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Research on behavioural differences in the processing of tenant listing information: An eye-movement experiment

Publié en ligne: 25 May 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 31 Aug 2021
Accepté: 27 Nov 2021
Détails du magazine
License
Format
Magazine
eISSN
2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Abstract

This study uses eye-movement technology to validate the need for online comments to be introduced into the internet rental model and to investigate the factors influencing tenants’ behaviours in the processing of information and decision-making. It has been found that the online review system could significantly increase the rate of house-viewing decisions. Tenants’ decisions were more affected by subjective comments, which were mainly affected by regression behaviours. Gender factors have an impact on information processing, but not on decision-making. The housing type factor does not affect information processing but has a significant effect on decision-making behaviour.

Keywords

Introduction

In recent years, with the development of Internet technology and the expansion of netizens, online shopping has become a common trend. To reduce the risk of Internet transactions and increase the authenticity of product information, the online comment system has emerged and has been applied to various industries such as shopping, tourism, hotels and short-term rental reservations. Online comments have become an important source of information for consumers’ online shopping decisions, which can help consumers refine product perceptions, and reduce information search difficulty and investment cost. The influence of online comments on consumer shopping decisions has been widely confirmed [1, 2, 3]. However, in the field of Internet renting, through the survey of China’s major Internet renting platforms: Ziruo, such as Lianjia, Anjuke, 58.com, and other Internet renting platforms, we can see that the current Internet renting model has yet introduce the online comment system, and the rental webpage only has the display of the basic situation of the house and the introduction of certain properties of the house. However, Internet renting carries common risks of Internet transactions. The renting platform also is not fully equipped and is replete with several problems such as false information, low creditworthiness and slow information updates [4, 5, 6], resulting in low trust among tenants regarding housing information on Internet rental platforms, high cost, and difficulty in distinguishing its authenticity. As a result, tenants waste a lot of time exploring housing that does not match their expectations, which may reduce the their offline viewing decisions and restrict the further development of the Internet rental model.

Internet renting is different from other online commodity transactions. Compared with other products, tenants are often more cautious about the choice of house leasing, and most of them will not choose to make decisions about house leasing online. They only use the Internet rental platform to initially understand housing information, select one or several intentional houses for further information collection, and then make the final leasing decision That is, the search and recognition of housing information on the Internet rental platform is the basic work for tenants to decide whether to rent a house. Only when the housing information meets tenants’ expectations, can they enter the stage of further understanding housing information and decision-making, which is essentially similar to the online shopping decision-making process. Therefore, this research focuses on the viewing stage of the housing information and the decision-making behaviours made after browsing, and explores the necessity and rationality of introducing the online review system into the Internet rental model. This research further discusses the impact of online comments on the information browsing and processing behaviours of housing information and offline viewing decisions.

Literature review
Eye movement and cognitive processes

The eye-mind hypothesis proposed by Just et al. provided a theoretical basis for the application of eye movement technology. which believed that people’s eye movements were consistent with the brain’s information processing process, and the gaze behaviour of the eyes reflected the inner cognitive processing process [7]. Therefore, eye-tracking technology was mainly suitable for the study of microscopic subjects’ visual behaviour and cognitive processing [8]. At present, the content of online comments using eye-tracking technology is not rich. In terms of content, the research mainly focused on the impact of online comments on consumer perception through eye-tracking technology [9, 10, 11, 12, 13], and the influence of online comments on consumers’ buying intention and behaviour [14, 15, 16, 17, 18, 19] and so on. In terms of the research field, the research involved tourism [13, 20, 21], hotel reservations [22], commodity purchases [16, 17, 23, 24] and many other industries. Generally, consumers’ browsing cognitive process was represented by eye movement indicators, such as fixation duration, fixation count, pupil diameter, reaction time, etc. Among them, the fixation duration and the fixation count were mostly used to measure consumers’ interest in a certain area or processing effort [25], and, especially, the regression duration was used to indicate the consumer’s reprocessing of a certain area of interest (AOI) [26].

Research on the usefulness of online comments

The online comment system was an important way for consumers to further understand product information, which can help them learn more about the product, reduce the increased time and energy costs caused by false information, reduce the product return rate and avoid consumers’ poor consumer experience [27, 28, 29]. Consumers’ trust in online comments was much higher than their trust in corporate advertising and marketing staff [30], so they looked forward to and actively seek online comments to provide a basis for their decision-making. At present, researches on online comments and consumer purchase decisions have received widespread attention and was an important factor that can influence consumer decisions [31, 32, 33]. In this study, we believe that under the Internet rental model, the use of the online review system will also have a positive effect on tenants’ decision-making for viewing properties, which will enhance their viewing decision-making behaviour and also reduce their cognitive processing efforts on housing information.

Hypothesis 1: Online review system can improve the decision-making behaviour of potential renters.

The research on the influence of online comments on consumer decision-making was mainly explored from three levels: the source of the review, the review itself and the recipient of the review [34, 35, 36]. First, in terms of comment source, the research was mainly based on the professionalism and reliability of the sender of the comment [37]; Second, the content of the comment itself mainly involved the attributes of the comment information, such as the extremeness of the comment [38], the comment length [39], number of comments [40] and comment rating [41], the quality of the review information, such as the relevance and timeliness of the review [42], etc., and also the comment types, such as expression form [43], display form [44], and the comment genre [10], etc.; Third, in terms of characteristics of recipients of online comments, recipients’ involvement [45], professional capabilities [27], recipient gender [46] and other characteristics have been proven to affect consumer perception and behaviour. In addition, some scholars distinguished between initial comments and additional comments and paid attention to the difference in their impact of them on consumer decision-making [47, 48].

In the research on online comments, scholars have not paid much attention to the impact of review types on consumer information search. Duhan et al. [49] used to classify word-of-mouth based on whether the evaluation was based on the internal subjective criteria of consumers. They believed that word-of-mouth was mainly divided into two types: Affective Cues and Instrumental Cues, which were important distinguishing marks for the type of comment expression. However, there are still disagreements on which form of expression of online comments is more concerned by consumers and helps consumers make purchase decisions. Hao et al. [43] divided the types of comments into subjective comments and objective comments according to different forms of expression. They defined the objective comment form as using declarative sentences and the third person to objectively evaluate the characteristics and attributes of the product and defined the subjective comment form as the first-person opinion expression with strong personal subjective colour in addition to the objective expression. And in the research, it was found that the greater the subjective expression tendency of comments, the lower the usefulness of comments to consumers. Consumers would have cognitive conflicts due to their subjective evaluations of others, which would increase the cost of consumer information cognitive processing and cause consumers to spend more time on subjective comments [43]. However, some scholars believed that compared with objectively expressed comments, subjectively expressed comments were more helpful for consumers to obtain more supplementary information and eliminate sensory uncertainty, which could reduce the cost of cognitive processing and was more helpful for consumers to search for information and make purchase decisions [50]. According to the content attributes of comments, some scholars defined the types of comment expressions as attribute type and experience type. They believed that attribute comments mainly provided objective attribute information of goods, while experience comments mainly presented consumers’ subjective feelings [51]. Tang [19] divided the types of online comments into objective attribute expressions and subjective experience expressions. They believed that objective attribute expressions conveyed objective product attributes, while subjective expressions conveyed personal experience. And in the research, it was found that the review type had a significant impact on consumers’ perceived usefulness, and the objective attribute comments of search products required more cognitive processing input than subjective experience comments. Existing studies had different naming of comment expression forms, but the main basis included the essential difference of whether it is a strong expression of self-experience. Based on summarising the previous research, these researches divided the comment types into subjective comments and objective comments according to the form of expression. The subjective comment was the tenant’s personal experience description of the property, while the objective comment was the description of the property. Existing studies still had different views on the impact of subjective and objective comment expression on consumer cognition and decision-making behaviour. We agree that subjective comments require tenants to invest more cognitive processing efforts to realise the process of summarising the experience of others into their own cognitive system [43]. What’s more, about the combined use of eye movement indicators and online comments, most studies generally use Fixation duration to indicate consumer cognitive effort and lack a detailed interpretation of cognitive processing. Only a few scholars distinguish between the first browsing and re-browsing processes and use regression indicators to interpret consumer information browsing behaviour in stages [18]. In this study, fixation duration, fixation counts, first fixation duration, first fixation counts and regression duration were used to interpret the browsing and processing process of rental room source information in stages. Tenants put more cognitive processing efforts in the AOI, which is shown as more fixation duration, fixation counts and regression duration. It is also assumed that the difference in the tenant’s cognitive effort towards comments is mainly reflected in the review process, and the difference in the initial process is not significant. Based on the above review, Hypothesis 2 and Hypothesis 3 are proposed.

Hypothesis 2: The subjective and objective expression of comments will have a significant impact on tenants’ information processing behaviour, and it is mainly caused by regression behaviour.

Hypothesis 2-1: The fixation duration, the fixation counts and the regression duration of subjective comments are significantly greater than those of objective comments;

Hypothesis 2-2: There is no significant difference between the first fixation duration and the first fixation counts of subjective comments and objective comments;

Individuals have different information processing methods, and males and females have certain differences in information browsing and processing methods. Meyers-Levy and Sternthal [52] believed that females are comprehensive information processors and pay more attention to subjective information, while males are selective information processors and pay more attention to objective information. Some scholars further proposed that males tend to give priority to information about functional attributes, while females tend to give priority to value/experience information. Smith [53] applied the above research conclusions to the consumer’s advertising information processing process and found that males make decisions based on one-sided information, while females make decisions after considering the information more comprehensively. In the field of combining online comments and eye-tracking technology, Song et al. [54] found that when buying clothing products, gender factors will have a certain impact on the average regression counts in AOI, and have an impact on consumers’ information search behaviour. Diao et al. [11] found that gender had a moderating effect between product type and review forms, and reflected complex interactions. Therefore, gender factors are introduced into the research of tenant browsing and information processing in housing information search in this study. Hypothesis 3 and Hypothesis 4 are proposed.

Hypothesis 3: Gender factor has a significant impact on tenants’ cognitive processing behaviour of subjective and objective comments.

Hypothesis 3-1: Female fixation duration, fixation counts and regression duration of subjective comment interest area are significantly greater than that of males.

Hypothesis 3-2: Female fixation duration, fixation counts and regression duration of objective comment interest area are significantly greater than that of male.

Hypothesis 4: Gender factor has a significant impact on tenants’ decision-making behaviour in house viewing.

The influence of product characteristics on consumer information browsing and decision-making behaviour has also been extensively explored. Commodity brands [54, 55], product types [54, 56, 57], platform types [58], etc., will all have an impact on consumer browsing and decision-making behaviour. In this study, the Internet rental market is selected for research, and the commodity being reviewed is a house. At present, the types of houses in the market are mainly divided into two types: ordinary residences and apartments. From the current rental market transaction volume, ordinary residential leasing occupies the main market. This study also uses the type of house as an independent variable to explore whether it has a significant impact on the cognition of the source information of rental rooms and the decision-making behaviour of viewing houses, and believes that ordinary houses will receive more attention from tenants. Accordingly, Hypotheses 5 and 6 are proposed.

Hypothesis 5: The type of house has a significant impact on the cognitive processing behaviour of tenants’ subjective and objective comments.

Hypothesis 5-1: In the context of subjective comments, the fixation duration, the fixation counts and the regression duration of the tenant on an ordinary house are significantly greater than that of an apartment;

Hypothesis 5-2: In the context of objective comments, the fixation duration, the fixation counts and the regression duration of the tenant on an ordinary house are significantly greater than that of an apartment;

Hypothesis 6: The type of house has a significant impact on tenants’ decision-making behaviour.

Method
Experimental equipment and participants

The Eyelink Pro Duo eye tracking device was used in this experiment, which has the advantages of low subject interference, simple operation and high sampling accuracy [26, 59], which was suitable for consumers behavioural research, cognitive psychology research and other fields. The experimental materials were presented by HP computer (21 inches) with a resolution of 1024 × 768. The experimental data were recorded and analysed by Eyelink’s supporting data analysis software Data Viewer.

The experiment was carried out in the Eye Movement Laboratory of Beijing Forestry University, which has a good lighting environment and no noise interference, so it met the eye movement experiment standards. The experiment recruited 122 subjects, including 65 females and 56 males. The subjects were divided into apartment groups and ordinary housing groups. Each group of subjects was randomly selected, with 61 subjects in each group. After the eye movement experiment, the data from the subjects with missing values were eliminated, and 104 valid data (45 males and 59 females) were obtained, with 52 subjects in each group. The subjects were all 20–30 years old students (mainly undergraduates and masters) who were willing to rent a house through the Internet in the future. According to the ‘2020 China Youth Rental Life Blue Book’, the main body of the new generation of urban tenants is composed of groups under the age of 30, obtaining bachelor’s degrees and above. The tenant groups in the rental market are showing younger characteristics, and they mostly choose the Internet rental platform to search for housing information, so the selected student participants are representative. Participants have all the experience of browsing online listing information but have no experience with similar experiments. And they all have normal vision after correction and normal colour vision.

Experimental design and process

The experiment adopted a three-factor mixed experimental design of 2 (comment expression: subjective, objective) × 2 (gender: male, female) × 2 (house type: apartment, ordinary housing). Among them, the subjective and objective expressions of the comments were the in-group factors, and the gender of the tenant and the house type were the between-group factors. During the experiment, subjective comments and objective comments were divided into two areas of interest of the same size. The independent variables in the experiment were the subjective and objective expressions of comments, the gender of the tenant, and the type of house. The dependent variables were the eye movement indicators and house inspection decisions. The specific eye movement indicators used in the study included fixation duration, fixation counts, first fixation duration, first fixation counts and regression duration.

The materials presented in the experiment were divided into ordinary housing groups and apartment groups, and the design was all the same except for the housing type information. The design of experimental materials drew on the design of real rental platforms, and the design of experimental materials was mainly based on the ‘Anjuke’ page, which is a famous rental website in China. The page display of each group of experimental materials included housing pictures, basic housing information, housing supporting design, detailed information such as housing traffic, surrounding areas, community greening, and housing comments. Housing comments mainly drew on existing rental information comments and short-term rental housing comments for simple processing to ensure that the comments are roughly equal in size, simple and easy to understand, making subjects have no cognitive processing difficulties. Before the formal experiment, the review content was tested for differences. Thirty real estate marketing researchers were selected to conduct a review differences survey, and the results were significant (p < 0.010). During the experiment, first, a questionnaire survey was conducted to understand the basic information of the subjects, confirming that the subjects have the intention or plan to rent a house on the Internet, and investigating the subjects’ opinions on the application of the online comment system in the field of Internet renting. Second, training the participants before the formal eye movement computer experiment to ensure that the participants clarified the basic procedures, basic concepts, key selection operations of the experiment, and the experimental requirements. Third, participants were asked to sign the experimentally informed consent, if they have no questions about the experiment. During the experiment, participants browsed and processed house information according to their will, and then made independent decisions about whether to view the house. There was no external interference during the experiment. After the eye movement experiment, retrospective interviews were conducted with the participants to explore the deep-seated reasons behind their decisions to see the house or not and understand how the experiment process and materials were easy to understand.

Results
Data analysis of house viewing decision behaviour

Before the formal eye movement experiment, 122 subjects were surveyed on the decision-making behaviour of house viewings with or without comments. The results showed that 120 subjects believed that the listing information with comments was easier to decide to see the house, proving that whether there were comments had a significant impact on consumers’ house seeing behaviour (p < 0.010). Therefore, it can be considered that it is necessary to introduce the online review system into the Internet rental platform.

After the end of the eye movement experiment, the subjects’ decision-making behaviour indicators for house inspections were re-stated, and the contingency table was used for analysis in SPSS. The results showed that under the premise of comments, the number of subjects who chose to see the house was 49, but only 33 in the context of no comment, which proved that there was a significant difference in the decision-making rate of the subject in the context of whether there were comments (p < 0.010). Therefore, it is believed that the online review system can improve tenants’ decision-making for viewing a house. Hypothesis 1 is valid.

Comments browsing process analysis

In this experiment, the comment expression form is within the group factor, so the subjective comment and the objective comment AOI were subjected to a paired sample T-test. First, a single-sample K–S test needs to be performed on the two sets of data, and the results are shown in Table 1.

Single sample K–S test

Fixation duration Fixation counts First fixation duration First fixation counts Regression duration

Subjective comments 0.200 0.200 0.000 0.000 0.200
Objective comments 0.200 0.100 0.000 0.000 0.086

It can be seen from Table 1 that the p-values of the Fixation duration (p = 0.200), Fixation counts (p = 0.200), and Regression duration (p = 0.200) of the subjective comment AOI are all >0.05, which obey the normal distribution and can be matched with a sample t-test. The p values of the First fixation duration (p < 0.010) and the First fixation counts (p < 0.010) are both <0.050, so they do not conform to the normal distribution and require non-parametric testing. The p-values of the Fixation duration (p = 0.200), Fixation counts (p = 0.100), and Regression duration (p = 0.086) of the objective comment AOI are all >0.050, which obey the normal distribution and can be matched with a sample T test. The p-values of the First fixation duration (p < 0.010) and the First fixation counts (p < 0.010) are both <0.050, so they do not conform to the normal distribution and require non-parametric testing.

Paired sample t-test. Since only the three indicators of the Fixation duration, Fixation counts, and Regression duration obey the normal distribution, the paired sample t-test can only be performed on the three indexes of subjective comment and objective comment AOI. The results are shown in Table 2. The subjective and objective expressions of comments have a significant impact on the Fixation duration (p < 0.050, t = 9.239), the Fixation counts (p < 0.050, t = 10.400) and the Regression duration (p < 0.050, t = 9.576) of the AOI, which is consistent with Hypothesis 2.

Since the Fixation duration, the Fixation counts, and the Regression duration of the subjective and objective comments are significantly different, the average value of these three indicators is further analysed. From Figure 1a1c, we can see that the average Fixation duration, the average Fixation counts, and the average Regression duration of subjective comments are higher than those of objective comments, which shows that tenants will put more cognitive processing on subjective comments. Therefore, Hypothesis 2-1 holds.

Non-parametric test. The two indicators of the First fixation duration and the First fixation counts don’t conform to the normal distribution. Therefore, the Wilcoxon rank sum test and sign test of two related sample tests of the non-parametric test need to be used for different analyses. The results are shown in Table 3. It can be seen from Table 3 that the First fixation duration (p = 0.220, p = 0.281) and the First fixation counts (p = 0.276, p = 0.246) analysed by Wilcoxon rank sum test and sign test are all >0.05, which means that the two samples come from the same distribution population and have the same distribution characteristics, so there is no significant difference. The results are consistent with the content of Hypothesis 2-2. There is no significant difference in the initial processing of comments by tenants, mainly due to the regression behaviour leading to different allocation of cognitive processing efforts to subjective and objective comments.

Paired sample t-test

Fixation duration Fixation counts Regression duration
t p t p T p
Subjective and objective comments 9.239 0.000 10.400 0.000 9.576 0.000

Fig. 1

Comment form differences in eye movement indicators. (a) the mean Fixation duration, (b) the mean Fixation counts. (c) the mean Regression duration

Non-parametric test

First fixation duration First fixation counts

Wilcoxon rank sum test 0.220 0.276
Sign Test 0.281 0.246
Variance analysis of subjective comment interest area

It can be seen from Table 1 that the Fixation duration, the Fixation counts and the Regression duration of the subjective comment AOI conform to the normal distribution, so the analysis of variance (ANOVA) is performed on these three indicators. The results are shown in Table 4.

Variance analysis of subjective comment AOI

Fixation duration Fixation counts Regression duration

F p F p F p

Gender 5.925 0.017 2.764 0.099 5.622 0.020
House type 0.018 0.893 0.003 0.954 0.168 0.682
Gender × house type 3.229 0.075 2.479 0.119 2.087 0.152

AOI, area of interest

ANOVA. It can be seen from Table 4 that gender has a significant effect on the Fixation duration (p = 0.017 < 0.050) and the Regression duration (p = 0.020 < 0.050) of subjective comments. However, it has no significant effect on the Fixation counts (p = 0.099), which partially meets the Hypothesis 3-1. The p-values of house type factors on the Fixation duration (p = 0.893), the Fixation counts (p = 0.954) and the Regression duration (p = 0.682) of subjective comments are all >0.050, which has no significant effect. Hypothesis 5 is invalid. The gender factor has a significant impact on the Fixation duration and the Regression duration. Therefore, the average value of these two indicators is compared and analysed, as shown in Figure 2a, 2b. It can be found that females invest more in cognitive processing than males in subjective comment AOI, which reflects the difference in cognitive processing behaviour between males and females.

Interaction ANOVA. It can be seen from Table 4 that the p value of the interaction of gender × brand on the Fixation duration (p = 0.075), the Fixation counts (p = 0.119), and the Regression duration (p = 0.152) are all >0.050. There is no significant difference in the three indicators.

Non-parametric test. The First fixation duration and First fixation counts don’t obey the normal distribution, so the non-parametric test method of two independent samples, namely the Mann–Whitney U test method, is used to analyse the difference. The results are shown in Table 5. It can be seen from Table 5 that the p values of gender for the First fixation duration (p = 0.644) and the First fixation counts (p = 0.531) are both >0.050. Therefore, it is considered that the two groups of data classified by sex come from the same distribution, and there is no significant difference. The p-value of the First fixation duration (p = 0.413) and the First fixation counts (p = 0.555) are also >0.050. Therefore, it is also believed that the two sets of data classified by brand come from the same distribution, and there is no significant difference. Therefore, there is no significant difference between males and females in the first processing of subjective comments.

Fig. 2

Gender differences in eye movement indicators under subjective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration

Mann–Whitney U test

First fixation duration First fixation counts

Gender 0.775 0.638
House type 0.413 0.555
Variance analysis of objective comment interest area

It can be seen from Table 1 that the Fixation duration, the Fixation counts and the Regression duration of the objective comment AOI conform to the normal distribution, so the ANOVA is performed on these three indicators. The results are shown in Table 6.

Variance analysis of objective comment AOI

Fixation duration Fixation counts Regression duration

F p F p F p

Gender 4.862 0.030 3.002 0.086 3.978 0.049
House type 1.821 0.180 1.046 0.309 1.946 0.166
Gender × house type 3.388 0.069 2.076 0.153 2.809 0.097

AOI, area of interest

ANOVA. It can be seen from Table 6 that gender has a significant effect on the Fixation duration (p = 0.030 < 0.050) and the Regression duration (p = 0.049 < 0.050) of subjective comments AOI. However, it has no significant effect on the Fixation counts (p = 0.086), which partially meets the Hypothesis 3-2. The p-values of house type factors on the Fixation duration (p = 0.180), the Fixation counts (p = 0.309) and the Regression duration (p = 0.166) of objective comments AOI are all >0.050, which has no significant effect. Hypothesis 5 is invalid. The gender factor has a significant impact on the Fixation duration and the Regression duration. Therefore, the average value of these two indicators is compared and analyzed, as shown in Figure 3a, b. It can be found that female invest more in cognitive processing than male in objective comments AOI, which reflects the difference in cognitive processing behavior between male and female students.

Interaction ANOVA. It can be seen from Table 6 that the p-value of the interaction of gender × brand on the Fixation duration (p = 0.069), the Fixation counts (p = 0.153), and the Regression duration (p = 0.097) are all >0.050. There is no significant difference in the three indicators.

Non-parametric test. The First fixation duration and First fixation counts don’t obey the normal distribution, so the non-parametric test method of two independent samples, the Mann–Whitney U test method, is used to analyze the difference. It can be seen from Table 7 that the p-values of gender for the First fixation duration (p = 0.383) and the First fixation counts (p = 0.108) are both >0.050. Therefore, it is considered that the two groups of data classified by sex come from the same distribution, and there is no significant difference. The p-value of the First fixation duration (p = 0.663) and the First fixation counts (p = 0.426) are also >0.050. Therefore, it is also believed that the two sets of data classified by brand come from the same distribution, and there is no significant difference. Therefore, there is no significant difference between males and females in the first processing of objective comments.

Fig. 3

Gender differences in eye movement indicators under objective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration

Mann–Whitney U test

First fixation duration First fixation counts

Gender 0.383 0.108
Brand 0.663 0.426
Behavioural data analysis

The behavioural data is the decision made by the subjects whether to look at the house offline after browsing the house information, which is analysed by using the SPSS contingency table. The results show that, without considering the type of house, 64.4% (29 persons) of males choose to inspect the house, and 35.6% (16 persons) choose not to inspect the house, correspondingly, while 67.8% (40 persons) of female choose to inspect the house, and 32.2% (19 persons) choose not to inspect the house. Through comparison, the decision-making rate of women choosing offline viewing is slightly higher than that of men, but the difference is not very significant (p = 0.720), Hypothesis 4 is not valid. Without considering the gender of the tenant, the viewing rate for ordinary houses is 76.9% (40 persons), and the non-viewing rate is 23.1% (12 persons), while the decision-making rate for apartment inspections is 55.8% (29 persons), and the non-viewing rate is 44.2% (23 persons). The data of the two groups showed great differences, which were also statistically significant (p = 0.022 < 0.050). The two groups of data showed great differences, and statistically significant differences (p = 0.022 < 0.050), indicating that the house type factors have a significant impact on whether tenants choose to look at the house offline. So, Hypothesis 6 is valid.

Discussion

Judging from the decision-making data of house viewing behaviour, it is shown that 98% of the subjects believe that the online review system has a positive effect on information processing and decision-making behaviour through the questionnaire survey. In the housing information browsing simulation experiment, it is also found that under the background of the property information with comments, the number of people who choose to see the house was 49, but only 33 people under the background of no review. There is a significant difference in the housing decision-making rate. Therefore, it can be considered that the online comments are beneficial to tenants’ cognitive processing of housing information, and the online review system can improve tenants’ viewing decision-making behavior. Hypothesis 1 is established. It is necessary and feasible for online review systems to introduce to the Internet rental field.

From the analysis results of the paired sample t-test, the subjective and objective expressions of comments have a significant impact on the Fixation duration, Fixation counts, and regression duration of AOIs. Through the mean comparison analysis of the Fixation duration, Fixation counts, and Regression duration with significant differences, it is found that the Fixation duration, Fixation counts and Regression duration of subjective comments are much greater than those of objective comments. Hypothesis 2-1 holds. The result indicates that the tenants put more cognitive attention on subjective comments which occupy more important roles in the tenants’ decision-making on viewing behaviour. However, there is no significant difference in the First fixation duration and the First fixation counts between the subjective and objective expressions of comments, so Hypothesis 2-2 holds. Therefore, the result can show that the subjective and objective expressions of comments have a significant impact on tenants’ information browsing and processing behaviour, and the difference is mainly caused by the regression behaviour. Hypothesis 2 holds. To explore the internal reasons why tenants pay more attention to subjective comments, retrospective interviews were conducted with the participants after the eye-tracking experiment. The participants generally believe that the review area was easy to understand. Under this premise, the main reasons why they pay more attention to subjective comments are as follows: (1) The subjective form of expression gives people more real feelings and a stronger degree of trust in comments, so people are more willing to process and adopt subjective comments. (2) Housing information is more complicated than general merchandise, and some professional parameters (such as greening rate, straight-line distance, etc.) are not easily understood by ordinary tenants. Subjective comments can explain housing attributes in a more accessible way, making it easier for tenants to understand housing information and make decisions, so, it attracted more attention from the subjects and provided more help for decision-making.

From the analysis of the effect of gender factors on the tenants’ house information browsing and cognition process, gender has a significant impact on the Fixation duration and the Regression duration of subjective comments and objective comments AOIs, but it has no significant effect on the Fixation counts, the First fixation counts, and the First fixation duration. So, the conclusion is supported by eye movement indicators that there are some cognitive differences between males and females in the process of information. Through the mean comparison analysis of the Fixation duration and the Regression duration with the significant difference, it is found that the Fixation duration and the Regression duration of female tenants on AOIs are much greater than that of male tenants. Hypothesis 3 are valid partially, indicating that both male and female tenants will pay more attention to subjective comments in review browsing, but females will pay more attention to them than males. There are certain cognitive differences between males and females in the process of browsing and processing housing information, which is in line with the selection theory assumption. Females are comprehensive information processors, while males are selective information processors [52]. Therefore, females will invest more cognitive processing efforts on reviewing information to obtain more comprehensive information to make decisions. From the perspective of the gender factor’s influence on tenants’ decision-making behaviours, there is a small difference in the ratio of male and female tenants’ decision-making behaviours. Gender factor has no significant impact on tenants’ decision-making behaviours, Hypothesis 4 doesn’t hold. The reason is explored in retrospective interviews and it is found that there is a ‘pass line’ effect in the tenants’ willingness to see the house. If the basic conditions of the house meet the budget, the house has no unacceptable conditions, and the comments have no significant cognitive conflicts, then the house reaches the pass line, and the subject will choose to go to the house. Due to the unique characteristics of Internet rental housing, housing rental cannot be completed online directly and quickly like ordinary online products. Most tenants will choose to conduct an offline inspection and then make a rental decision. There is a difference between the viewing decision and the final rental decision. Only after the decision to conduct the viewing can the final rental transaction be reached. Therefore, the decision to inspect a house is only a preliminary decision to select housing, compared with the decision to see a house, which is a decision that is easier to make, if the basic conditions of the house and the supplementary information of the comment can meet the tenant’s demand for housing rental. Moreover, the main factors for tenants to make decisions about viewing are housing information and review supplements, which also leads to the fact that gender factors have no significant impact on tenants’ viewing decisions.

From the analysis of the effect of the house type factor on the tenants’ house information browsing and cognition process, the house type factor has no significant effect on the Fixation duration, the Fixation counts, the First fixation counts, the First fixation duration, and the Regression duration. The house type has no significant effect on the tenants’ information browsing and processing mode, and Hypothesis 5 is not valid. From the perspective of the influence of house type factor on tenants’ viewing decision-making behaviours, there is a significant difference in the decision-making rate of house viewing between ordinary houses and apartment groups. Through the mean comparison analysis, it is found that the viewing rate of ordinary houses is much higher than that of apartments. The type of house has a significant impact on whether the tenants decide to see the house, so Hypothesis 6 holds. Before the computer experiment, the subjects all stated that they are clear about the difference between ordinary houses and apartments. Their main differences are reflected in house water and electricity fees and property fees. Generally, apartments need to pay more. Therefore, potential tenants will consider the house type when deciding to inspect the house or not, and will choose ordinary houses with lower extra costs for leasing when the rent price is similar. In addition, the interaction between gender and house type in the form of subjective and objective comments are not significant and have no significant effect on the tenants’ house information browsing and cognition process.

However, there are limitations to the study. Although the number of participants met the basic experimental criteria, the sample size could be expanded in the future to further improve the universality and stability of the research conclusions. In addition, future research could be more systematic. The study selected three important factors influencing tenant behaviour based on previous studies, but this is not all, and future studies can consider gradually adding other influencing factors so that consumer behaviour can be more comprehensively perceived.

Fig. 1

Comment form differences in eye movement indicators. (a) the mean Fixation duration, (b) the mean Fixation counts. (c) the mean Regression duration
Comment form differences in eye movement indicators. (a) the mean Fixation duration, (b) the mean Fixation counts. (c) the mean Regression duration

Fig. 2

Gender differences in eye movement indicators under subjective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration
Gender differences in eye movement indicators under subjective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration

Fig. 3

Gender differences in eye movement indicators under objective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration
Gender differences in eye movement indicators under objective review. (a) The influence of gender on the mean Fixation duration. (b) The influence of gender on the mean Regression duration

Mann–Whitney U test

First fixation duration First fixation counts

Gender 0.383 0.108
Brand 0.663 0.426

Non-parametric test

First fixation duration First fixation counts

Wilcoxon rank sum test 0.220 0.276
Sign Test 0.281 0.246

Variance analysis of objective comment AOI

Fixation duration Fixation counts Regression duration

F p F p F p

Gender 4.862 0.030 3.002 0.086 3.978 0.049
House type 1.821 0.180 1.046 0.309 1.946 0.166
Gender × house type 3.388 0.069 2.076 0.153 2.809 0.097

Single sample K–S test

Fixation duration Fixation counts First fixation duration First fixation counts Regression duration

Subjective comments 0.200 0.200 0.000 0.000 0.200
Objective comments 0.200 0.100 0.000 0.000 0.086

Variance analysis of subjective comment AOI

Fixation duration Fixation counts Regression duration

F p F p F p

Gender 5.925 0.017 2.764 0.099 5.622 0.020
House type 0.018 0.893 0.003 0.954 0.168 0.682
Gender × house type 3.229 0.075 2.479 0.119 2.087 0.152

Paired sample t-test

Fixation duration Fixation counts Regression duration
t p t p T p
Subjective and objective comments 9.239 0.000 10.400 0.000 9.576 0.000

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