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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (11): 74-83    DOI: 10.11925/infotech.2096-3467.2020.0161
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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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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 words： Cues Consistency      Picture Information      Text Information      Shared Accommodation Platform      Purchasing Decisions     
Received: 05 March 2020      Published: 04 December 2020
ZTFLH:  G203  
Corresponding Authors: Chi Maomao     E-mail: chimaomao@aliyun.com
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Cite this article:

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.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0161     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I11/74

Research Model
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
Constructs and Measurement
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
Descriptive Statistics
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
Correlation Coefficient of Variables
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
Output of Model Estimation
Three-Way Interaction
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
Robustness Test
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