Bike sharing and users’ subjective well-being: An empirical study in China

Transportation Research Part A 118 (2018) 14–24

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Bike sharing and users’ subjective well-being: An empirical study in China

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Liang Ma , Xin Zhang, Xiaoyan Ding, Gaoshan Wang School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China

A R T IC LE I N F O

ABS TRA CT

Keywords: Perceived value Social influence Trust attitude Personal accomplishment Subjective well-being

The rise of bike sharing has been phenomenal in China. However, few studies have focused on it relation to subjective well-being. Here we develop an integrated model to investigate factors that affect the subjective well-being of shared bike users in China. An online survey of 908 users was conducted. The highlights are: (1) perceived value has a positive effect on users’ subjective wellbeing through users’ trust attitude. Hedonic value has the greatest impact on users’ subjective well-being, followed by social value and utilitarian value; (2) social influence has a positive effect on users’ trust attitude and hence to subjective well-being; (3) perceived ease of use and perceived usefulness of the system have positive effects on users’ trust attitude; (4) personal accomplishment and users’ trust attitude have a positive effect on users’ subjective well-being. Theoretical and practical implications are also discussed.

1. Introduction The use of bike sharing has suddenly taken off in China since 2016 (Ma et al., 2017). Bike sharing services are conveniently provided at university campuses, subway and bus stations, residential, commercial and public service areas. According to the Chinese bicycle market share report (Big Data Research, 2017), as of the end of 2016 the total number of shared bikes users had reached 18,860,000, with the bike share market expected to reach 50,000,000 users by the end of 2017. Although the advent of bike sharing has facilitated people’s lives, it is not clear whether it enhances their subjective well-being. Prior research has mainly focused on psychological, sociological, and social media aspects of the problem. Lamu and Olsen (2016) investigated the relative importance of health, income, and social relations to subjective well-being. Gerson et al. (2016) found that individual differences in personality mediate the relationship between Facebook use and subjective well-being. However, the literature on the subjective well-being of shared bicycle users is limited. Bike sharing involves unique aspects such as the need to download an app, pay a deposit, and disclose individual location, and is affected by factors beyond user’s control such as the weather (Campbell et al., 2016). In contrast, bike sharing also presents unique benefits, such as positive effects on health (communicated to users in the form of information on travelled distance and burnt calories) and carbon savings. Those factors may have an impact on subjective well-being. With bike sharing becoming increasingly popular in China, the balance of beneficial and detrimental outcomes described above may potentially affect life styles and attitudes of users. This context is also relevant to marketing strategies of bike sharing companies. Here we investigate for the first time how bike sharing is affecting the subjective well-being of Chinese users. Based on the characteristics of bike sharing, we propose an integrated model to investigate factors underlying subjective well-being. We verified

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Corresponding author. E-mail address: [email protected] (L. Ma).

https://doi.org/10.1016/j.tra.2018.08.040 Received 17 July 2017; Received in revised form 10 July 2018; Accepted 31 August 2018 0965-8564/ © 2018 Elsevier Ltd. All rights reserved.

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results through a questionnaire survey. We interpret the results by applying the perceived value theory and the social influence theory to the Chinese bike share market, and discuss potential practical consequences to bike sharing operators. This paper is organized as follows. Section 2 provides the literature review and hypothesis development. Section 3 describes the research methods, followed by data analysis in Section 4. Section 5 discusses the research findings and implications as well as limitations. 2. Literature review and hypothesis development 2.1. Perceived value and trust attitude Customer perceived value is defined as the overall evaluation of the benefits of the product or service that the customer perceives, and the trade-off between gain and cost of the product or service (Zeithaml, 1988). Research on customer perceived value theory is mainly divided into two camps. The first focuses on customer perception, and the benefits derived by customers from a product or service, based on the concepts of customer value and experience value. The second is based on the enterprise, and refers to the value of the customer to businesses as measured by the concepts of customer lifetime value and customer engagement value. Customer perceived value is generally divided into three dimensions: utilitarian value, hedonic value and social value (Kim et al., 2013a; Yu et al., 2013). Initially, customer perceived value theory mainly applied to the study of enterprise practices (Chen and Quester, 2006) but in recent years it has been increasingly applied to the study of users’ behavior (Kim, 2015; Tasci, 2016). In this study, we draw upon customer perceived value theory to investigate how value perception affects trust attitude and subjective well-being of Chinese bike sharing users. We rely on a definition of customer value as the benefit of the service to users. We analyzed three dimensions of customer perceived value: utilitarian value, hedonic value, and social value. Specifically, the perception of utilitarian value refers to the usefulness of bike share, in terms of time savings and physical exercise; hedonic value refers to the happiness derived from riding the shared bicycle; and social value refers to the contribution to society in the form of green travel. Customers perceive value engenders a series of positive behaviors, such as new attitudes and trust among others (Kuo et al., 2009; Overby and Lee, 2006; Ryu et al., 2008). The theory of economy-based trust argues that trust emerges when one individual believes that others perform actions whenever they are beneficial. A positive relationship between perceived value and trust attitude was also identified by Kim et al. (2013b) and Wang (2014). Based on this evidence, we propose the following hypotheses regarding users of bicycle sharing services: H1. Utilitarian value has a positive effect on trust attitude. H2. Hedonic value has a positive effect on trust attitude. H3. Social value has a positive effect on trust attitude. 2.2. Social influence and trust attitude Social influence describes the phenomenon whereby emotions, opinions or behaviors of individuals are affected by others (Kelman, 1958). In information systems studies, it can be defined as the importance individuals ascribe to the opinions of others when choosing whether or not to adopt a new system (Venkatesh et al., 2003). In 1958, Kelman (1958) identified three broad types of social influence: compliance, identification, and internalization. Compliance refers to situations where people appear to agree with others, but privately keep their dissenting opinions; identification describes the effect of influential individuals such as public figures and celebrities; while internalization occurs when people accept a belief both publicly and privately. The theory can be seen in socialization, persuasion, sales, and marketing, and it has also been highlighted in previous information system studies (Venkatesh and Brown, 2001; Wang et al., 2013). Social influence is particularly relevant when people are uncertain, either because stimuli are intrinsically ambiguous or because there is social disagreement. Social influence was shown to mitigate consumers’ uncertainty. Because consumers are generally uncomfortable with uncertainty, they tend to interact with friends or relatives, which increase their trust in a product or service. Social influence also increases trust towards a new information technology or service (Montazemi and Qahri-Saremi, 2015). Therefore we hypothesize that: H4. Social influence has a positive effect on trust attitude. 2.3. Perceived ease of use, perceived usefulness and trust attitude Shared bike service is a new shared-economy bike service in China (Ma et al., 2017). Because users lack comprehensive information about shared bike services and operators, it is their interaction with the shared bike system and apps that should determine the perception of usefulness and ease of use of the service (Benamati et al., 2010). Such positive feelings would allow users to overcome risk perceptions and ultimately establish trust in services such as shared bicycles. Previous studies support the view that technology can affect trusting attitudes (Lankton et al., 2016; Mcknight et al., 2002) and that perceived ease of use and usefulness are important predictors of trust (Arpaci, 2017; Gefen et al., 2003; Gidhagen and Persson, 2011; Hajli et al., 2017). Thus, we argue that perceived usefulness and ease of use of the shared bicycle system and app will positively affect trust attitude of users and hypothesize that: 15

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H5. Perceived ease of use has a positive effect on trust attitude. H6. Perceived usefulness has a positive effect on trust attitude.

2.4. Users’ trust attitude and subjective well-being Subjective well-being is defined as ‘a broad category of phenomena that includes people’s emotional responses, domain satisfactions, and global judgments of life satisfaction’ (Diener et al., 1999). Life satisfaction is frequently a measure of well-being in studies of social network use (Gerson et al., 2016; Grieve et al., 2013; Ishii, 2017), and also a subjective evaluation of the quality of life. The empirical study of life satisfaction began in the 1960s have focused on the effects of marital status and environment (Bailey, 2007) mostly in adults and old-age samples (Ghubach et al., 2010). With the development of information technology, new investigations on its relationship with subjective well-being have emerged (Gerson et al., 2016). Some scholars hold that information technology promotes subjective well-being (Grieve et al., 2013; Ishii, 2017; Oh et al., 2014). In addition, trust is crucial in buyerseller relationships containing an element of risk (Schefter and Reichheld, 2000). When users perceive lower risks, their trust attitude rises and subjective well-being increases (Helliwell et al., 2014; Jovanović, 2016). Few studies have focused on subjective well-being and trust in the context of shared bicycle use, and for this reason here we investigate the hypothesis that: H7. Trust attitude has a positive effect on subjective well-being of shared bicycle users.

2.5. Personal accomplishment and subjective well-being Personal accomplishment refers to feelings of competence and achievement at work (Mackenzie and Peragine, 2003). This article defines personal accomplishment as feelings of competence and achievement when users complete a bike ride. Previous studies of personal accomplishment have mainly focused on psychology, education, and enterprise organizations (Kadi et al., 2015; Shih et al., 2013; Sommerfeld, 2016), with research in the field of information systems remaining relatively rare. Overall, studies suggest that personal accomplishment positively influences subjective well-being (Bao et al., 2003) through mediators such as a pleasure derived from compliments and recognition from others. Here we argue personal accomplishment is expected to result from bicycle use as a result of the reward to burnt calories and saved carbon, and propose the hypotheses: H8. Personal accomplishment has a positive effect on the subjective well-being of bicycle users. Fig. 1 summarizes the model and hypotheses proposed above. 3. Research methodology 3.1. Measures We designed a questionnaire survey based on variables adapted from prior literature (Appendix A). Three measures of utilitarian value and three measures of hedonic value were adapted from Vries and Carlson (2014). We also applied adapted scales of social Perceived Value Utilitarian Value H1

Hedonic Value Social Value

Personal Accomplishment

H2 H8

H3

Control Variable Personal Income

Social Influence Characteristics of the Bike System Perceived Ease of Use

H4

Trust Attitude

H7

Subjective WellBeing

H5 H6

Perceived Usefulness Fig. 1. Research model. 16

Frequency of Shared Bike Use

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Table 1 Descriptive statistics of respondent characteristics and usage of shared bicycle. Demographic variable

Sample size

%

Gender

Male Female

408 500

44.93 55.07

Age

≤30 years old 31–40 years old > 40 years old

397 397 114

43.72 43.72 12.56

Education

Junior college or below Bachelor’s degree Master’s degree or above

171 646 91

18.83 71.15 10.02

Monthly personal income (RMB)

≤3000 3001–5000 5001–8000 8001–12,000 > 12,001

96 219 327 190 76

10.57 24.12 36.01 20.93 8.37

value (Shen et al., 2013), social influence (Borrero et al., 2014), perceived ease of use (Chen and Chao, 2011), perceived usefulness (Venkatesh, 2000), personal accomplishment (Shih et al., 2013), trust attitude (Gefen et al., 2003) and subjective well-being (Iii et al., 2015). All items were measured on a 7-point Likert scale ranging from 1 (strongly disagree/unlikely) to 7 (strongly agree/likely). 3.2. Survey design and data collection The surveys used in this study were distributed by a Chinese website providing online survey services, and this platform has been used in numerous previous studies (Chen et al., 2013; Jia et al., 2018; Liu et al., 2017) for distributing questionnaires. To ensure data quality, this study used the platform's paid sample service, which utilizes more than 2.6 million sample resources from different cities in China with diverse demographic backgrounds. The platform sends email invitations to its registered members inviting them to complete a questionnaire. If members respond to the invitation and complete the surveys, the platform charges customers 2–96 Chinese yuans (One USA dollar ≈ 6.3 Chinese yuans) per response, depending on the complexity of the surveys (Chen et al., 2013). We employed the platform to randomly select 1013 members from their pool of registered members and then to send email invitations to them to complete our questionnaire. A total of 1013 respondents participated in the survey. We eliminated subjects providing the same answer to all questions, those with no experience with shared bicycle usage, and those who finished the survey in less than five minutes. Among the questionnaires returned, 908 were valid, for an effective response rate of 89.63%. The final questionnaire requested demographic information (Table 1) and usage of shared bicycles (Table 2). 4. Data analysis Smart PLS2.0 was used in all analyses. As a second-generation multivariate technique, PLS2.0 can simultaneously assess the measurement model and the structural model. Compared to covariance-based structural equation modeling (SEM), PLS requires a relatively small sample size, does not require variable to exhibit a normal distribution (Goodhue et al., 2012), and is more appropriate for exploratory analysis and handling formative constructs. Following a two-step analytical procedure (Hair et al., 2009), we examined the measurement model before the structural model. 4.1. Measurement model The measurement model can be assessed by examining its reliability and validity. Reliability is estimated by Cronbach’s alpha, composite reliability (CR) and average variance extracted (AVE). Cronbach’s alpha is a measure of internal consistency, or how closely related a set of items is as a group; composite reliability describes to what extent a series of items can represent the latent construct; and AVE measures the amount of variance captured by the constructs compared to variance due to the measurement error (Wang et al., 2017). The measurement model is accepted when Cronbach’s alpha exceeds 0.7, CR exceeds 0.7, and AVE exceeds 0.5 (Hair et al., 2009). Our estimated Cronbach’s alpha ranges from 0.717 to 0.884, CR ranges from 0.807 to 0.928, and AVE ranges from 0.582 to 0.811 (Table 3). Thus, the measurement constructs have high reliability. Validity of the measurement model was assessed through convergent validity and discriminate validity. Convergent validity is achieved when item loadings on the expected constructs are high enough. Discriminant validity is achieved when model crossloadings are smaller than the item loadings on their respective constructs (Sun et al., 2015). All estimated factor loadings were higher than 0.7 at the significance level of P < 0.01, suggesting good convergent validity (Appendix B). Besides, the cross-loadings are smaller than the item loadings on their respective constructs, suggesting satisfactory discriminate validity. The square root of each factor’s AVE is larger than its corresponding correlation coefficients with other factors (Table 3), also indicating adequate discriminate validity. To make sure that the dataset was free from common method biases, an inspection of Harman’s single-factor with nine constructs 17

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Table 2 Usage of shared bicycles. Items

Classifications

Size

%

Frequency of use

Occasionally used (multiple times a month) Often used (multiple times a week) Very frequently used (multiple times a day)

263 574 71

28.96 63.22 7.82

Travel distance

1 km and under 1–2 km 2–3 km 3–4 km 5 km and above

68 325 340 134 41

7.49 35.79 37.44 14.76 4.52

Time of use

3 months and below 3–6 months 6–12 months 1 years and above

38 258 332 280

4.19 28.41 36.56 30.84

Time spent per trip

Under 10 min 11–30 min 31–60 min Over 1 h

68 621 196 23

7.49 68.39 21.59 2.53

Purpose of use

Physical exercise Transfer to a bus or a subway Alternative to walking, convenience Recreation & Entertainment Go to work Others

466 509 762 323 235 9

51.32 56.06 83.92 35.57 25.88 0.99

Other vehicles at home

Car Bicycle A storage battery car Motorcycle Others

636 370 409 137 43

70.04 40.75 45.04 15.09 4.74

Table 3 Descriptive statistics and inter-construct correlations. Items

Alpha

CR

AVE

UV

HV

SV

SI

PE

PU

ST

PA

SWB

UV HV SV SI PE PU ST PA SWB

0.781 0.774 0.884 0.780 0.780 0.739 0.717 0.730 0.804

0.872 0.869 0.928 0.872 0.872 0.807 0.841 0.881 0.884

0.695 0.688 0.811 0.694 0.694 0.582 0.638 0.787 0.719

0.834 0.420 0.180 0.496 0.538 0.545 0.494 0.379 0.365

0.830 0.566 0.557 0.348 0.446 0.581 0.535 0.660

0.900 0.552 0.141 0.272 0.414 0.460 0.621

0.833 0.411 0.508 0.543 0.522 0.539

0.833 0.537 0.508 0.376 0.351

0.763 0.589 0.450 0.460

0.799 0.522 0.584

0.887 0.613

0.848

Notes: Utilitarian Value (UV); Hedonic Value (HV); Social Value (SV); Social Influence (SI); Perceived Ease of Use (PE); Perceived Usefulness (PU); Trust attitude (ST); Personal Accomplishment (PA); Subjective Well-Being (SWB). The diagonal (bold) elements are the square roots of AVEs, and off-diagonal elements are correlations between constructs.

(UV, HV, SV, SI, PE, PU, ST, PA, SWB) and 26 scale items was conducted. The statistical results indicated that no single factor emerged, as the first factor accounted for 37.246% of variance (less than the suggested cut-off value of 50%). Thus, we have no concerns regarding common method bias. 4.2. Structural model The structural model results are shown in Fig. 2. Utilitarian value, hedonic value, and social value have a positive effect on users’ trust attitude. Thus, hypotheses H1, H2 and H3 are supported. Furthermore, among the three components of perceived value, hedonic value has the greatest impact on users’ trust attitude, followed by social value and utilitarian value. Wang (2014) found that perceived value had a significant impact on users’ trust towards mobile government continuance use, and our study shows that perceived value has a positive effect on users’ trust attitude specifically in the context of bicycle sharing. However, contrary to the conclusion that utilitarian value exerts the greatest positive effect on users’ trust in social network websites (Kim et al. 2013b), we found that hedonic value has the greatest impact on users’ trust attitude and subjective well-being of shared bicycle users. A possible explanation is that when users perceive hedonic value, they experience a positive emotion such as pleasure, which may promote a positive attitude as well as trust attitude toward the service provider (Dewitt et al., 2008). 18

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Fig. 2. Results of PLS analysis.

Meanwhile, social influence has a positive effect on users’ trust attitude, supporting hypothesis H4. Prior studies have shown that social influence is an important factor affecting users’ adoption behavior (Chou et al., 2015), intention to use mobile social networking sites (Lin and Lu, 2015), and self-disclosure in social networking sites (Cheung, 2015). Moreover, Shih et al. (2013) proved that social influence is the strongest predictor of intent to use new technology. We confirmed this effect of social influence in a shared bicycle context, possibly because when users perceive risks and uncertainty at the early stage of shared bicycle usage, they tend to interact with their friends or relatives to mitigate uncertainty. Furthermore, perceived ease of use and perceived usefulness of the system have positive effect on users’ trust attitude, thus supporting hypotheses H5 and H6. Benamati et al. (2010) found that perceived ease of use and perceived usefulness of the online website were important factors determining trust towards an online website. We confirmed this effect in the context of bicycle sharing. When users find that the shared bicycle system and app are logical and functional in their layout, positive feelings may allow users to overcome risk perceptions and encourage feelings of trust toward the system. Finally, both users’ trust attitude and personal accomplishment have a positive effect on subjective well-being, supporting hypotheses H7 and H8. According to Diener et al. (1999), subjective well-being can be assessed by people’s emotional responses, domain satisfactions, and global judgments on satisfaction with life. Personal accomplishment and trust attitude engender pleasure and meaningfulness, and thus contributing to positive emotional responses and the perception of subjective well-being.

4.3. Mediating effect Many tests of mediation effects have been proposed. We selected the bootstrap method implemented with the PROCESS SPSS macro (Hayes, 2013). Prebensen and Xie (2017) suggested that when the confidence interval of the statistic (indirect effect) as measured by the bias-corrected method or the percentile method excludes zero, then the statistic has an intermediary effect. Such partial mediating effect occurs when the confidence interval of both the indirect and direct effects exclude zero. A fully mediated effect is present when the confidence interval of the indirect effect excludes zero but the direct effect includes zero. Results show that trust attitude partially mediates the positive effect of perceived value (which includes utilitarian value, hedonic value, and social value) on subjective well-being (Table 4), confirming the positive. Meanwhile, trust attitude also partially mediates the positive effect of social influence on subjective well-being. Another result was that trust attitude partially mediates the positive effect of perceived ease of use on subjective well-being. Trust attitude partially mediates the positive effect of perceived usefulness on subjective well-being.

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Table 4 Results of mediating effects. M/(IV)/(DV)

Items

Effect

Coefficient

Bias-Corrected

Percentile 95%CI

SE

T

95%CI

Effect

ST/(UV)/(SWB)

Direct effect Indirect effect

0.101 0.262

0.031 0.028

3.292 9.357

0.041 0.209

0.162 0.321

0.041 0.207

0.162 0.319

Partial

ST/(HV)/(SWB)

Direct effect Indirect effect

0.483 0.176

0.029 0.022

16.764 8.000

0.426 0.135

0.539 0.219

0.426 0.135

0.539 0.219

Partial

ST/(SV)/(SWB)

Direct effect Indirect effect

0.458 0.163

0.254 0.019

17.997 8.579

0.408 0.125

0.508 0.201

0.408 0.125

0.508 0.201

Partial

ST/(SI)/(SWB)

Direct effect Indirect effect

0.322 0.221

0.030 0.023

10.671 9.609

0.263 0.176

0.381 0.269

0.263 0.176

0.381 0.267

Partial

ST/(PE)/(SWB)

Direct effect Indirect effect

0.075 0.276

0.031 0.030

2.421 9.200

0.014 0.218

0.137 0.336

0.014 0.218

0.137 0.335

Partial

ST/(PU)/(SWB)

Direct effect Indirect effect

0.178 0.285

0.033 0.030

5.415 9.500

0.113 0.223

0.242 0.339

0.113 0.224

0.242 0.340

Partial

Note: Bootstrap executed 5000 times; IV: independent variable; M: mediator; DV: dependent variable.

4.4. Moderating effects Moderating roles can be tested by accessing differences in path coefficients for each subgroup (Keil and Wassenaar, 2000). To examine differences in user characteristics, path comparison testing was conducted between groups. We dichotomized gender groups as male (group 1) and female (group 2), and age groups as younger (age ≤ 30, group 1) and older (age > 40, group 2) (LiébanaCabanillas et al., 2014). We then compared path coefficients based on the method of categorical moderating variables (Keil and Wassenaar, 2000). The results are shown in Table 5. The results point to significant gender differences in behavior intention and subjective well-being among shared bicycle users. The results of this study suggest that female users are more likely have a trustful attitude through utilitarian values, hedonic values and social influence, while male users are more likely to be trustful because of social values and perceived usefulness. There was no gender difference when considering the effect of perceived ease of use on user’s trust attitudes; but, male users were more likely to have feelings of well-being through trust and personal accomplishment. Age differences have significant effects on users’ behavior and subjective well-being. Younger users are more likely to be trustful

Table 5 Tests on user’s characteristic differences. Items

Path

Gender (N1 = Male) (N2 = Female)

UV → ST HV → ST SV → ST SI → ST PE → ST PU → ST ST → SWB PA → SWB

0.069 0.244** 0.146** 0.085 0.196** 0.249** 0.378** 0.461**

Age

UV → ST HV → ST SV → ST SI → ST PE → ST PU → ST ST → SWB PA → SWB

0.123** 0.320** 0.071 0.119 0.168** 0.152** 0.288** 0.457**

(N1 = Younger) (N2 = Older)

PC1

PC2

T

Remarks

Effect

0.104 0.287** 0.084** 0.130** 0.194** 0.192** 0.319** 0.373**

−11.562 −12.784 19.053 −12.755 0.597 16.904 16.709 25.287

O O O O X O O O

(Female > Male) (Female > Male) (Male > Female) (Female > Male) No difference (Male > Female) (Male > Female) (Male > Female)

−0.017 0.248** 0.111 0.044 0.251** 0.265** 0.400** 0.323**

24.878 10.437 −6.531 10.096 −10.353 −17.374 −17.749 20.269

O O O O O O O O

(Younger > Older) (Younger > Older) (Older > Younger) (Younger > Older) (Older > Younger) (Older > Younger) (Older > Younger) (Younger > Older)

**

Note: Spooled = {[(N1−1)/(N1 + N2−2)] × SE12 + [(N2−1)/(N1 + N2−2)] × SE22} t = (PC1−PC2)/[SPooled × (1/ N1 + 1/ N2)] Spooled = pooled estimator for the variance. t = t-statistic with N1 + N2 − 2 degrees of freedom. Ni = sample size of dataset for group i. SEi = standard error of path in structural model of group i. PCi = path coefficient in structural model of group i. O: support; X: not support. Significance levels: *, p < 0.05 and **, p < 0.01. 20

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because of utilitarian values, hedonic values and social influences, while older users are more likely to be trusting because of social values, perceived ease of use, and perceived usefulness. Furthermore, younger users are more likely to generate subjective well-being through personal accomplishment, while older users are more likely to generate subjective well-being through trust. 5. Discussion Here we investigated factors affecting the subjective well-being of bicycle sharing services. Our integrated model provided evidence that users’ perceived value, social influence, and characteristics of the shared bicycle system are important factors determining users’ subjective well-being. The results also show that perceived value and social influence had a positive effect on subjective wellbeing through trust attitude. This finding has important implications. Subjective well-being is determined at two levels: individual (perceived value) and other people (social influence). In addition, subjective well-being requires trust on others. Characteristics of the bicycle system such as perceived ease of use and perceived usefulness are also relevant to subjective well-being. Finally, subjective well-being can also be affected by psychological factors such as personal accomplishment. The results of this study offer several guidelines and implications for current efforts to promote shared bicycle use. First, hedonic value has the greatest impact on users’ subjective well-being, followed by social value and finally by the utilitarian value. This means that shared bicycle users value the pleasure inherent in the more highly than other factors, operators should guarantee the safety of shared bicycles, as well as enhance the pleasure value of the cycling experience. This could be achieved if operators refined the already high-standard visual design of bicycles. To attract non-users, operators should reinforce the image of utility, pleasure, and happiness of the shared bicycle service in marketing campaigns in addition to ensuring product function and availability. Second, social influence has a positive effect on trust attitude and subjective well-being, operators should apply social influence theory to attract and expand the number of shared bicycle users. As shown by our results, psychological factors such as personal accomplishment are highly relevant. Operators could also take advantage of the communication power of users themselves. For example, Morin (1983) showed that ‘other people’s recommendations’ were three times more effective at driving consumer behavior than advertising. Third, with regard to the characteristics of the bike system, design of human-computer interaction could also be improved through integration of personalized elements in system. Government leaders and shared bicycle companies can use the results of the study to modify their current shared bicycle policy, and publicize shared bicycle regulations to establish positive perceptions and alter social attitudes. We suggest that government leaders and shared bicycle companies could enhance the subjective well-being of residents through improving the quality of service of the shared bicycle transportation system, and enhancing residents' personal value perception and trust attitude towards the shared bicycle industry. More importantly, insights into the gender and age differences of shared bicycle usage, attitude and well-being may be of importance to shared bicycle companies and shared bicycle departments for more effectively developing their shared bicycle marketing strategies. Policy makers should also pay attention to the effects of policies among different gender and age groups. We suggest that government leaders could better promote shared bicycle usage by contrasting its advantages with the disadvantages of other travel modes, such as private cars. In addition, by educating residents, their attitudes towards shared bicycle usage might become more favorable. Bicycle sharing can reduce automobile exhaust emissions and health problems resulting from lack of physical activity. Thus, the government should support and provide more subsidies to public transport initiatives such as bicycle sharing schemes. A final role for the government would be to enact laws governing the shared bicycle market and regulating users’ behavior. Some limitations to this study should be acknowledged. There are advantages and disadvantages to online survey research (Yüksel and Yüksel, 2007). In addition, although many scholars assess subjective well-being through life satisfaction (Gerson et al., 2016; Ishii, 2017), the concept includes people’s emotional responses, domain satisfactions, and global judgments regarding life satisfaction (Diener et al., 1999). Thus, those three components of subjective well-being should be separately analyzed in future research and used to verify our conclusions. Future research should also consider perceived risks and other factors associated with bicycle use and examine their effect on the intention of users to adopt bicycle sharing. Acknowledgements This work is supported by the National Social Science Foundation of China under project number 16AJY003 and 18BGL263. The authors also would like to express appreciation to the anonymous reviewers for their helpful comments on improving the paper. Appendix A Construct measuring

Factor

Measure items

Source

Utilitarian Value (UV)

The use of shared bike is helpful to me The use of shared bike is useful to me The use of shared bike is practical to me

Vries and Carlson (2014)

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Hedonic Value (HV)

The use of shared bike is fun The use of shared bike is exciting The use of shared bike is pleasant

Vries and Carlson (2014)

Social Value (SV)

The use of shared bike improves the way I am perceived The fact I use shared bike makes a good impression on other people The use of shared bike gives me social approval

Shen et al. (2013)

Social Influence (SI)

Most people who are important to me think that I should use a shared bike Most people who are important to me think that using a shared bike is a good idea Most people who influence my decisions think that I should use a shared bike

Borrero et al. (2014)

Perceived Ease of Use (PE)

My interaction with the information system of the shared bike is easy and Chen and Chao understandable (2011) My interaction with the facilities and services of the shared bike system is easy and understandable It is easy for me to use the shared bike system

Perceived Usefulness (PU)

Using the shared bike system could make my travel more convenient Using the shared bike system could make my travel more efficient I find the shared bike system to be useful to my daily travel

Venkatesh (2000)

Personal Accomplishment (PA)

I feel exhilarated after seeing my bicycle mileage I feel exhilarated after seeing my consumed calories

Shih et al. (2013)

Trust Attitude (ST)

Based on my experience with shared bikes, I know it is a trustworthy system Gefen et al. (2003) Based on my experience with shared bikes, I know it is not an opportunistic system Based on my experience with shared bikes, I know it delivers what it promises to users

Subjective Well-Being (SWB)

My experience with a shared bike was memorable and enriched my quality of life Iii et al. (2015) After riding the shared bike I felt that my life was meaningful and fulfilling In general, I felt good about my life shortly after riding the shared bike

Appendix B Loading and cross-loading table.

Items

UV

HV

SV

SI

PE

PU

ST

PA

SWB

UV1 UV2 UV3 HV1 HV2 HV3 SV1 SV2 SV3 SI1 SI2 SI3 PE1 PE2 PE3 PU1 PU2 PU3 ST1 ST2

0.847 0.837 0.817 0.372 0.247 0.412 0.180 0.156 0.148 0.326 0.501 0.397 0.438 0.450 0.457 0.474 0.404 0.365 0.430 0.417

0.357 0.340 0.353 0.844 0.831 0.813 0.554 0.501 0.469 0.468 0.453 0.475 0.298 0.296 0.275 0.314 0.338 0.371 0.468 0.466

0.155 0.151 0.144 0.464 0.517 0.436 0.910 0.899 0.892 0.597 0.372 0.438 0.111 0.132 0.107 0.150 0.225 0.252 0.318 0.315

0.387 0.430 0.421 0.430 0.395 0.548 0.506 0.493 0.491 0.803 0.859 0.837 0.348 0.335 0.347 0.387 0.377 0.401 0.469 0.437

0.443 0.477 0.427 0.303 0.218 0.335 0.145 0.114 0.120 0.246 0.410 0.357 0.835 0.837 0.827 0.479 0.389 0.358 0.441 0.440

0.453 0.453 0.456 0.354 0.324 0.423 0.267 0.252 0.213 0.359 0.469 0.433 0.432 0.445 0.467 0.791 0.785 0.711 0.513 0.481

0.400 0.399 0.433 0.491 0.430 0.515 0.402 0.366 0.346 0.397 0.489 0.463 0.420 0.444 0.406 0.466 0.453 0.430 0.794 0.806

0.316 0.328 0.305 0.438 0.402 0.483 0.414 0.431 0.397 0.411 0.432 0.462 0.324 0.323 0.290 0.328 0.335 0.367 0.433 0.405

0.294 0.283 0.333 0.516 0.565 0.563 0.564 0.562 0.552 0.496 0.404 0.460 0.288 0.304 0.285 0.316 0.352 0.389 0.468 0.452

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ST3 PA1 PA2 SWB1 SWB2 SWB3

0.333 0.349 0.323 0.327 0.265 0.338

0.456 0.508 0.439 0.539 0.562 0.578

0.361 0.452 0.361 0.498 0.549 0.531

0.391 0.502 0.423 0.430 0.472 0.468

0.333 0.344 0.322 0.314 0.282 0.299

0.414 0.403 0.395 0.404 0.384 0.384

0.797 0.464 0.463 0.475 0.519 0.491

0.412 0.895 0.879 0.495 0.532 0.531

0.480 0.562 0.525 0.816 0.870 0.857

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