Identifying the Optimum Combination of Use of Smartphone Apps and Hedonic Motivation for Increasing Public Transit Loyalty: An fsQCA Approach

Abstract

Intensive use of public transit plays an important role in commuting and daily travel in China and many other countries. With the widespread use of smartphones and other information and communication technologies, using mobile applications while on public transit has become increasingly popular, and more passengers rely on them during travel. To understand how the use of smartphone apps during transit travel and hedonic motivation can improve passengers’ willingness to use public transit, this study examines their combined effects. Smartphone apps used during transit travel can be classified into four categories: entertainment; information retrieval; e-ticketing; and mobile working. Drawing on complexity and configuration theories, we propose a theoretical model that is empirically tested using fuzzy set qualitative comparative analysis. Based on an empirical study of 567 Shanghai residents, we present eight different combinations of the use of smartphone apps during transit travel and hedonic motivation that explain a high degree of public transit loyalty. These can be categorized into three types: (a) mobile working oriented; (b) hedonic motivation oriented; and (c) digital management oriented. The findings suggest that satisfaction and happiness with public transit are sufficient but not necessary conditions for public transit loyalty. If passengers use smartphone apps or experience hedonic motivation during public transit travel, their satisfaction and happiness levels are likely to increase. Overall, this paper provides insights to guide policymakers and public transit operators in considering more external factors that might improve passengers’ travel satisfaction, happiness, and loyalty.

Keywords

public transportation quality of service passenger satisfaction information technology online payment

Is long-distance public transit (hereafter referred to as transit, i.e., mainly buses and subway trains) travel boring and irksome? Do passengers perhaps feel that spending a lot of time on transit is a waste of their time? Individuals who travel with smartphones may not think so. Many people use their smartphones to chat, listen to music, or study on the subway in Shanghai, even when it is overcrowded. To date, a great deal of literature on transit travel perception is available for reference; however, the evidence on whether smartphone use or emotions triggered by using transit can result in a better travel experience is limited. This paper will aim to study the impact of smartphone use and hedonic motivation on travel satisfaction during transit.

In Shanghai, the subway has emerged as the primary mode of transportation for residents. Its operating length has reached 831 km, and the daily passenger flow peak throughout the year reached 13.01 million in 2021. Buses also play a significant role in the city’s transportation infrastructure. The service coverage rate of the city’s bus stops within a radius of 500 m has reached 64%, and the average daily passenger volume has reached 4.1 million. However, the rising ownership of private cars has led to a growing trend of self-driving among Shanghai residents. More and more people are opting to purchase their own vehicles and travel independently, making driving a popular transportation choice. However, a survey on transit travel intention in Shanghai shows that transit is the primary mode of travel in the city ( 1 ). Although the demand for non-transit travel has increased because of the pandemic, as have the distances covered by private cars, intensive use of transit still plays an essential role in commuting and daily travel. To improve residents’ transit travel perception and use intention, the Shanghai city government has continuously optimized transit services to create a user-friendly travel environment. However, although governments in other countries (e.g., the USA, the UK, and Canada) have invested in transit, increasing service frequencies and opening more lines to attract riders, the impact has still been limited because this mode of transportation faces numerous barriers ( 2 ). Therefore, policy changes alone may not improve the conditions attached to transit travel in the long term ( 3 ). The reasons travelers are willing to use sustainable and environmentally friendly transport methods have gradually become the focus of scholars’ attention ( 4 , 5 ).

In this respect, when reviewing the existing literature, features such as comfort, reliability, safety, accessibility, and fare structure can all affect how satisfied passengers are with transit as a mode of transportation. In addition, the surrounding built environment and the first-/last-mile experience have been taken into consideration ( 6 ). There has been an increasing focus on happiness when using transportation ( 7 – 9 ). The current study interprets willingness to use transit as a behavioral intention, defined as transit loyalty. Many previous studies have shown that overall satisfaction and happiness directly affect loyalty ( 10 ). Therefore, previous studies have mainly discussed the influence of policies or the quality of services on transit perception and loyalty, but have rarely considered external or emotional factors.

As information and communication technologies have gradually penetrated various fields of daily life, many scholars have conducted empirical studies on what impact this has had on an individual’s travel behavior ( 11 , 12 ). According to the 50th Statistical Report on China’s Internet Development, as of June 2022, the number of Internet users in China has reached 1.051 billion, and the proportion of those using smartphones to access the Internet has reached 99.6%. Similarly, smartphone ownership has grown rapidly around the world. Given the popularity of smartphones, some scholars have explored how they influence the perceptions of different areas in people’s daily lives. For example, the quality of the information and services provided by mobile shopping apps significantly affect customer satisfaction and perceived privacy protection ( 13 ). In addition, some studies have found that the use of smartphone apps during transit travel may improve the perceived reliability of transit services, increase perceived safety, reduce anxiety while waiting, build a positive image of transit, and even help users reduce wait time at transit stops ( 14 ). Therefore, focusing on smartphone apps, we define those that are used during transit travel as transit apps, and categorize them into four categories: entertainment; information retrieval; e-ticketing; and mobile working. As fundamental life problems have been solved, the pursuit of higher levels of pleasure, that is, hedonic motivation, has gradually become popular ( 15 , 16 ). Existing studies have confirmed the positive effects of hedonic motivation on customer perception ( 17 ). Based on the concepts of hedonic shopping, hedonic travel has also emerged ( 18 ). However, in the field of travel, there have been few studies on hedonic motivation as a variable that influences transit perception. Instead of considering the competitiveness of transit with regard to other forms of transportation, we focus on transit users who may have had a good impression of transit in the past or believe that transit can make them experience hedonic feelings.

In empirical studies, multiple statistical techniques, such as multiple regression or structural equation models, which assume that relations between variables are symmetric, are always employed ( 19 ). However, relationships between variables, as in real life, are quite complex and not always proportional or balanced. Indeed, these models suggest that a predictor needs to be both a necessary and sufficient condition to achieve the desired outcome. They assume the relationship between variables to be symmetric and compute the best solution to explain the outcome. Simply focusing on symmetric relations may be misleading because such effects do not apply to all cases in the data set; thus, the relationship between two variables is unlikely to be symmetric ( 20 , 21 ). For example, high convenience may lead to high satisfaction, and high convenience can also result from high satisfaction, indicating that relationships between variables are symmetric. In addition, high convenience may not lead to high satisfaction, but high satisfaction must come from high convenience and other conditions. Convenience may be a necessary condition for satisfaction, indicating that asymmetric relationships exist between variables.

To address the gap in the literature, we draw on complexity and configuration theories and seek to capture what causal patterns of factors lead to high satisfaction, happiness, and loyalty because of using smartphone apps during transit travel and the influence of hedonic motivation. The following research questions are addressed:

RQ1. How does the use of different smartphone apps during transit travel and the influence of hedonic motivation combine to explain passengers’ high or low/medium degree of transit loyalty?

RQ2. What conditions in relation to the use of smartphone apps during transit travel and the influence of hedonic motivation are sufficient or necessary to create causal combinations that explain a high or low/medium degree of transit loyalty?

To answer each research question, we employ a fuzzy set qualitative comparative analysis (fsQCA) ( 22 ). FsQCA provides multiple solutions that can explain an outcome, thus identifying the causal combinations of the four categories of transit apps and the influence of hedonic motivation that affect the perception of and loyalty to transit travel. The findings identify eight different combinations of the factors mentioned above that explain the outcomes, which can be divided into three types of orientation: (a) mobile working oriented; (b) hedonic motivation oriented; and (c) digital management oriented. The contribution of this paper to the literature is threefold: first, we extend the literature by exploring the combined effects of the use of different smartphone apps during transit travel and the influence of hedonic motivation on transit travel satisfaction, happiness, and loyalty; second, we employ fsQCA, an innovative data analysis methodology that offers a deeper insight into the data and should be considered an alternative and complementary method to traditional variance-based approaches; third, we aim to identify the interplay among the constructs. The findings may guide policymakers and transit operators to consider external factors that might affect user satisfaction and the competitiveness of transit in addition to relatively good transit infrastructure and services.

The paper is organized as follows. Following the introduction, Section 2 introduces the theoretical background and discusses the conceptual model and research propositions. Section 3 outlines the research methods and data, and Section 4 presents the empirical results. Section 5 concludes and Section 6 considers some implications.

Theoretical Background and Conceptual Model

Smartphone Use during Travel

All communication equipment and application software appear under the umbrella of information and communication technologies. Smartphones are among the technologies that have developed rapidly and are carried conveniently. Mobile-based technologies have permeated our daily lives and dramatically transformed the way we travel. The literature on smartphone apps and other information and communication technologies has looked at cognitive behavioral theory, suggesting that people use these technologies to relieve loneliness, pass the time, and escape reality. One previous study investigated how information and communication technologies have influenced behavior in the fields of transport, health, energy, and climate ( 23 ), and another examined the current knowledge in relation to information and communication technologies and travel behavior based on several key theoretical concepts ( 24 ). According to existing literature, the use of smartphone apps during transit travel presents a cost-effective strategy that could facilitate and promote transit use and improve user experience. For example, some studies found that the use of smartphone apps during transit travel may improve the perceived reliability of transit services and relieve boredom and anxiety while waiting. For instance, if a person intends to travel, but according to the real-time information searched by the smartphone the next bus will take 20 min to arrive at the stop, he/she may wait at home for 10 min before going out. This example shows that transit apps may help users reduce the waiting time at the bus stop. Some studies reported that the use of smartphone apps during transit travel might potentially boost ridership ( 2 ). Therefore, we mainly explore the impact of the use of smartphone apps during transit travel on travelers’ perception and loyalty.

As smartphone use has become prevalent, the use of smartphone apps during transit travel emerged as an indispensable channel for disseminating relevant and timely information. In addition, other apps not directly related to transit as such, for example, WeChat, and games and music apps are often used during transit travel. Therefore, going beyond the scope of previous literature reviews on the use of smartphone apps during transit travel, we consider a range of apps covering real-time information, route planning, e-ticketing, mobile working, and entertainment. Travelers often use smartphones for entertainment and stress relief, for example, surfing the Internet, listening to music, and so forth, which can make the journey seem shorter ( 25 ). In addition, Brakewood et al. have demonstrated that real-time information provided by OneBusAway significantly reduced passengers’ perceived wait time. Others supported this argument, claiming that users of real-time information typically experienced shorter wait times for commuter rail services ( 26 ). Zhu and Fan ( 9 , 27 ) have indicated that travel duration is negatively correlated with travel satisfaction, so entertainment and information retrieval apps may have a positive impact on transit travel satisfaction ( 28 , 29 ). Carreira ( 30 ) mentioned that convenient access to information is a factor affecting the transit travel experience. Many smartphone apps, such as Google Maps and Baidu Maps, have been developed to provide travel information in advance and to enhance the travel experience. In emerging markets, technological innovations have transformed traditional payment patterns into electronic payment ( 31 ), and some results suggested that most e-ticket users spent less time purchasing bus or subway passes. Consequently, riders do not have to risk missing the bus or subway to wait in line at the ticket vending machine. Both passengers and transit drivers noticed that e-ticketing apps made boarding more efficient ( 32 ). A method of payment for travel across all modes of transportation can provide travelers with a safe payment environment and increase the efficiency of transit travel ( 33 ). Because many mobile payment apps have been developed to provide convenient payment methods (digital management), this may have a certain impact on transit travel. Notably, the huge increase in mobile working, for example, videoconferencing and remote teaching, has greatly benefited people. A literature review published in 2022 identified key user benefits in three domains: changes in perception and psychological changes; time savings on trips; and changes in travel behavior. In particular, Chakrabarti ( 2 , 34 ) pointed out that mobile working would increase the frequency of transit or active travel to a certain extent because people would realize they could save time by working during travel. Loo and Wang ( 35 ) proposed that remote communication devices, such as smartphones, enable people to control the time at which they deal with tasks and also where they deal with them. Considering such circumstances, we assume that using smartphones for mobile working during transit travel will, inevitably, affect the transit experience.

Hedonic Motivation during Travel

Hedonic motivation is the willingness to initiate behaviors that enhance the positive experience (pleasant or good) and decrease the negative experience ( 36 ). Hedonic motivation has been considered in the context of well-being, in which hedonic motivation (seeking pleasure and avoiding pain) is contrasted against eudaimonia (seeking personal excellence) to explain how people differ in their pursuit of happiness ( 37 ). Because of the improvement in infrastructure and the integration of services, transit travel might gradually become less and less fraught in Shanghai. Similarly, in other countries, even though problems such as unreliable services, long travel times, the difficulty of reading/working while traveling, and lack of accessibility to transit services still exist, various transit agencies have made some significant investments. Although their impact on transit is still small, it is certainly progressive. As well as objective factors, subjective factors such as pragmatism and habit may also affect travelers’ intention to use transit. Here, we define hedonic motivation in relation to transit as a special emotion or a habit that may cause travelers to have nice memories about transit or think that it can make them feel good or hedonic.

It has been proved that hedonic motivation plays a mediating role between performance expectancy and behavior intention, for example, willingness to use a specific service ( 38 ). In addition, hedonic motivation has been shown to have a significant role in the continued use of information technology ( 39 ). Kim and Forsythe ( 40 ) indicated that hedonic motivation had a stronger positive relationship than functional motivation with a person’s attitude toward buying clothes online. In addition, Tyrväinen et al. ( 17 ) examined the effects of hedonic motivation on customer experience and loyalty in the retail context. However, to the best of the authors’ knowledge, there is little research that has looked at the influence of hedonic motivation on transportation services, modes, or technologies, and the role played by hedonic motivation in transit perception and loyalty is worthy of further investigation.

Satisfaction, Happiness, and Loyalty

Travel satisfaction is the direct cognitive response to a travel experience ( 41 ), and travel satisfaction refers to a person’s degree of satisfaction with their own travel experience, including planning before travel, the various experiences undergone and feelings experienced during travel, and even retrospective evaluation after travel. This research specifically defines travel satisfaction as an evaluation of the transit travel experience. Travel happiness is a relatively new research field. Some scholars have studied the influence of travel characteristics and personal attributes on travel happiness ( 9 ). Some scholars have pointed out that the spillover effect of the happiness felt during travel may affect the activities carried out afterwards ( 42 ). Here, travel happiness refers to the feelings and emotional state of individuals during transit travel in relation to comfort, relaxation, and pleasure. Compared with travel satisfaction, which emphasizes the specific experience and evaluation of travel, travel happiness pays more attention to personal emotional experience and feelings. The articles reviewed showed that the use of smartphone apps during transit travel offered significant perception benefits to users, including enhanced perceived safety, reduced waiting anxiety at transit stops, increased perceived reliability of transit services ( 14 ), and improved positive impressions about transit ( 43 ). A randomized controlled trial in Tampa, Florida, in the USA, reported that compared with the control group, respondents in the experimental group were significantly more satisfied with the on-time transit performance after using the OneBusAway app. In addition, other qualitative and descriptive statistical results showed that e-ticketing apps might also offer satisfaction and happiness benefits, arguing that e-ticketing makes it easier to purchase transit passes and eliminates the hassle of carrying cash, change, paper tickets, or maps, thereby making transit use more flexible ( 32 ). Therefore, the use of smartphone apps during transit travel is beneficial for improving both travel satisfaction and happiness.

Transit loyalty is the main factor that influences willingness to use transit, and the target variable is transit loyalty. In the literature, travel satisfaction has been confirmed to be an important factor affecting transit loyalty. When passengers perceive the quality of transit services and are satisfied, they will be willing to continue using them and also be willing to recommend them ( 30 ). Compared with transit travel satisfaction, few scholars have proposed a direct relationship between happiness and loyalty. Because Gong and Yi ( 44 ) have put forward the relationship between satisfaction, happiness, and loyalty in the consumer field, we can imagine that travel satisfaction or happiness may also influence loyalty in the field of travel. If we consider the use of smartphone apps during transit travel, more than 30% of app users stated that they made transit trips more often ( 32 , 45 ). Some studies claimed that the deployment of real-time vehicle information via the use of smartphone apps during transit travel boosted bus ridership by about 2% in Chicago and New York ( 46 , 47 ). However, as smartphone functions have increased, simply considering the function of travel information retrieval would not give a complete or accurate picture. Rather, it would be meaningful to consider the use of smartphone apps during transit travel to explore which apps would improve transit loyalty.

Complexity and Configuration Theories

Relationships between factors are complex, and depending on how they combine with each other, both high and low scores of a certain factor may explain the high scores of an outcome. Based on complexity theory and the principle of equifinality, a result may be equally explained by alternative sets of causal conditions, which may be coalesced in sufficient configurations to explain the outcome ( 20 , 48 ). According to the previous literature review, the use of different smartphone apps during transit travel may be an important influence on travelers’ perception and loyalty; thus, they may interact with each other in various configurations. Therefore, because different people care about different experiences of transit, the attributes that can make individuals satisfied and happy will probably be different too.

Configuration theory proposes the principle of causal asymmetry or the possibility that the paths to the absence of the same outcome are diametrically different from the paths to its presence ( 48 – 50 ). A predictor may have an asymmetric relationship with the outcome, meaning that even if one variable is insufficient for the outcome to occur, it can still serve as a necessary condition for the same outcome. In this case, a necessary condition is a variable present, at least to a degree, in every configuration that explains the outcome, making it indispensable to the outcome. In addition, the same outcome may be positively or negatively influenced by a specific factor, depending on how it combines with the other factors. The use of smartphone apps during transit travel combined with hedonic motivation have an influence on travel satisfaction, happiness, and even loyalty. Because travelers’ needs differ, it is necessary to examine how the combination of the use of different categories of smartphone apps and hedonic motivation during transit travel can explain the results. For example, people who seek travel information or entertainment during their journeys are more likely to be satisfied with transit if they feel comfortable and relaxed. At the same time, they may still be satisfied even if transit travel lacks convenience, but they can use their travel time for mobile working or e-learning using a smartphone. In addition, there are a significant number of travelers who pursue hedonism. Even if people do not use smartphones during travel, hedonic travel per se will also make them satisfied with transit. Therefore, a high level of satisfaction and happiness with transit travel may be achieved in various ways.

Conceptual Model

Based on the above discussion, we have confirmed that there are multiple and equally effective configurations of smartphone apps used during transit travel and hedonic motivation that explain travelers’ high level of transit satisfaction and happiness, and high degree of loyalty. Transit travelers may use a variety of different apps, including those concerned with entertainment, information retrieval, e-ticketing, and mobile working, and there have been some discussions about whether the use of these apps has an impact on travelers’ perception and loyalty. Additionally, hedonic motivation may drive positive travel perception and a high degree of loyalty. Following the previous discussion, we suggest that the combination of smartphone apps used during transit travel and hedonic motivation should be used to explain travelers’ satisfaction, happiness, and loyalty.

To conceptualize these asymmetric relationships, we propose a conceptual model (Figure 1), which shows five independent variables and their intersections in the middle, the outcomes on the left, and the negated outcomes on the right. The factors examined, their intersections, and overlapping areas represent possible combinations between factors.

Figure 1.

Venn diagram of the conceptual model.

As shown in Figure 1, we expect that the combination of the use of smartphone apps and hedonic motivation during transit travel will lead to a high level of travel satisfaction and happiness, and a high degree of loyalty. The combinations will refer to different kinds of travelers. Thus, we formulate the following propositions.

Proposition 1a: Multiple configurations of smartphone apps used during transit travel and hedonic motivation will lead to changes in transit travel satisfaction.

Proposition 1b: Multiple configurations of smartphone apps used during transit travel and hedonic motivation will lead to changes in transit travel happiness.

Proposition 1c: Multiple configurations of smartphone apps used during transit travel and hedonic motivation will lead to changes in transit loyalty.

Relationships between two factors (e.g., A and B) are complex, and the presence of one (A) may lead to the presence of the other (B), suggesting sufficiency. Further, factor B may also be present when A is absent. Thus, A is a sufficient but unnecessary condition for B to occur. Following the previous discussion, we know that travel satisfaction and happiness are the important variables that influence transit loyalty, but we have not understood their relationship. Therefore, our conceptual model predicts the following:

Proposition 2a: Travel satisfaction is a sufficient but unnecessary condition for transit loyalty.

Proposition 2b: Travel happiness is a sufficient but unnecessary condition for transit loyalty.

Data and Methodology

Measure

A questionnaire survey method was employed to collect data to test the model hypotheses given above. The questionnaire comprised a series of measurement items, most of which were adapted from existing scales or literature. Some adjustments and additions have been made to make the items more suitable for studying the issues in question. The measurement items are scored using a 5-point Likert scale, from never/very slightly/strongly disagree (1) to every time/very strongly/strongly agree (5).

The use of smartphone apps during transit travel often depends on travelers’ needs. We summarized smartphone use while traveling based on Jamal and Habib ( 51 ) and combined the activities they cite to determine four kinds of smartphone use, which were measured by asking respondents how often they use related apps during travel. When measuring hedonic motivation in the field of travel, we used hedonic indicators in the field of consumer behavior for reference. For the measurement of travel satisfaction, considering the actual situation of transit, we combined the cognitive evaluation aspects of the Satisfaction with Travel Scale ( 52 ) and the Satisfaction with Life Scale ( 53 ) to ask the interviewees some questions. McCabe and Johnson pointed out that subjective well-being was a comprehensive concept ( 54 ). We used the Subjective Well-being Scale they proposed for reference, considered the specific situation, and asked the interviewees five questions. Finally, we applied consumer loyalty to transit loyalty, and measured it according to two dimensions: intention to reuse; and willingness to recommend ( 55 , 56 ). Table 1 shows each item in detail.

Table 1.

Descriptive Statistics of the Conditions

Conditions	Items	Mean	SD	CR	AVE
Entertainment	[En1] Using smartphones to browse the web during transit travel. [En2] Using smartphones to relax during transit travel. [En3] Using smartphones to take photos during transit travel. [En4] Using smartphones to chat during transit travel.	4.000	0.785	0.879	0.646
Information retrieval	[IR1] Using smartphone apps to query timetables during transit travel. [IR2] Using smartphone apps to view the real-time vehicle during transit travel. [IR3] Using smartphone apps to transfer during transit travel. [IR4] Using smartphone apps to track the journey during transit travel. [IR5] Using smartphone apps to navigate during transit travel.	3.966	0.762	0.882	0.599
E-ticketing	[ET1] Using smartphone apps to pay for travel. [ET2] Using smartphone apps to scan the code to enter and exit the station. [ET3] Using smartphone apps to pay the fare during transit travel.	4.093	0.813	0.889	0.727
Mobile working	[MO1] Receiving tasks remotely through smartphones during transit travel. [MO2] Timely handling of tasks received through smartphones during transit travel. [MO3] Timely submission of tasks received through smartphones during transit travel.	3.930	0.848	0.860	0.673
Hedonic motivation	[HM1] I have felt more enjoyment during transit travel. [HM2] I have felt more relaxed during transit travel. [HM3] I forget my troubles temporarily during transit travel. [HM4] Transit travel left me with better impressions.	4.008	0.815	0.888	0.665
Travel satisfaction	[TS1] Considering the cost, transit travel is the best I can imagine. [TS2] It is the right decision to choose transit. [TS3] The general service provided by the current transit system is quite satisfactory. [TS4] Overall, I am satisfied with my transit experience.	4.033	0.795	0.880	0.648
Travel happiness	[TH1] Using smartphones while traveling will make me feel that using transit is convenient. [TH2] Using smartphones while traveling will make me feel that using transit is pleasant. [TH3] Using smartphones while traveling will make me feel that using transit is meaningful. [TH4] Using smartphones during transit travel will increase my happiness. [TH5] Compared with self-driving, transit travel will increase my happiness.	3.985	0.784	0.892	0.625
Transit loyalty	[PTL1] Because of its hedonic experience and convenience, I will use transit again. [PTL2] Because of its green credentials and sustainability, I will use transit again. [PTL3] I will convert some of my car travel to transit travel. [PTL4] I will encourage/recommend transit travel to others.	4.034	0.800	0.881	0.650

Note: SD = standard deviation; CR = construct reliability; AVE = average variance extracted.

Data Collection

We used a website that provides online survey services (https://www.wjx.cn/) to obtain data, and a total of 567 people in Shanghai participated in the survey. Because there were no missing values or outliers in each questionnaire, and the interviewees also met daily transit travel conditions and used smartphones, 567 valid questionnaires were obtained. It is worth noting that although the strength of mobile signals can be problematic, mobile connectivity is widely available throughout the Shanghai metro system. The sample consisted of more women (50.44%) than men (49.56%). Most respondents were 20 to 40 years of age (66.14%), and only 4.59% were over 50 years of age, indicating that older people might tend not to use smartphones during travel or be reluctant to respond to the survey because our questionnaire was published online. Students and full-time employees accounted for the majority (71.07%) of the respondents, possibly because of their fixed daily travel and their relatively high frequency of smartphone use as well as their proficiency in using smartphones. In addition, 73.72% of respondents mostly used transit (i.e., buses and subway trains in the questionnaire) as their travel choice, consistent with Shanghai’s overall transit utilization rate. In summary, the data collected were suitable for our study.

The conceptual model consisted of eight constructs, a total of 32 items, and no error terms because of using the observed value as the reference value and not considering the random error terms. Table 1 shows the descriptive statistical results of the constructs, including the mean and standard deviation. The rule of thumb points out that a construct reliability (CR) value of higher than 0.70 indicates an acceptable level of internal consistency reliability ( 57 ). In more advanced stages of research, values between 0.70 and 0.90 can be regarded as satisfactory. An average variance extracted (AVE) of no lower than the minimum level of 0.5 is required to ensure a higher convergence validity ( 58 , 59 ). Here, the CR values are all higher than 0.860, and the AVE ranges between 0.599 and 0.727. Furthermore, we test for multicollinearity, and the variance inflation factor for all factors is lower than the recommended value (<5). Thus, multicollinearity is not an issue.

FsQCA

FsQCA is the combination of fuzzy sets and logic principles with qualitative comparative analysis ( 60 ) that has been applied to examine the condition combinations for the acceptance of or satisfaction with technology such as social networking services and e-commerce (Tran et al. [ 61 ]). FsQCA can calculate multiple solutions that lead to the same outcome. The multiple solutions may include both necessary and sufficient conditions, which may appear as present or negated in a solution. In addition, both necessary and sufficient conditions may be present (or absent) as core conditions, indicating a strong causal relationship with the outcome, or as peripheral conditions, indicating a weaker relationship with the outcome ( 48 ). If a condition is necessary, its consistency should exceed the threshold of 0.9 ( 62 ).

Data calibration is one of the most important steps in fsQCA. In this step, we need to calibrate all values of the variables into fuzzy sets with a value range of 0 to 1 ( 22 ). Data calibration can be either direct, during which the researchers choose three qualitative thresholds as anchors to calibrate all the values, or indirect, during which the factors are calibrated following qualitative assessments. For direct data calibration, the three thresholds are full-set membership, the crossover point, and full-set nonmembership, representing the level at which a case belongs to a set ( 22 ). The most straightforward method of calibrating the data is to choose the values of 1, 0.5, and 0 as breakpoints. However, when using fsQCA software, the values follow a logarithmic transformation, with the breakpoints for full-set membership and full-set nonmembership being 0.95 and 0.05, instead of 1 and 0, because the log-odds transformation is not capable of producing memberships that are exactly equal to 0 or 1. Because the variables or constructs in this research are measured with multiple items, we need to calculate a value for each construct as input into fsQCA (i.e., for each case in the data set, each construct needs a value). The simplest way is to calculate the average value of all items to provide a single value for each case. Here, we perform a direct calibration after calculating the average value of all items. In each indicator data set, the full membership threshold is fixed at 0.95, the full nonmembership threshold is fixed at 0.05, and the crossover point is fixed at 0.5.

After the calibration, we run the fuzzy set algorithm in the fsQCA software, which computes a truth table of 2^k rows (k is the number of predictors, and each row represents every possible combination of the causal predictors). The truth table includes all possible combinations and presents the frequency for each one (i.e., the number of observations for each combination), which means some combinations may have zero frequency. In addition, the consistency for each combination is presented in the truth table. Consistency refers to the degree of correspondence between cases and the set-theoretic relationships expressed in a solution ( 22 , 48 ). Next, it is necessary to sort the table based on the frequency and consistency values. Specifically, to ensure a minimum number of observations for the assessment, a frequency threshold should be set. Setting the threshold value requires taking into account the knowledge and experience of experts in the research field, and requires a consideration of sample size, number and type of variables, and other factors too. For our large-scale samples (567, over 150 cases), the cut-off point should be set at higher than 1. Here, the frequency threshold is set at 4, and this threshold can ensure the stability and reliability of the results of the sufficient necessity analysis. If the value is too low, it may lead to a lack of stability and reliability in the research conclusion. At the same time, if the value is too high, it may neglect some necessary conditions/factors. In fsQCA, setting the frequency threshold at 4 means that each condition/factor must appear in at least four unique samples to be regarded as a necessary condition/factor.

Finally, the fsQCA software outputs three sets of solutions (complex, parsimonious, intermediate) ( 54 ). To explain the three solutions, the concept of the logical remainder should be understood. The logical remainder is a configuration (combination of conditions) lacking empirical instances that may be included in the Boolean minimization. The complex solution is based on the practical observation cases, excluding any logical remainder, and usually contains the largest number of configurations, whereas the parsimonious solution includes all logical remainder and the number of configurations is the least. The intermediate solution only includes the logical remainder expected by the theoretical direction, and its moderate complexity seeks to balance the complex and parsimonious solutions. Because the conditions appearing in the parsimonious and intermediate solutions at the same time are the core conditions that have an important impact on the results, we present the intermediate solution and identify core conditions according to the parsimonious solution.

Results

Analysis of Necessity

The analysis of necessity refers to the process of determining whether variables with high causal weight are necessary factors in fsQCA. The purpose of the analysis of necessity is to determine the key factors in the model, that is, the combination of the least necessary factors, which may help us to understand the relationship between different factors. The findings from the analysis of necessity are shown in Table 2. We test the necessary conditions for the presence of transit loyalty (high degree of transit loyalty) and its negation (i.e., no transit loyalty or a low/medium degree of transit loyalty is the negation of transit loyalty). According to the above, transit loyalty here is measured with a 5-point Likert scale; thus, a high degree of transit loyalty means that respondents rated their loyalty levels as 4 or 5. Specifically, for a high degree of transit loyalty, the consistency values range between 0.499 and 0.805 for both the presence and negation of the causal conditions. None of the causal conditions exceed the value of 0.9 ( 62 ), so there are no necessary conditions for transit loyalty. Similarly, for a low degree of loyalty, no low levels of causal conditions are necessary. Thus, we proceed with the fuzzy set analysis to identify combinations of causal conditions that explain a high degree of loyalty. In addition, the coverage scores of these conditions indicate that these factors can be considered nontrivial, because they have high coverage scores (i.e., 0.545 and above). Nontrivial necessary conditions are those that exert some constraint on the outcome. In contrast, trivial conditions are the ones that are strongly present in most cases (i.e., high consistency), whether they display the outcome or not (i.e., low coverage). The results imply that even though travelers have a low level of one of the causal conditions, as mentioned, there may still be a high degree of transit loyalty for other conditions. After testing the necessity, we analyze the configurations of the target variables.

Table 2.

Analysis of Necessity for the Presence and Negation of Transit Loyalty

	Transit loyalty		~Transit loyalty
Causal conditions	Consistency	Coverage	Consistency	Coverage
Entertainment	0.792	0.819	0.703	0.585
~Entertainment	0.599	0.714	0.782	0.752
Information retrieval	0.778	0.817	0.709	0.599
~Information retrieval	0.618	0.726	0.783	0.740
E-ticketing	0.759	0.802	0.717	0.610
~E-ticketing	0.630	0.734	0.768	0.720
Mobile working	0.824	0.749	0.744	0.545
~Mobile working	0.499	0.708	0.658	0.751
Hedonic motivation	0.791	0.803	0.716	0.585
~Hedonic motivation	0.591	0.721	0.760	0.746
Travel satisfaction	0.805	0.809	0.720	0.582
~Travel satisfaction	0.584	0.722	0.763	0.759
Travel happiness	0.791	0.808	0.717	0.590
~Travel happiness	0.598	0.724	0.766	0.747

Configuration Analysis for a High Degree of Transit Loyalty

The analysis of sufficiency determines which combination of factors can produce specific results from multiple possible combinations in fsQCA. The purpose of the analysis of sufficiency is to determine the sufficient combination of models, that is, the combination that produces specific results. This kind of analysis can help us obtain a better understanding of complex relationships and identify the interaction between factors. The findings from the fsQCA with regard to the configurations for a high degree of transit loyalty are presented in Table 3. Every combination in the solution can explain the same outcome at a specific amount. Conditions (core or peripheral) may be either present, negated, or absent with no influence on the solution. Consistency values presented for each solution and the overall solution are higher than the recommended threshold (>0.75). Consistency shows the degree to which a relationship has been approximated, and coverage evaluates the empirical relevance of a consistent subset. The overall consistency is similar to the correlation showing how strong the solution is, and the overall solution coverage indicates the extent to which a high degree of transit loyalty may be determined from the existing configuration, which is comparable with the R-square value reported in traditional regression analyses. The overall solution coverage of 0.805 shows that the eight paths explain a large proportion of the outcome. These paths are combinations of variables and include variables as predictors of the outcome only in a small subset of cases but remain important for the outcome. Furthermore, fsQCA computes the empirical relevance of each solution by calculating raw and unique coverage. The raw coverage describes the proportion of the outcome that is explained by a certain alternative solution, whereas the unique coverage describes the proportion of the outcome that is explained exclusively by a certain alternative solution. For a high degree of loyalty, the solutions present combinations for which the factors examined may be present or absent, depending on how they combine with each other.

Table 3.

Configuration Analysis for a High Degree of Transit Loyalty

Configuration	1a	1b	1c	2a	2b	2c	3a	3b
Condition
En		•	⨂		⨂	•	⨂	⨂
IR		⨂	•		⨂	•		•
ET	⨂				⨂	⨂	•	•
MO	•	•	•	•			⨂
HM				•	•	•	⨂	⨂
Consistency	0.912	0.944	0.936	0.858	0.964	0.975	0.955	0.964
Raw coverage	0.582	0.548	0.526	0.724	0.361	0.527	0.303	0.403
Unique coverage	0.010	0.006	0.003	0.037	0.004	0.009	0.004	0.000
Solution consistency	0.827
Solution coverage	0.805

Note: En = entertainment; IR = information retrieval; ET = e-ticketing; MO = mobile working; HM = hedonic motivation. Black circles (•) indicate the presence of a condition, and circles containing an “x” (⨂) indicate its absence. Large circles indicate core conditions, small ones indicate peripheral conditions. Blank spaces indicate “do not care”.

Solutions 1(a–c): Mobile working oriented. Travelers have a high degree of transit loyalty when they have a high frequency of using smartphones for mobile working, regardless of whether they think transit travel is hedonic as far as they are concerned. Based on this, there are three situations: low frequency of using e-ticketing apps (MO*∼ET); high frequency of using entertainment apps (MO*En*∼IR); or high frequency of using information retrieval apps (MO*∼En*IR). This shows that using transit travel time to process work has played an important role in improving transit loyalty. Under this premise, using smartphones to pass the time or obtain traffic information during travel can also improve loyalty. These solutions explain the high degree of loyalty displayed by 58.2%, 54.8%, and 53% of the sample for solutions 1a, 1b, and 1c, respectively. For example, a person suddenly receives a call from his/her boss when traveling and is asked to prepare a report to submit immediately. Facing this situation, travelers will be more inclined to choose transit travel and use the duration of the journey to process unexpected work, even if they have previously regarded such an experience as not hedonic.

Solutions 2(a–c): Hedonic motivation oriented. When travelers have previously experienced hedonic travel or consider transit travel to be hedonic as far as they are concerned, there are three situations that result in a high degree of transit loyalty. First, the higher level of hedonic motivation and higher frequency of using mobile working apps during travel coexist, regardless of using smartphones for other purposes, which supplements solutions 1 to 3. This solution explains the high degree of transit loyalty (72.4% of the sample), indicating that travelers are most likely to continue using transit for hedonic motivation and because they intend to use mobile working. Second, travelers have a high degree of loyalty when they have a high level of hedonic motivation. At the same time, they have a low frequency of using entertainment, information retrieval, and e-ticketing apps during travel, which explains the high degree of transit loyalty displayed by 36.1% of the sample. The third scenario explains the high degree of transit loyalty displayed by 52.7% of the sample, which will be higher with a higher level of hedonic motivation and frequency of using entertainment and information retrieval apps. We can imagine that if in addition to handling work during travel, the basic services such as accessibility, punctuality, on-board comfort, and external conditions are all perfect, people will have a hedonic travel experience. Thus, the probability of people continuing to use transit may increase.

Solutions 3(a–b): Digital management oriented. Different from the above, these two solutions indicate that when people use smartphones more frequently to pay for fares and sometimes search for travel information during their journeys, even if the frequency of using entertainment and mobile working apps is low, or the degree of hedonic motivation is not high, they will have a higher degree of transit loyalty. The two solutions 3a and 3b explain the high degree of loyalty displayed by 30.3% and 40.3% of the sample, respectively. This category of travelers does not care about the level of hedonic motivation. They may not use smartphones to pass the time or work, but if transit goes paperless (digital management), their loyalty will increase.

Previous studies have shown that transit loyalty is affected by travelers’ satisfaction with all aspects of transit ( 63 , 64 ), and by various emotional factors, especially travel happiness ( 7 , 8 ). Therefore, after the sufficiency analysis of a high degree of transit loyalty, to obtain a better understanding of the impact of travel satisfaction and happiness on loyalty, we take the use of smartphone apps and hedonic motivation as causal conditions to carry out further sufficiency analysis. Surprisingly, the condition configurations that cause high levels of satisfaction and happiness are consistent with a high degree of loyalty, except for the slight difference in coverage. Thus, we think that travel satisfaction and travel happiness are sufficient conditions for transit loyalty. Taking satisfaction and happiness as conditions, the sufficiency analysis of a high degree of transit loyalty is complete, and the results shown in Table 4 verify propositions 2a and 2b.

Table 4.

Constructs for Predicting a High Degree of Transit Loyalty

Constructs	Raw coverage	Unique coverage	Consistency
Travel satisfaction	0.805	0.805	0.809
Solution consistency: 0.809
Solution coverage: 0.805
Travel happiness	0.791	0.791	0.808
Solution consistency: 0.808
Solution coverage: 0.791

Configuration Analysis for a Low Degree of Transit Loyalty

Under the premise that the same configurations lead to a high level of satisfaction and happiness, and a high degree of loyalty with transit, we further test for negating the outcomes, with the aim of obtaining a better understanding of the internal combination of conditional factors.

We find that among the seven configurations in Table 5 that cause a low level of satisfaction, information retrieval and hedonic motivation appear as absent core conditions in four configurations, indicating that travelers are dissatisfied with transit. To a large extent, this is because of shortcomings in relation to the provision of travel information. In addition, the travel experience is fraught rather than hedonic, making satisfaction with it difficult. Unlike the results for a low level of satisfaction, e-ticketing appears four times as the absent core condition of a low level of happiness in Table 6, even though the configuration (∼ET*MO) explains 58.6% of the sample. We regard information retrieval and e-ticketing as convenience services in relation to travel, and they are necessary for happiness. Poor quality convenience services with regard to travel have greatly reduced travel happiness. For a low degree of loyalty, each antecedent variable is an important absent core condition, especially the use of mobile working apps. The results in Table 7 are proof of the competition between travel modes. Because private cars have gradually taken the place of transit, to increase transit use, there needs to be a significant improvement in infrastructure, basic services, and digital services, but also additional advantages over private cars. Compared with driving, during transit, people can receive and process tasks remotely or chat and play games, which drivers cannot do because they need to concentrate on the task in hand.

Table 5.

Configuration Analysis for a Low/Medium Level of Travel Satisfaction

Configuration	1	2	3	4	5	6	7
Condition
En	⨂	⨂		•	•	⨂	•
IR		⨂	⨂	⨂	⨂	•	•
ET		⨂	⨂		•		⨂
MO	⨂			•	⨂		⨂
HM	⨂		⨂	⨂	•	⨂	•
Consistency	0.862	0.854	0.850	0.797	0.816	0.785	0.816
Raw coverage	0.514	0.572	0.567	0.431	0.363	0.437	0.360
Unique coverage	0.007	0.032	0.004	0.021	0.014	0.044	0.025
Solution consistency	0.783
Solution coverage	0.855

Table 6.

Configuration Analysis for a Low/Medium Level of Travel Happiness

Configuration	1	2	3	4	5	6	7	8
Condition
En	⨂	⨂		•	•	⨂	•
IR		⨂	⨂	⨂	⨂	⨂	•
ET		⨂	⨂		•		⨂	⨂
MO	⨂			•	⨂	•		•
HM	⨂		⨂	⨂	•	•	•
Consistency	0.858	0.869	0.876	0.806	0.801	0.795	0.800	0.759
Raw coverage	0.502	0.571	0.574	0.428	0.349	0.421	0.524	0.586
Unique coverage	0.020	0.004	0.004	0.019	0.008	0.016	0.005	0.025
Solution consistency	0.759
Solution coverage	0.904

Table 7.

Configuration Analysis for a Low/Medium Degree of Transit Loyalty

Configuration	1	2	3
Condition
En	⨂	⨂
IR		⨂	⨂
ET		⨂	⨂
MO	⨂	⨂	⨂
HM	⨂		⨂
Consistency	0.848	0.871	0.865
Raw coverage	0.509	0.467	0.458
Unique coverage	0.082	0.040	0.032
Solution consistency	0.823
Solution coverage	0.580

In addition, the configuration (∼En*∼MO*∼HM) leads to a low level of travel satisfaction and happiness and a low degree of transit loyalty. Entertainment is the absent core condition, in other words, it is not the core condition for a high degree of transit loyalty, indicating that using entertainment apps during transit may not improve loyalty. However, if these are unavailable, there is likely to be a less positive evaluation of transit, and loyalty to this form of transportation will decrease. Furthermore, it is interesting to find that for a low level of satisfaction with transit and a low degree of transit loyalty, the use of mobile working apps during transit travel appears as an absent core condition, although it is a peripheral condition for a low level of travel happiness. It is not difficult to explain why when people are on a bus or a subway, they fail to work freely for several reasons, which may significantly reduce the competitiveness of transit, because it is no different from self-driving. However, it may not be highly correlated with a low level of travel happiness. After all, if one does not need to run to work, one will be more likely to be happy.

Conclusion

The results obtained from previous empirical studies that used multiple statistical techniques are often symmetric. However, focusing on symmetric relationships only may be misleading because such effects do not apply to all cases. Thus, this paper establishes a theoretical model and draws on complexity and configuration theories to capture what causal patterns of factors lead to a high level of satisfaction and happiness with transit, and a high degree of transit loyalty. To the best of the authors’ knowledge, this research is the first of its kind to study the impact of the use of smartphone apps and hedonic motivation on target variables. The results indicate that travel satisfaction and happiness are sufficient but unnecessary conditions for transit loyalty. If travelers use smartphones during transit and experience hedonic motivation, transit satisfaction, happiness, and loyalty will increase.

Through the analysis of the necessity of the result variables, there are no necessary conditions for a high/medium/low degree of transit loyalty. This implies that even though travelers have a low level of one of the causal conditions mentioned, for example, low frequency of using entertainment apps during travel, the degree of loyalty may still be high for other conditions, for example, high frequency of using e-ticketing or information retrieval apps. The results of the necessity analysis indicate that use of smartphone apps during transit and hedonic motivation may not have a significant or direct influence on the degree of transit loyalty.

Based on the configuration analysis, to explain a high degree of transit loyalty, fsQCA identifies eight different solutions for the combination of the use of smartphone apps and hedonic motivation that are divided into three types: (a) mobile working oriented; (b) hedonic motivation oriented; and (c) digital management oriented. Although a single variable may not be necessary for a high degree of transit loyalty, combining multiple variables can contribute to this being the case. The results show that using smartphones to work remotely or pay for the fare greatly enhances the competitiveness of transit. It is worth noting that in our research sample, there is a class of travelers who are very willing to use transit but whose enjoyment of using this mode of transport involves not using smartphone apps. We also conclude that when smartphone apps can improve convenience and realize the digital management of transit travel (such as e-ticketing and travel information retrieval), travelers’ willingness to use transit will significantly increase. In addition, we find that entertainment is the absent core condition rather than the core condition for a high degree of loyalty, indicating that using entertainment apps may not improve transit loyalty. However, if there is no entertainment to pass the time during a long and perhaps uncomfortable journey, this must be a disadvantage. For a low level of travel satisfaction and a low degree of loyalty, the use of mobile working apps appears as an absent core condition, whereas it is a peripheral condition for a low level of travel happiness, indicating that the low frequency of mobile working may not be highly correlated with a low level of travel happiness. If transit travelers need to use mobile working and certain other apps frequently, their satisfaction with transit and loyalty to this mode of transportation may be relatively higher. Compared with self-driving, transit travel means travelers’ hands are free, so they can make full use of their travel time. However, this may not necessarily improve happiness, and if passengers still need to deal with work during travel, the level of travel happiness may not be very high. This phenomenon confirms the finding that using mobile working apps appears as an absent core condition for a low level of satisfaction with transit travel and a low degree of loyalty to this mode of transportation, whereas it is a peripheral condition for a low level of travel happiness.

The research indicates that travel satisfaction and happiness are sufficient but unnecessary conditions for transit loyalty. Even if passengers’ level of satisfaction or happiness with transit is not high, they will still be willing to choose this mode of transport because of other factors such as a lower ticket price, high convenience, and availability of travel time. However, if passengers’ level of satisfaction or happiness is high, they may strongly intend to use transit again. Future research can use travel satisfaction and happiness as moderating variables to explore factors that influence transit loyalty further.

Practical Implications

Based on the findings in this paper, some implications can be made at multiple levels. First, compared with the era when the use of smartphone apps during transit was not popular, and travelers had few options other than sleeping, reading the newspaper, or chatting with companions during their journeys, nowadays, there is considerable potential for transit to become enjoyable and productive if passengers use different apps while traveling, for example, Baidu Maps, games and music apps, or even apps that enable mobile working, including dealing with emails. According to the outcomes of our research, using smartphones during transit travel can leave travelers with a better perception of their satisfaction and happiness, thereby increasing their intention of using transit again. Thus, app developers could cooperate with transit operators to develop more apps (especially for mobile working and e-ticketing) so that transit travel can embrace digital development, and travelers can make full use of their travel time to handle tasks in hand. In addition, other stakeholders would benefit from an improvement in the digital management of transit. For example, transit operators could use big data to carry out integrated dispatching and coordination and use a smartphone app as the means by which to launch a readily available transit arrival and transfer information service for passengers, thus greatly improving the efficiency of transit and also the likelihood that citizens will choose it as a mode of transportation. In addition, operators could pay more attention to the development and management of digital transit cards so passengers could pay their fares more easily, thereby avoiding the problem of having to carry physical cards or cash and maybe forgetting to do so. What ultimately achieves transit loyalty is the infrastructure that provides data connectivity underground, because without it, the use of smartphone apps during transit would not be possible. Charging equipment is also a factor influencing transit loyalty. Although charging sockets have been installed under the seats of trains in Shanghai, they have not yet been installed on subway trains or buses. To allow long-distance travelers to use smartphones seamlessly, it would be worth considering installing sockets or other charging devices on subway trains and buses too.

The sufficiency analysis indicates that hedonic motivation plays an important role in improving satisfaction, happiness, and loyalty. Therefore, in addition to using smartphones, the improvement of infrastructure, the installation of basic relaxation and entertainment equipment in subway cars, and encouraging people to travel in groups, could all give transit travelers a more hedonic impression, thus increasing their willingness to continue to use transit. Most importantly, perceived satisfaction and happiness have a sufficient impact on loyalty. That is, if passengers have a high degree of loyalty to transit, their level of satisfaction and happiness with this mode of transport will also be high. In this regard, in addition to improving service quality and facilities, transit operators should consider a range of other options available to them for creating a more eco-friendly and user-friendly travel environment, thereby encouraging greater use of transit.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: Jiatao Chen, Yang Zhang, Yang Wu, Lizhen Zhou; data collection: Jiatao Chen, Yang Wu; analysis and interpretation of results: Jiatao Chen, Yang Wu; draft manuscript preparation: Jiatao Chen, Yongping Zhang, Yang Zhang, Yang Wu, Lizhen Zhou. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Yang Wu

Jiatao Chen

Yongping Zhang

Lizhen Zhou

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