Psychology

Intimate Relationships in the Era of WeChat

 

Abstract

Given the prevalence of social media and the limitations of existing studies, the present research seeks to examine the correlation between WeChat use and intimate relationships based on the Investment Model, and the contribution of each Investment Model construct (i.e. Satisfaction, Quality of Alternatives, and Investment Size) to predict Commitment Level. In an online survey, participants were asked to estimate the amount of time spent on Instant Messaging, Moments and WeChat Pay, which are the three functions of WeChat examined in this research, and demographic information. To measure intimate relationships, the Investment Model Scale was used. Except for the time spent on Instant Messaging, which correlated moderately with Global Satisfaction, all WeChat variables correlated weakly with Investment Model constructs. As a post-hoc analysis, the components of “time spent on Instant Messaging” (i.e. time spent chatting with partners, time spent chatting with family/friends, and time spent chatting with other contacts) were correlated with the Investment Model predictors, and time spent chatting with partners correlated most significantly with Global Satisfaction. Predictive relationships between Investment Model constructs and Commitment Level were confirmed, but the post-hoc analysis suggested that the strength of prediction differed from that in the original research conducted by Rusbult, Martz and Agnew in 1998. Potential reasons for the findings and evaluation of the present study were discussed. It is hoped that future research will recruit more representative samples, phrase questionnaire items more concisely, and assess the impact of cultural differences on intimate relationships in detail.

Keywords: Investment Model, intimate relationships, WeChat

Introduction

According to Backstrom, “when considering even the most distant Facebook user in the Siberian tundra or the Peruvian rainforest, a friend of your friend probably knows a friend of their friend” (Backstrom 2011). In the era of social media, this saying is no longer an exaggeration. Among Internet users worldwide, who account for two thirds of the global population, 76% use social network sites (SNSs) (Smart phone ownership, 2011). Due to the popularity of SNSs, extensive literature has shed light on how SNS use can impact our lives, with one focus being intimate relationships. Specifically, the influence of SNS use on relationship satisfaction has been widely examined. For example, Facebook use is positively related to jealousy that people engaged in intimate relationships experience, suggesting a negative effect of SNS use on relationship satisfaction (Muise et al. 2009).In contrast, another study indicates that public displays of affection on SNSs strengthen relationship satisfaction (Papp et al. 2012).However, satisfaction on its own does not accurately predict relationship longevity. According to Rusbult’s Investment Model (Rusbult et al. 1998), Commitment Level, the extent to which the individual wants to persist in a relationship, is most strongly related to the final stay-or-leave decision, and is determined by one’s Satisfaction, Quality of Alternatives and Investment Size jointly. The sole focus on SNS use and relationship satisfaction means existing research may lack validity. Secondly, the exclusive focus on SNSs that are prevalent in Western countries (such as Facebook) means inadequate attention is given to Eastern countries and other types of SNSs, leading to ethnocentric bias. Therefore, the present study intends to examine the relationship between the use of WeChat, the top mobile app with 768,000,000 daily logged-in users in China (WeChat 2016), and relationship stability based on Rustbult’s Investment Model, which better captures the mechanism by which SNS use leads to relationship success or failure by taking into account more variables.

The Investment Model

Rusbult’s Investment Model (Rusbult 1980)is an extension of the interdependence theory devised by Thibaut and Kelley (Thibaut and Kelley 1959). Both characterise dyadic interactions as producing rewards and costs, and the difference between them is the outcome value of current relationship. Satisfaction, defined as the extent to which one has a positive evaluation of the relationship, is equal to the difference between the outcome value of the relationship and one’s expectations of relationship outcome: Comparison Level (CL). The relationships are represented mathematically as:

Rewards – Costs = Outcome of current relationship

Outcome of current relationship – Comparison Level = Satisfaction

Both theories thus emphasize the need to distinguish Satisfaction from Commitment. They also attach importance to Quality of alternatives, defined as “the attractiveness and availability of alternatives to a relationship”. This is calculated by subtracting CL from the outcome value of the Alternative, which is the difference between rewards and costs in an alternative relationship. The relationships are shown mathematically as:

Rewards of Alternatives – Costs of Alternatives = Outcome of Alternatives

Outcome of Alternatives – Comparison Level = Quality of Alternatives

The Investment Model differs from the interdependence theory on the third determinant of Commitment Level: Investment Size, defined as “the number and magnitude of resources that are tied to a relationship.” Investment is divided into two types: Extrinsic Investments refer to resources invested before the relationship but are now tied to the relationship, such as personal properties. Intrinsic Investments are those that occur during the relationship, such as self-disclosure. Mathematically, Investment Size is calculated by multiplying quantity of resources and importance of resources, represented as:

Quantity of resources * Importance of resources = Investment Size

Commitment Level is the outcome of the three aforementioned variables, such that a high level of Satisfaction, low Quality of Alternatives and great Investment Size will lead to high Commitment Level, which most accurately predicts relationship longevity.

Features of WeChat

Unlike most highly specialised SNSs, WeChat aggregates various types of services ranging from basic chatting to hotel booking. The present research is interested in three main functions of WeChat:

Instant Messaging (IM)

Features potentially related to dyadic interactions include text messaging, push-to-talk voice messaging (this involves holding a button while talking, and releasing it when the talk is finished to send short audio clips), photograph and video sharing, video calls, and voice calls.

“Moments”

“Moments” is similar to the Facebook Timeline, but with heightened privacy: only the user and friends in the user’s contact list can view each other’s posts, and Likes and comments under a friend’s post are only visible to the user if the “Likers” or commenters are the user’s friends.

WeChat Pay

An online payment system that enables transactions between WeChat friends, online and with offline participating retailers.

Literature Review

Existing research points out the potential association between SNSs that are similar to the functions of WeChat and the investment model variables. For example, IM was found to have the “strongest influence” on relationship satisfaction (Coyne et al. 2011); people engage in more self-disclosure online via IM than in face-to-face communication (Tidwell et al. 2002), suggesting higher investment associated with IM use. IM also “facilitated online flirting” (Schiano et al.), which could be associated with increased alternatives and/or a rise in the partner’s perceived cost as insecurity augments. Posting pictures of the couple and writing on the partner’s Walls on Facebook are positively related to relationship commitment (Toma et al. 2015). Given that the social media feed function of Facebook is similar to that in Moments, this research suggests that Commitment Level may be positively associated with Moments use as well. Although no study has examined the impact of an online payment system on intimate relationships, given its prevalence in daily life, it is worth exploring the underlying mechanism between the two variables.

Another research interest of this study lies in testing the predictive relationships between the three Investment Model constructs and Commitment Level. Since the original research was performed on undergraduate students at University of North Carolina at Chapel Hill who were predominantly Caucasians (Rusbult et al.), the present study is interested in examining the contribution of each of the three Investment Model variables to predicting Commitment Level on a Chinese sample with larger demographic variations.

Given the interest to retest the validity of Investment Model Scale and the fact that no existing literature has examined how WeChat use affects relationship longevity based on Rusbult’s Investment Model, the following research questions were formulated:

Research question 1: How is time spent on IM related to Satisfaction, Quality of Alternatives, and Investment Size?

Research question 2: How is time spent on Moments related to the three variables?

Research question 3: How is time spent on WeChat Pay related to the three variables?

Research question 4: To what extent do the three Investment Model variables predict Commitment Level?

Method

Participants

A convenience sample consisting of 64 heterosexual Chinese individuals were recruited. 9 participants were excluded from the data analysis because five of them were not engaged in intimate relationships and four skipped more than 20% of the items, which created difficulty for statistical analysis. Among the remaining 55 subjects, 22 were engaged in exclusive dating relationships or were engaged, and 32 were married. All participants were engaged in geographically close romantic relationships (defined as being able to see the partner in person at least once a week) for at least 6 months. They all used the three functions of WeChat examined in this research (i.e. IM, Moments and WeChat Pay) daily. Participants voluntarily responded to a post distributed on WeChat Moments that described the purpose of the research, eligibility for participation (e.g. engaged in exclusive dating relationships or marriage, heterosexual, being geographically close to the partners), and confidentiality of the data.

Measures

The questionnaire consisted of three sections. The first and third sections are shown in the Appendix. The first section assessed participants’ use of Instant Messaging, Moments and WeChat Pay.

IM

Participants were asked to indicate the average time spent chatting with their partners, with close friends or family members, and with other contacts per day over the past week, respectively.

Moments

Participants were asked to indicate: (a) the frequency of writing posts; (b) the length of time spent on reading and/or commenting other people’s posts per day.

WeChat Pay

Participants were asked to indicate their frequency of using WeChat Pay per week.

The second section of the questionnaire concerned participants’ subjective evaluation of the variables in Rusbult’s Investment Model. Measurement was adapted from Rusbult, Martz and Agnew’s research (Rusbult et al.), which is highly refined and allows participants to evaluate all variables using both global (general) items and facet (specific) items. The inclusion of facet items prevents the assessment from being too vague and thus lowers the validity of responses, as concrete aspects of each construct are listed in facet items to prepare participants to answer the following global items.

There were 5 facet items and 5 global items for all variables except Commitment Level, which was assessed solely by 7 global measures. Participants rated all global items on 9-point Likert scales (1 = “Do Not Agree At All”, 5 = “Agree Somewhat” and 9 = “Agree Completely”). In the original research, subjects responded to all specific items on 4-point Likert scales (“Don’t agree at all, Agree slightly, Agree moderately, and Agree completely”). However, since it was possible that situations described in certain items were not applicable to some dating couples (e.g. “My partner fulfils my sexual needs”), “N/A” was included as the fifth option.

Satisfaction

The global items asked about participants’ general satisfaction (e.g. “I feel satisfied with our relationship”), relationship happiness, satisfaction in comparison to other people’s intimate relationships and participants’ ideal relationships, and the extent to which the relationship gratifies their needs for intimacy, companionship, sexual activities, security and emotional involvement. The facet items were lifted from the Investment Model Scale.

Quality of Alternatives

The general measures concerned the appeal and attractiveness of potential alternatives (e.g. “the people other than my partner with whom I might become involved are very appealing”), closeness of alternatives to participants’ ideal relationships, participants’ confidence in finding alternatives, and general estimation of the fulfilment of the five needs in alternative relationships. The facet items were lifted from the Investment Model Scale.

Investment Size

Global measures included general estimation of investment, comparison with other people regarding investment (e.g. “compared to other people I know, I have invested a great deal in my relationship with my partner”), overlap between personal life and the partner, degree of emotional involvement, and the extent to which relationship dissolution would influence participants’ personal social circles. The facet items were the same as those in the Investment Model Scale.

Commitment Level

Only general measures were used to assess Commitment Level. Participants were asked about their desire for a long-lasting present relationship (e.g. “I want our relationship to last for a very long time”), commitment to the maintenance of their relationship, perceived degree of negative emotion experienced due to relationship dissolution, perceived likelihood of dating an alternative partner within the next year, attachment to the relationship, desire for an eternal present relationship, and to what extent they focused on the long-term future of their relationships.

The third section was concerned with participants’ demographic characteristics. To confirm participants’ eligibility for participation, they were asked to indicate if they were involved in romantic relationships, relationship types, relationship duration, number of times per week they see their partners in person over the past month, and age. The last three questions asked about gender, educational level and hukou status (Hukou refers to the record of household registration that identifies an individual as the resident of an area; one’s hukou status can be either “agricultural” or “non-agricultural”).

Procedure

A post was sent on WeChat Moments with a self-introduction, a description of the study and a URL to the online survey. The description covered the purposes, eligibility of participation, and confidentiality of data. Participants accessed the questionnaire on cell phones or other mobile devices, and were instructed to answer the questions on their own, though it was impossible to confirm the independence of completion. After finishing all the questions, participants were thanked and given opportunities to leave comments if they had any doubt to the study.

Results

Demographic Data

Among the 49 respondents who answered the question on gender, 26 were female and 23 were male. The age of respondents ranged from 18 to 51, with the greatest number of participants concentrated in the 25-34 and 35-44 categories. The mean age was 31.85 (SD=8.40). The average relationship duration was 75.40 months (SD=71.00), with the longest one lasting for 312 months and the shortest one lasting for 6 months. Most respondents had obtained 2-year or 4-year college degrees (n=35) and possessed non-agricultural hukou status (n=34). Demographic data is presented in detail in Table 1.

Participants’ Satisfaction, Investment Size and Quality of Alternatives

In order to compute scale scores for each variable, mean scores for facet and global measures of each variable were calculated respectively. Statistical analyses were then carried out on the mean scores to provide an overview of participants’ responses. However, it is worth noting that the results yielded from global measures were more indicative than that from facet items, as the inclusion of facet items was merely for preparing participants to answer the following global items. One case was removed from the Global Investment measure as the subject skipped all items in this section. Overall, participants reported high levels of Satisfaction in global items (M=6.30, SD=1.85) and moderate levels in facet items (M=3.18 SD=0.58). The average scores of Quality of Alternatives on global and facet measures were 4.13 (SD=1.60) and 2.33 (SD=0.69) respectively, suggesting moderate-to-low level of Alternatives. Mean scores for Investment Size also suggested moderate level of Investment: 5.50 (SD=1.80) for global measures and 3.18 (SD=0.56) for facet measures. Detailed results are summarised in Table 2.

Correlations Between Investment Model Constructs and WeChat Use

Since facet items were included solely to enhance the comprehensibility of global items (Rusbult et al.), the present study only examined correlations between scores on the global items and WeChat usage (summarised in Table 3).

To test the first three research questions, Pearson bivariate correlational analyses were performed on the four WeChat variables (i.e. the total time spent on IM per day, the amount of time spent per day viewing or commenting in Moments, the weekly frequency of posting in Moments, and the daily frequency of using WeChat Pay) and three global variables from the Investment Model (i.e. Satisfaction, Quality of Alternatives, and Investment Size). The total time spent on IM was calculated by combining durations of the 3 types of WeChat conversations (i.e. with partners, with friends and families, and with other contacts). This was based on the assumption that these three types of contacts were both exclusive and exhaustive. Results revealed that Global Satisfaction correlated positively with total time on IM (r = .25, p = .06). All other correlations were much smaller (in the .03 to .10 range, and all p values were greater than .20).

As an exploratory analysis, the three IM variables (i.e. chat with partners, with friends and families, and with other contacts) were correlated separately with the Investment Model predictors. Global Satisfaction correlated positively with the amount of time spent chatting with partners (r = .28, p = .04), and the relationship was more significant than that between total time spent on IM and Global Satisfaction. All other correlations were much more insignificant (in the .006 to .14 range, and all p values were greater than .33).

Associations Between Investment Model Constructs and Commitment Level

Although clear predictive relationships between each of the Investment Model variables and Commitment Level were demonstrated in the original research (Rusbult et al.), the present study intended to replicate the Investment Model Scale on a different sample to test its validity. Pearson bivariate correlations were firstly conducted on each of the Investment Model predictors and Global Commitment Level (results summarised in Table 4). Commitment Level correlated .48 (p < .001) with satisfaction, -.22 (p = .10) with quality of alternatives, and .53 (p < .001) with investment. Three-factor simultaneous regression analyses were then conducted, and results indicated that the three Investment Model predictors jointly predicted Commitment Level (R2 = .42; p < .001). Satisfaction positively predicted Commitment Level (beta = .29); Quality of Alternatives was negatively predictive of Commitment Level (beta = -.19); and Investment Size was positively predictive of Commitment Level (beta = .44). Detailed data was presented in Table 5. Interestingly, the post-hoc analysis that compared the previous study on a predominantly Caucasian undergraduate students sample (Rusbult et al.) and the present study revealed that Investment Size displayed stronger contribution in predicting Commitment Level in this study (original study: betas ranged from .19 to .27), whereas Satisfaction and Alternatives were less predictive of Commitment Level (original study: betas ranged from .47 to .69 for Satisfaction, from -.29 to -.32 for Alternatives).

Discussion

The present study sought to examine the correlations between each of the WeChat variables and the three Investment Model constructs, and the predictive relationships between each Investment Model predictor and Commitment Level. Overall, WeChat variables displayed relatively weak associations with the Investment Model variables, except for the total time spent on IM that showed a more significant relationship with Global Satisfaction. Specifically, the amount of time spent chatting with partners correlated with Global Satisfaction more significantly than not only other WeChat variables but also the overall IM use. This finding aligns with research from Coyne, Stockdale, Busby, Iverson and Grant (Coyne et al. 2011) that also points out the significant correlation between text messaging and relationship satisfaction. Their research proposes positive communication as the intermediate factor that explains this positive correlation: texting messaging is strongly associated with positive communication, which in turn predicts high relationship satisfaction. However, since WeChat IM includes more functions than traditional text messaging, such as push-to-talk voice messaging and video calls, it may better address the asynchronicity that commonly presents in computer-mediated communication, which might explain its positive impact on Satisfaction.

The relationships between the three Investment Model predictors and Commitment Level were confirmed in the present study by testing it on a Chinese sample with greater demographic variations, suggesting the validity of the Investment Model. However, this research differs from the original study on the strength of prediction: Investment Size seemed to be more predictive of Commitment Level, while Satisfaction and Quality of Alternatives contributed less to the prediction than that in previous research. The discrepancy may hint at cultural differences between the Western and Eastern countries: in the East where collective welfare is emphasised, couples might be more willing to sacrifice individual satisfaction or the possibility of finding desirable alternatives in exchange for relationship stability than those in the West. Future research may explore this area more thoroughly. Another explanation could be the difference in relationship duration: the average relationship duration ranged from 15.96 to 19.69 months in the original study, whereas it was 75.40 months in this research. It is reasonable to speculate that Investment plays an increasingly big role as relationships progress.

Conclusion

The present study yielded several conclusions. First, total time spent on IM is positively related to Satisfaction. Second, total time spent chatting with partners is most significantly related to Satisfaction than other components of total time spent on IM. Third, other WeChat variables have no significant relationships with the three Investment Model constructs. Fourth, the Investment Model is valid when tested on a Chinese sample: Satisfaction is positively predictive of Commitment Level; Quality of Alternatives is negative predictive of Commitment Level; and Investment Size is positively predictive of Commitment Level. Finally, the predictive relationship between Investment Size and Commitment Level is more salient than that in Rusbult’s research (Rusbult et al.).

The present research could be improved by recruiting a larger sample. Since most of the participants held non-agricultural hukou status and were relatively well-educated, and given that the majority of Chinese population still possesses agricultural hukou status, the present study should be generalised only cautiously. Also, most of the data was correlational, meaning that causal relationships cannot be established. Finally, many of the participants were found to have answered anomalous values. This could be due to the failure to translate the original English questionnaire concisely, which causes confusion. On the other hand, it may be explained by participants’ unwillingness to reveal information on such a private topic as romantic relationships. This intolerance might be especially prevalent in Eastern countries like China, where the culture encourages reserved attitude. Thus, future research that examines the same area needs to pay special attention to recruiting representative samples and phrasing questionnaire items.

Despite methodological weaknesses, the present study has three major strengths. Firstly, it is the first study that examines WeChat use and relationship longevity. Given the uniqueness of WeChat in comparison to other SNSs and its widespread use, this research presents a novel and meaningful approach to intimate relationships, which can point out directions for future research. In addition, it is the first study that replicates the Investment Model on a Chinese sample, a population not frequently examined in psychology. Therefore, the present research not only provides a preliminary insight into the dynamics of Chinese people’s romantic relationships in response to the introduction of WeChat, but also leads to another potential research interest: cultural differences and intimate relationships. Furthermore, the model was tested on not only dating couples but also married couples, and the sample was much less demographically homogenous than that in the original research. Thus, this study provides strong support for the validity of Investment Model.

Bibliography

Backstrom, Lars. 2011. ”Anatomy of Facebook.” https://www.facebook.com/notes/facebook-data-science/anatomy-of-facebook/10150388519243859/.

Coyne, Sarah M., Laura Stockdale, Dean Busby, Bethany Iverson, and David M. Grant. 2011. ”’I luv u :)!’: A descriptive study of the media use of individuals in romantic relationships.” Family Relations: An Interdisciplinary Journal Of Applied Family Studies 60, no. 2: 150-162. http://doi.org/10.1111/j.1741-3729.2010.00639.x.

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Muise, Amy, Emily Christofides, and Serge Desmarais. 2009. ”More information than you ever wanted: Does Facebook bring out the green-eyed monster of jealousy?.” Cyberpsychology & Behavior 12, no. 4: 441-444. http://doi.org/10.1089/cpb.2008.0263.

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Papp, Lauren M., Danielewicz, Jennifer, and Cayemberg, Crystal. 2012. “‘Are we Facebook official?’ Implications of dating partners’ Facebook use and profiles for intimate relationship satisfaction.” Cyberpsychology, Behavior, And Social Networking 15, no. 2: 85-90. http://doi.org/10.1089/cyber.2011.0291.

Pew Research Center. 2016. “Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies: But Advanced Economies Still Have Higher Rates of Technology Use.” http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/.

Rusbult, Caryl E. 1980. ”Commitment and satisfaction in romantic associations: A test of the investment model.” Journal Of Experimental Social Psychology 16, no. 2: 172-186. http://doi.org/10.1016/0022-1031(80)90007-4.

Rusbult, Caryl E., John M. Martz, and Christopher R. Agnew. 1998. “The Investment Model Scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size.” Personal Relationships 5, no. 4: 357-391. http://doi.org/10.1111/j.1475-6811.1998.tb00177.x.

Schiano, Diane J., Coreena P. Chen, Ellen Isaacs, Jeremy Ginsberg, Unnur Gretarsdottir, and Megan Huddleston. “Teen use of messaging media.” In CHI’02 extended abstracts on Human factors in computing systems, 594-595. ACM, 2002. https://doi.org/110.1145/506443.506500.

Thibaut, John W. and Kelley, Harold H. 1959. The social psychology of groups. New York: Wiley. https://archive.org/details/socialpsychology00thib.

Tidwell, Lisa Collins, and Joseph B. Walther. 2002. “Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time.” Human Communication Research 28, no. 3: 317-348. https://doi.org/10.1111/j.1468- 2958.2002.tb00811.x.

Toma, Catalina L., and Mina Choi. 2015. “The couple who Facebooks together, stays together: Facebook self-presentation and relationship longevity among college-aged dating couples.” Cyberpsychology, Behavior, And Social Networking 18, no. 7: 367-372. https://doi.org/10.1089/cyber.2015.0060.

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Table 1

Number Percentage
Gender
Female 26 47.27%
Male 23 41.82%
Unanswered 6 10.91%
Age (M=31.85, SD=8.40)
18-24 10 17.86%
25-34 20 35.71%
35-44 20 35.71%
45-54 3 5.36%
Unanswered 3 5.36%
Relationship duration / month (M=75.40, SD=71.00)
0-24 17 30.91%
25-48 6 10.91%
49-72 10 18.18%
73-96 3 5.45%
96-120 8 14.5%
Beyond 120 8 14.5%
Unanswered 3 5.45%
Education level
Elementary school 0 0%
Middle school 3 5.45%
High school 9 16.36%
Two-year and Four-year college 35 63.64%
Graduate degree ( e.g., Masters, PhD, JD, MD) 6 10.91%
Unanswered 2 3.64%
Origin (hukou status)
Agricultural 19 34.55%
Non-agricultural 34 61.82%

Table 2

Mean Scores, Maximum and Minimum Scores, and Standard Deviation of Global and Facet Measures of the Investment Model Constructs

N Minimum Maximum Mean Std. Deviation
Facet Satisfaction 55 1.8 4.0 3.171 .5817
Global Satisfaction 55 2.4 9.0 6.291 1.8470
Facet Alternative 55 1.0 3.8 2.331 .6931
Global Alternative 55 1.0 8.0 4.135 1.6033
Facet Investment 55 1.0 4.0 3.167 .5601
Global Investment 54 1.0 9.0 5.504 1.8022
Valid N (listwise) 54

Table 3

Correlations Between WeChat Variables and Investment Model Constructs

Global satisfaction Global alternative Global investment
Chat with partner Pearson Correlation .281* -.067 .128
Sig. (2-tailed) .038 .625 .350
N 55 55 55
Chat with friends & family Pearson Correlation .135 -.028 .051
Sig. (2-tailed) .326 .838 .712
N 55 55 55
Chat with others Pearson Correlation .104 .133 -.006
Sig. (2-tailed) .458 .343 .969
N 53 53 53
Total time on IM Pearson Correlation .254 .029 .082
Sig. (2-tailed) .061 .834 .551
N 55 55 55
View / comment in Moments Pearson Correlation -.066 .163 .032
Sig. (2-tailed) .632 .233 .819
N 55 55 55
Send posts in Moments Pearson Correlation -.058 -.082 .036
Sig. (2-tailed) .672 .552 .796
N 55 55 55
Use WeChat Pay Pearson Correlation -.038 .077 -.064
Sig. (2-tailed) .789 .583 .648
N 53 53 53
*. Correlation is significant at the 0.05 level (2-tailed).

Table 4

Correlations Between the Investment Model Variables and Commitment Level

Global commitment
Global satisfaction Pearson Correlation .483**
Sig. (2-tailed) .000
N 55
Global alternative Pearson Correlation -.222
Sig. (2-tailed) .104
N 55
Global investment Pearson Correlation .522**
Sig. (2-tailed) .000
N 54
**. Correlation is significant at the 0.01 level (2-tailed).

Table 5

Regression Analyses on the Investment Model Variables and Commitment Level

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .644a .415 .381 1.266133328955001
  1. Predictors: (Constant), Global investment, Global alternative, Global satisfaction
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 57.985 3 19.328 12.057 .000b
Residual 81.758 51 1.603
Total 139.742 54
a. Dependent Variable: Global commitment
b. Predictors: (Constant), Global investment, Global alternative, Global satisfaction
Coefficientsa
Model t Sig.
Unstandardized Coefficients

B

Std. Error Standardized Coefficients Beta
1 (Constant) 4.298 .872 4.928 .000
Global satisfaction .250 .103 .287 2.423 .019
Global alternative -.187 .111 -.187 -1.682 .099
Global investment .368 .096 .442 3.821 .000
a. Dependent Variable: Global commitment

Appendix

Part 1. WeChat Use

Instructions: The following questions pertain to your WeChat usage. Please answer the questions based on your estimation of your WeChat use.

1. On average, how many minutes per day did you spend chatting with your intimate partner on WeChat over the past week?

___________________________________________ minutes per day.

2. On average, how many minutes per day did you spend chatting with your family members (excluding husband/wife) and/or close friends on WeChat over the past week?

___________________________________________ minutes per day.

3. On average, how many minutes per day did you spend chatting with non-close contacts (not mentioned above) on WeChat over the past week?

___________________________________________ minutes per day.

4. On average, how many times per week do you write posts in WeChat Moments over the past month?

___________________________________________ times.

5. On average, how many minutes per day did you spend viewing and/or commenting on other people’s posts in WeChat Moments over the past week?

___________________________________________ minutes per day.

6. On average, how many times per day did you use WeChat Pay over the past week?

The use of WeChat Pay includes activities such as making transactions with other WeChat users on WeChat, making transactions on third-party APPs/websites, such as Taobao and Didi, and making transactions with offline retailers, such as Starbucks.

___________________________________________ times per day.

Part 3.  Background Demographics

Instructions: The following questions ask about your background information. Please enter / choose the answers that best describe your situations. As promised, all data will be completely anonymous.

  1. Are you currently involved in an intimate relationship?

__Yes

__No

  1. What type of intimate relationship are you currently engaged in?

__Non-exclusive dating relationship (dating multiple people concurrently)

__ Exclusive dating relationship

__Engagement

__ Marriage

__Other: _________

  1. How long have you been engaged in the current intimate relationship? _________ months
  2. On average, how many times per week have you seen your partner in-person over the past month?   __________ times per week
  3. How old are you? ________years
  4. Gender: __________
  5. What is the highest level of education you have completed?

__ Elementary school

__ Middle school

__ High school

__ 2-year college

__ 4-year college

__ Graduate degree (e.g., Masters, PhD, JD, MD)

  1. What is your hukou status?

__Agricultural

__Non-agricultural

 

Jiani Li

Dr. Debra Mashek

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