
(60.658)
241.62***
(60.606)
HNO 15.202***
(0.485)
15.107***
(0.473)
15.260***
(0.476)
15.294***
(0.476)
15.204***
(0.473)
15.206***
(0.473)
15.162***
(0.473)
主效应 WordC 23.796***
(9.091)
23.663***
(2.067)
23.582***
(2.067)
28.581***
(2.220)
26.017***
(3.867)
18.456***
(5.049)
Hue 5.732
(9.149)
4.583
(9.175)
2.659?
(9.119)
2.566
(9.120)
5.956
(9.228)
ln(Bright) 2.621
(3.940)
10.249?
(6.225)
0.458*
(0.368)
5.288
(6.289)
11.099
(6.762)
交互效应 Hue × ln(Bright) -12.638*
(7.986)
-9.358
(7.951)
-9.954
(7.986)
-16.008?
(8.393)
WordC × Hue -16.70***
(2.819)
3.577
(4.416)
11.787*
(5.651)
WordC × ln(Bright) -16.35***
(2.854)
-0.043
(7.568)
WordC × Hue× ln(Bright) -19.018*
(8.177)
F值 196.429*** 196.185*** 153.927*** 138.868*** 131.152*** 120.261*** 111.622***
R2 0.318 0.353 0.356 0.357 0.365 0.366 0.367
Adj. R2 0.317 0.351 0.354 0.354 0.363 0.363 0.364
样本量 2 529 2 516 2 516 2 516 2 516 2 516 2 516
Table 5  稳健性检验
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