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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 wordsCues 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

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.

URL:

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
[1] Ert E, Fleischer A. The Evolution of Trust in Airbnb: A Case of Home Rental[J]. Annals of Tourism Research, 2019,75(2):279-287.
doi: 10.1016/j.annals.2019.01.004
[2] Liang S, Schuckert M, Law R, et al. The Importance of Marketer-Generated Content to Peer-to-Peer Property Rental Platforms: Evidence from Airbnb[J]. International Journal of Hospitality Management, 2020,84(1):102329.
doi: 10.1016/j.ijhm.2019.102329
[3] Zhang S Y, Lee D, Singh P V, et al. How Much is an Image Worth? An Empirical Analysis of Property’s Image Aesthetic Quality on Demand at Airbnb[C]// Proceedings of the 37th International Conference on Information Systems. 2016.
[4] Zhao Y B, Xu X, Wang M S. Predicting Overall Customer Satisfaction: Big Data Evidence from Hotel Online Textual Reviews[J]. International Journal of Hospitality Management, 2019,76(1):111-121.
doi: 10.1016/j.ijhm.2018.03.017
[5] 卢向华, 冯越. 网络口碑的价值——基于在线餐馆点评的实证研究[J]. 管理世界, 2009(7):126-132, 171.
[5] ( Lu Xianghua, Feng Yue. The Value of Online Word-of-Mouth: An Empirical Study Based on Online Restaurant Reviews[J]. Management World, 2009(7):126-132, 171.)
[6] Goh K Y, Heng C S, Lin Z J. Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content[J]. Information Systems Research, 2013,24(1):88-107.
doi: 10.1287/isre.1120.0469
[7] 吴江, 靳萌萌. 在线短租房源图片对消费者行为意愿的影响[J]. 数据分析与知识发现, 2017,1(12):10-20.
[7] ( Wu Jiang, Jin Mengmeng. Online Room Listing Photos Affect Consumer’s Intentions[J]. Data Analysis and Knowledge Discovery, 2017,1(12):10-20.)
[8] Shin D, He S, Lee G M, et al. Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach[J]. MIS Quarterly, 2019. DOI: 10.2139/ssrn.2830377.
pmid: 26752802
[9] Korfiatis N, García-Bariocanal E, Sánchez-Alonso S. Evaluating Content Quality and Helpfulness of Online Product Reviews: The Interplay of Review Helpfulness vs. Review Content[J]. Electronic Commerce Research and Applications, 2012,11(3):205-217.
doi: 10.1016/j.elerap.2011.10.003
[10] Maheswaran D, Chaiken S. Promoting Systematic Processing in Low-Motivation Settings: Effect of Incongruent Information on Processing and Judgment[J]. Journal of Personality and Social Psychology, 1991,61(1):13-25.
doi: 10.1037//0022-3514.61.1.13 pmid: 1890583
[11] Xu Y C, Cai S, Kim H W. Cue Consistency and Page Value Perception: Implications for Web-Based Catalog Design[J]. Information & Management, 2013,50(1):33-42.
[12] Miyazaki A D, Grewal D, Goodstein R C. The Effect of Multiple Extrinsic Cues on Quality Perceptions: A Matter of Consistency[J]. Journal of Consumer Research, 2005,32(1):146-153.
[13] Hu X R, Wu G H, Wu Y H, et al. The Effects of Web Assurance Seals on Consumers’ Initial Trust in an Online Vendor: A Functional Perspective[J]. Decision Support Systems, 2010,48(2):407-418.
doi: 10.1016/j.dss.2009.10.004
[14] Herr P M, Kardes F R, Kim J. Effects of Word-of-Mouth and Product Attribute Information on Persuasion: An Accessibility Diagnosticity Perspective[J]. Journal of Consumer Research, 1991,17(4):454-462.
doi: 10.1086/jcr.1991.17.issue-4
[15] Yoo J, Kim M. The Effects of Online Product Presentation on Consumer Responses: A Mental Imagery Perspective[J]. Journal of Business Research, 2014,67(11):2464-2472.
doi: 10.1016/j.jbusres.2014.03.006
[16] 宋文雯. 寻求设计背后的色彩依据——中国人情感色调认知探究[J]. 设计, 2019,32(24):105-108.
[16] ( Song Wenwen. A Study on the Chinese Cognition of the Tones[J]. Design, 2019,32(24):105-108.)
[17] Mehta R, Zhu R J. Blue or Red? Exploring the Effect of Color on Cognitive Task Performances[J]. Science, 2009,323(5918):1226-1229.
doi: 10.1126/science.1169144 pmid: 19197022
[18] Goldstein K. Some Experimental Observations Concerning the Influence of Colors on the Function of the Organism[J]. Occupational Therapy, 1942,21:147-151.
[19] Bagchi R, Cheema A. The Effect of Red Background Color on Willingness-to-Pay: The Moderating Role of Selling Mechanism[J]. Journal of Consumer Research, 2013,39(5):947-960.
doi: 10.1086/666466
[20] Labrecque L I, Milne G R. Exciting Red and Competent Blue: The Importance of Color in Marketing[J]. Journal of the Academy of Marketing Science, 2012,40(5):711-727.
doi: 10.1007/s11747-010-0245-y
[21] Gorn G J, Chattopadhyay A, Sengupta J, et al. Waiting for the Web: How Screen Color Affects Time Perception[J]. Journal of Marketing Research, 2004,41(2):215-225.
doi: 10.1509/jmkr.41.2.215.28668
[22] Summers T A, Hebert P R. Shedding Some Light on Store Atmospherics: Influence of Illumination on Consumer Behavior[J]. Journal of Business Research, 2001,54(2):145-150.
doi: 10.1016/S0148-2963(99)00082-X
[23] Jraissati Y, Slobodenyuk N, Kanso A, et al. Haptic and Tactile Adjectives are Consistently Mapped onto Color Space[J]. Multisensory Research, 2016,29(1-3):253-278.
doi: 10.1163/22134808-00002512 pmid: 27311299
[24] Li M, Wei K K, Tayi G K, et al. The Moderating Role of Information Load on Online Product Presentation[J]. Information & Management, 2016,53(4):467-480.
[25] Liang S, Schuckert M, Law R, et al. Be a “Superhost”: The Importance of Badge Systems for Peer-to-Peer Rental Accommodations[J]. Tourism Management, 2017,60:454-465.
doi: 10.1016/j.tourman.2017.01.007
[26] Ert E, Fleischer A, Magen N. Trust and Reputation in the Sharing Economy: The Role of Personal Photos in Airbnb[J]. Tourism Management, 2016,55:62-73.
doi: 10.1016/j.tourman.2016.01.013
[27] 梁晓蓓, 徐真, 李晶晶. 共享短租平台商家属性对消费者网络口碑的影响研究[J]. 数据分析与知识发现, 2018,2(11):46-53.
[27] ( Liang Xiaobei, Xu Zhen, Li Jingjing. Impacts of Landlords on Tenants of Short-Term Rentals[J]. Data Analysis and Knowledge Discovery, 2018,2(11):46-53.)
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