The Role of Technophilia and User Goals in the Intention to Use a Mobility Management Travel App

Abstract

The ubiquitous use of mobile devices along with the amount of traffic, transportation services, and travel pattern data available has led to the emergence and deployment of smartphone applications for providing information about personal travel management. Several of these travel apps are aimed at voluntary travel behavior change (VTBC) to support and increase sustainable mobility, and have led to the development of research to investigate their influence on travel behavior. Here, the aim is to study the role of technophilia and goal-framing theory in the intention to adopt and situationally use a prospective VTBC travel app. A Structural Equation Model is developed with the aim of empirically testing a sample of 971 respondents collected in two suburban corridors in the Lisbon Metropolitan Area. The results support that goal-framing theory is important for explaining the adoption of VTBC travel apps. Gain and normative motives are more relevant than hedonic motives, pointing to the importance of their tangible benefits. Frequent car users may benefit from VTBC travel apps in terms of encouraging behavioral changes, supporting sustainable mobility management solutions. The results also outline the importance of technophilia and the current use of travel apps in influencing the intention to use VTBC apps.

1. Introduction

The ubiquitous use of smartphones and the availability of huge amounts of data collected from transportation operators, infrastructure managers, and cities has led to the emergence and deployment of smartphone applications aimed at providing information to transportation users for their travel management. The emergence of these travel apps, several of which are aimed at voluntary travel behavior change (VTBC), has led to the development of research for the purpose of investigating their influence on travel behavior.

Recent research work has explored the potential of VTBC apps to contribute to changing travel behavior, particularly the possibility of increasing the use of public transportation services, as in [1]. Particularly important here is the work of Dastjerdi et al. [2], which used a Structural Equation Model (SEM) to study the role of user goals, technophile attitudes, and community trust to explain the use of a multimodal travel app. This research did have several restrictions which penalized its generalizability: the surveyed population was mostly made up of university students and staff members, and the app was not implemented in practice. Moreover, the results are dependent on the sustainability values and mobility patterns of the population of Copenhagen. In this research, the goal was to extend some aspects of this previous research, namely the role of technophilia and goal-framing theory (GFT), with the intention to adopt a prospective VTBC travel app. GFT is used here to explore interdisciplinarity issues such as the public acceptance of sustainable behavior policies and measures [3] and the adoption of new travel information technologies [2,4,5]. With the aim of contributing to a generalization of the findings of Dastjerdi et al. [2], we have applied a similar set of hypotheses to a markedly different context consisting of two suburban corridors in a southern European metropolis with a different transportation system and an incipient bicycle culture. Also, with the aim of increasing generalizability, the data used for this paper come from a sample considered representative of the population of two suburban corridors, instead of from a very specific socioeconomic group as was the case in Dastjerdi et al. [2]. We have also considered the role of the current use of existing travel apps, which can mediate the relationship between technophilia and use intention and which was not included in the work of Dastjerdi et al. [2]. Furthermore, this work contributes to the body of knowledge by exploring the importance of Lindenberg’s goal-framing theory [6,7] as a motivator for the intention to adopt mobility management travel apps. The theory maintains that all three types of goals (hedonic, normative, and gain) are behavioral drivers [6,7].

This paper is organized as follows. The following section presents a brief review of the literature, with a strong focus on aspects related to technophilia and goal-framing theory. The Section 3 describes the case study. Section 4 presents the modeling method used in the research, SEM, while in Section 5 the model results are presented and discussed. The paper ends with the conclusions.

2. Literature Review

The promotion of a more sustainable mobility behavior has led to the adoption of VTBC programs and schemes. Generally speaking, these schemes are designed to increase the awareness of the range of transportation alternatives to the car [8]. Since the 1990s, VTBC programs have been implemented with varying degrees of success in North America, Europe, and Australia [9]. More recently, social marketing strategies previously used to promote healthier habits have also been used to encourage more sustainable and pro-environmental behavior [10].

In this context, the use of Advanced Traveler Information Systems (ATIS) is a powerful mobility management tool for urban areas. These information apps can reach a wide audience and, from a behavioral perspective, be a powerful tool. Given that the awareness of the problem is through perceived responsibility, behavioral control and social norms will affect behavioral intentions and actions [11,12]. The behavioral change elements induced by ATIS are motivation, ability, and triggers for behavioral change [4].

A range of ATIS for mobility management is presented by Gärling et al. [13], including navigation apps providing drivers with route alternatives and/or alerts, and the provision of real-time information on public transportation services. These systems can potentially improve the travel experience and increase the mobility of users, as they allow individuals to make better-informed travel decisions. They encourage people to make rational choices based on the associated costs and benefits [14] and satisfy higher-order emotional needs of self-actualization, regarded as important for long-term behavioral shifts [15]. Travel mobile apps incorporating persuasive strategies are mostly based on Fogg’s framework [16], where the design of the system seeks to change behavior without resorting to coercion or deception. The Fogg behavior model focuses on persuasive technology to influence people’s behavioral change through three elements that must occur simultaneously—motivation, ability, and triggers [16]. Smartphones are currently the main tools in the dissemination and evolution of persuasive technology [4].

The most recent ATIS include user-based alerts, prescriptive advice (e.g., route alternatives), reflective memory (e.g., saving trips and locations), and persuasive strategies (e.g., carbon emission scores and interaction with social networks) [17,18]. There is wide agreement that satisfying users’ needs is fundamental for mobility management travel apps aimed at encouraging VTBC [4,19,20,21]. Moreover, the widespread utilization of the internet, smartphones, and mobile travel apps provides an opportunity for producing critical mass for VTBC-based ATIS [21].

Field experiments, from an ATIS perspective, provide evidence that offering opportunities for communication and collaboration across users, information sharing, and social networking [22,23] is important in influencing users to change their travel behavior [19,24,25,26]. Nevertheless, these new solutions may give rise to concerns in terms of technology, privacy, reliability [27], and unintended externalities [15].

From the literature review made by Andersson et al. [19] on the effectiveness of smartphone apps in supporting behavioral change in the field of transportation, it is possible to say that user customization, relevant and contextualized information and feedback, commitment, and an appealing design are significant factors for encouraging behavioral change. Also, Khoo and Asitha [28] showed through a stated preference survey that real-time traffic information is one of the most desired features of smartphone traffic information apps. In the short term, gamification could also increase engagement and possibly promote active travel modes [29].

However, ATIS influence is highly dependent on how users interact with the system. This process is not distinctly technological but has a social dimension, which requires a socio-technical evaluation [21]. Adopting VTBC-based travel apps is not exclusively determined by their functional utility, but also by their ability to satisfy emotional needs [30]. Furthermore, the results obtained by Dastjerdi et al. [2] show that technophiles are an important target group for VBTC-based travel apps.

2.1. Technophilia

The success of these innovative technologies (e.g., ATIS) may be affected by consumer attitudes and psychological factors. Technophilia encompasses the aspects of familiarity, interest, and the ability to access the communication and information technologies managed by ATIS [31]. Accordingly, in analyzing these factors, we focused on technophilia, which relates to a person’s openness to, interest in, and ability to deal with innovative technologies, i.e., their attitude toward technology [32].

Technophilia is also an important factor in the willingness to adopt ATIS [31]. From the ABC model of attitudes [33], the technophile attitude consists of three components, which are affective (e.g., satisfaction), behavioral (e.g., frequency of use), and cognitive (e.g., technology self-efficacy) [31]. One of the advantages of technophilia is that it allows for a more precise segmentation of users than demographic variables [31].

Previous literature regarding this topic supports the positive effect of technology self-efficacy in predicting the use of mobile applications [34,35,36] and both the direct [31] and indirect [36] effects of technology affinity on user attitude and behavior. Technophilia is a strong predictor of a driver of change’s intention to adopt navigation apps [37]. Dastjerdi et al. [2] show that a stronger technophile attitude has a positive influence over user motives and on the intention to use VTBC-based travel apps. Also, behavioral intention to use a travel app is positively affected by functional and psychological motives; however, their effects are situation-based [2]. Technophilia is more pronounced among men, younger people, individuals with higher education, and persons who frequently use the Internet for travel information or who frequently use an in-car navigation system [31].

2.2. Goal-Framing Theory

In an environmental context, goal-framing theory (GFT) presents the argument that, in all situations, individuals want to achieve a goal that incorporates certain types of motives. These behavioral motives can be grouped into three overarching categories of goals, in accordance with the core desires and needs that they satisfy: gain, hedonic, and normative motives [6,7]. The theory suggests that the three areas: gain, hedonic, and normative, along with their respective motives, are simultaneously present and active. Nevertheless, only one goal frame becomes dominant at any given time, which determines the way individuals interpret and frame the situation and how they act, leaving the motives from the other two areas still present but in the background. The other goals may increase the strength of the focal goal if they are compatible or reduce that strength if they conflict with the focal goal [6].

Goal-framing theory thus predicts that people face a trade-off between doing the right thing (normative goal), saving resources (gain goal), and feeling good (hedonic goal) [6]. de Canto et al. [38] has pointed out that GFT has two major strengths when compared with other theories (e.g., planned behavior, behavioral perspective model, or elaboration likelihood model): Firstly, it can explain contextual variability in goals, and secondly, it explicitly distinguishes different types of goals and their interactions. The results of Zhu et al. [35] endorse the idea that gain motives (e.g., time and monetary savings) together with hedonic motives (i.e., enjoyment and social image improvement) significantly influence the overall perceived value of ridesharing applications. These two motives (gain and hedonic) were also identified in recent studies as important antecedents of the intention to adopt mobile devices [39,40,41]. When it comes to pro-environmental behavior, people are more sensitive to the hedonic consequences of their actions [42]. Thus, this harms pro-environmental behavior as such actions are costlier in time or effort and people tend to choose the most convenient option [43]. However, travel behavior accepts combinations of the three categories of goals [44].

In the context of transportation, recent literature explored the GFT with a view to explaining individual environmentally responsible decision-making [43,45,46]. Timmer et al. [1] argue that gain and hedonic goals were not found to affect the intention to switch. However, normative goals strongly influence the switch to multimodal mobility.

3. Conceptual Model and Hypotheses

Based on the concepts explored above, we have developed a conceptual model to better explain the intention to adopt a VTBC-based travel app. This model is based on the concept of technophilia, together with the goal-framing theory from a user perspective. It also includes the current use of travel apps as a mediator between technophilia and situational use. The research hypothesis framing this conceptual model is the following, and is presented in Figure 1:

• H (hypothesis) 1: Technophilia increases the intention to use an app;
• H2: Goal-frame theory relates positively to technophilia;
• H3: Goal-frame theory constructs (gain, hedonic, and normative motives) motivate the intention to use aVTBC travel app;
• H4: The current use of travel apps is a mediator in the relationship between technophilia and use intention.

H1 to H3 are derived from the work of Dastjerdi et al. [2], whereas H4 is partly supported by the work of Seebauer et al. [31], who found an association between technophilia and the use of ICT.

4. Case Study

A new real-time multimodal mobility app (U-Move App) is currently being researched for the Lisbon Metropolitan Area (LMA). The U-Move app is currently only a conceptual application. It is meant to be designed so that individuals can plan, book, and pay for a complete journey in the LMA. It also seeks to include multimodal real-time information, multi-criteria route planning based on time and cost, multimodal choice combinations, ridesharing opportunities, active mobility opportunities, and easy payment. This new travel app aims to provide users with information about the CO₂ emissions that are produced/saved by taking different travel options and the trip’s caloric expenditure by using active modes. Users can set mobility challenges and post them on social media, as well as share their travel information, e.g., CO₂ emissions saved and calories burned.

Survey Design and Sample Characteristics

A technology-use preference survey was designed to collect data for analysis while translating a behavioral framework into a concrete framework that can be empirically validated. We gathered data in two suburban corridors in the LMA, in the Almada, Seixal, Oeiras, and Cascais municipalities. One corridor consisted of the municipalities of Almada and Seixal on the south bank of the Tagus estuary, the other was made up of Oeiras and Cascais, located west of Lisbon. The sample was recruited using two complementary methods: first of all, flyers, containing a QR code with the survey, were distributed in heavy public transportation (trains and ferries) stations (10,000) and major shopping malls (5000) along the two corridors. The number of flyers that were distributed was proportional to the transported passengers in each station and the leasable floor area in the shopping malls. This data collection method and the flyer format have been used before and reported in the literature [47]. A total of 576 completed questionnaires were obtained using this method. The resulting response rate can be considered very low, about 3.8%. To complement these observations, a market research company (using an online panel) was hired to obtain 400 valid survey responses from a sample proportional to the age and gender distribution of the four suburban municipalities in both corridors. After data cleaning, the number of observations used here was 971.

The survey elicited several groups of variables. The first group focused on the perceived values and attitudes that motivate travelers to use real-time multimodal travel planner apps as well as technology-related self-concepts of technophilia. The second group of variables comprised the likelihood of using the app measured on a 5-point Likert scale ranging from highly unlikely to highly likely. The third group characterized the current travel patterns (e.g., usual travel mode and use of travel information). The last group of variables consisted of socioeconomic information (e.g., income, age, gender, education levels, place of residence, and status within the household). The statements of attitudinal variables (e.g., technophilia) were measured using a 7-point Likert scale ranging from strongly disagree to strongly agree. Technophilia was measured using statements reflecting emotional and cognitive attitudes toward the use of smartphone apps. The statements were inspired by the work of Seebauer et al. [31], who researched the technophilia attribute in the context of online travel apps. In relation to use intention, respondents were asked to rate the likelihood of using the app for their daily commute and specific travel purposes.

Individual characteristics included socioeconomic variables, travel habits, past travel experiences, and information use habits. For travel habits, the frequency of trips using cars, motorcycles, public transport, active modes, and shared modes was included. The frequency was measured on a 6-point Likert scale with never/rarely and daily as the extreme limits of the scale. The respondents were also asked about their perceived travel time using the transportation mode most frequently used, as well as situational attributes, namely the commuting distance. Travel information use habits cover the frequency of using travel information applications. The frequency of information use was measured on a 6-point Likert scale ranging from never to always. The survey was administered from mid-May to mid-August 2022. The sample gender distribution, mean age, and education levels are comparable to the population characteristics of the two corridors (

Table 1. Sample characteristics.

Variable	Categories
Gender	Male	Female	Other
Sample	46.90%	52.40%	0.70%
Corridors	47.20%	52.80%	0.00%
Age	Mean	Std deviation
Sample	37.58	13.78
Corridors	40.87
Education	No schooling	Primary education	Secondary education	Higher education	Others
Sample	0.00%	3.50%	43.00%	53.50%	0.00%
Corridors	3.94%	38.80%	27.00%	30.30%	0.00%
Household Gross Monthly Income (EUR)	Under 1450	1450–2900	2901–5700	Over 5700
	0.70%	34.80%	45.20%	19.30%
Frequency of use:	Never	Rarely	Occasionally	Frequently	Very frequently	Daily
Car	20.39%	6.28%	11.64%	16.48%	19.05%	26.16%
Rail	5.66%	15.65%	12.67%	9.58%	19.98%	36.46%
Driver’s license	No	Yes
	20.40%	79.60%

). The sample showed that, except for education levels, for which the sample overrepresents individuals with higher qualifications, both gender distribution and mean age are aligned with the two corridors’ values. The bias towards more educated people could be explained by the fact that the survey deployment was web-based, either through the flyers or the online panel of the market research company.

5. Methods

We built a structural equation model (SEM) to test the conceptual model and the hypothesis developed here. SEM is a popular modeling technique combining two types of statistical methods: factor analysis and a simultaneous equation model [48]. A complete SEM includes both a measurement submodel and a structural submodel. The measurement submodel links indicators to latent constructs (similar to confirmatory factor analysis), and the structural submodel incorporates the relationships among different latent constructs and between these and the observed variables. The SEM used here includes a structural submodel (Equation (1)) and a measurement submodel (Equation (2)).

$$\eta =B\eta +\Gamma x+\xi ,$$

(1)

$$y={\Lambda }_y\eta +\epsilon$$

(2)

where

η is a vector (m*1) of the m latent endogenous variables;

B is a matrix (m*m) of coefficients of endogenous variables;

Γ is a matrix (m*n) of coefficients of exogenous variables;

x is a vector (n*1) of the n observed exogenous variables;

ζ is a vector (m*1) of errors from the structural relationship;

y is a vector (p*1) of the p observed endogenous variables;

Λy is a matrix (p*m) of regression coefficients of y on η;

ε is a vector (p*1) of measurement and errors on y.

The names and labels of the variables used in the measurement submodel are presented in

Table 2. Measurement model.

Latent Construct	Statement	Standardized Coefficient	p-Value
Technophilia	I like to install new apps	0.756	0.000
	I regularly use apps for payments, reservations, etc.	0.682	0.000
	I am enthusiastic about GPS and travel apps (e.g., Google Maps, Moovit, etc.)	0.832	0.000
	I think it is exciting to try new apps	0.870	0.000
Gain Motives	Estimate my travel time	0.823	0.000
	Be on time	0.834	0.000
	Be faster and more efficient in my trips	0.859	0.000
	Receive personalized information about my most frequent trips	0.832	0.000
	Obtain information about the total costs of all transportation alternatives	0.811	0.000
	Obtain real-time information about the occupancy level of public transportation	0.761	0.000
	Obtain information about alternative travel routes when there is a disruption in the transportation system	0.856	0.000
	Reduce difficulties in obtaining travel information	0.866	0.000
	Feel more in control of my daily mobility	0.888	0.000
	Have more freedom of choice in my daily mobility	0.900	0.000
	Consider more transportation options in reaching my destination	0.881	0.000
Hedonic Motives	Accumulate points and be rewarded with a bonus for eco-friendly behavior	0.800	0.000
	Monitor the number of calories burnt while traveling	0.779	0.000
	Share my saved CO2 emission levels on social media	0.792	0.000
	Make my trips more fun	0.823	0.000
	Entertain myself more often on my trips	0.824	0.000
	Suffer less stress when traveling	0.883	0.000
Normative Motives	Use more shared modes	0.752	0.000
	Use more public transportation	0.836	0.000
	Choose healthier alternatives for my trips	0.891	0.000
	Use bicycles more often	0.672	0.000
	Know the CO2 emission for each trip I make	0.774	0.000
	Reduce my CO2 emissions	0.815	0.000
Situational Use	Likelihood of using the app on occasional trips (leisure, health, etc.)	0.904	0.000
	Likelihood of using the app on spontaneous, unscheduled trips	0.876	0.000
	Likelihood of using the app to go to unknown places	0.803	0.000
	Likelihood of using the app on trips where I have to change modes or make transfers	0.899	0.000
	Likelihood of using the app to receive alerts about accidents, construction sites, etc.	0.806	0.000
	Likelihood of using the app to check public transportation schedules	0.809	0.000

, and the variables used in the structural submodel are presented in

Table 3. Standardized effects from the exogenous variables.

Endogenous Variables/ Latent Constructs	Exogenous Variables	Standardized Coefficient	p-Value
Technophilia	Frequency of car use (as a driver)	0.132	0.011
	Frequency of rail use (subway, light rail or heavy rail)	0.143	0.000
Normative Motives	Household income	−0.116	0.002
	Education level	−0.148	0.000
	Frequency of rail use (subway, light rail or heavy rail)	0.167	0.000
Hedonic Motives	Gender (1 = male)	0.076	0.022
	Owns driver’s license (1 = yes)	−0.130	0.019
	Frequency of car use (as a driver)	0.079	0.013
Gain Motives	Gender (1 = male)	0.082	0.006
Situational Use Intention	Frequency of rail use (subway, light rail or heavy rail)	0.094	0.000
Current Use of Travel Apps	Age	−0.238	0.000
Adoption Intention	Education level	0.102	0.002
	Frequency of rail use (subway, light rail or heavy rail)	0.151	0.000

. The model estimated herein includes several endogenous variables that are ordinal (e.g., adoption intention). At the same time, the sample size is relatively small. These two conditions mean that the Mean and Variance Adjusted Weighted Least Squares (WLSMV) estimation method should be used [49]. To evaluate the model and goodness of fit, we have used the Comparable Fit Index (CFI) and the absolute Root Mean Square of Approximation (RMSEA). Examining the total and indirect effects allowed us to identify mediation and moderation effects as well as self-defeating variables, on account of contrary direct and indirect effects. The total effects are the sum of both direct and indirect effects and could be statistically non-significant if the contrary direct and indirect effects cancel each other out. The indirect effects are the product of the direct effects of the different mediating variables in each structural path. For a more in-depth explanation of SEM, see Bollen [50].

6. Results and Discussion

The model fit indicators are indicative of a reasonable fit. The CFI is 0.968 and the RMSEA is 0.051. The results obtained are presented as follows. First of all, the results from the measurement submodel are presented, followed by the structural relationships between exogenous socioeconomic and behavioral variables and the path diagram between the endogenous variables (Figure 2). The discussion of the results ends with presentation of the total and indirect effects of all variables and latent constructs as to the Situational Use of U-Move (our proposed VTBC travel app).

The standardized coefficients in the measurement submodel (Table 2) are all statistically significant and are in line with previous exploratory factor analysis and the factors extracted using similar Likert scale questions [2,30]. The first latent construct, Technophilia, has to do with self-concepts about technology and its use. The following three latent constructs have to do with goal-framing theory and relate to the gain, hedonic, and normative motives. Normative Motives in particular have relevant sustainability implications, as seen by the coefficients of the statements on CO₂ emissions. Finally, the final latent construct relates to the prospective perception of the purposes for which the travel app will be used and is called Situational Use. As all the statements are positive towards the concepts associated with the different latent variables, their coefficients are, as expected, positive.

In the SEM, several variables reflecting socioeconomic and mobility-related characteristics are used as exogenous variables. These include age, gender, education level, and household income as socioeconomic characteristics, along with the frequency of car (as a driver) and rail-based transportation use, as well as the possession of a driver’s license as mobility-related variables, which show the respondent’s current behavior. The model coefficients, representing the direct effects of these variables on the endogenous latent constructs presented above, and two other endogenous variables capturing the intention to adopt the U-Move app and the current use of travel apps, are presented in Table 3.

Respondents with a higher frequency of car and rail-based transportation use are more likely to be technophiles. A higher income and higher education levels reduce the importance of normative motives. In contrast, more frequent use of rail-based transportation is associated with a higher importance of normative motives. Men who are frequent drivers of cars have stronger hedonic motives. Gain motives are directly and positively associated with being a male. Situational use and adoption intention are positively associated with the frequency of use of rail-based transportation. The intention to adopt is also positively affected by education level. Current Use of Travel Apps is negatively associated with age, which is in line with the fact that younger people are more likely to use technology more intensely.

Figure 1 presents the relationships between the endogenous variables. These are, to a large extent, in line with previously carried out research [2,30], and support the conceptual model and research hypothesis. All depicted relationships are statistically significant (α = 5%). Technophilia is, in this case, the latent variable that is the primary cause of all the others. Technophilia has a positive influence on all the goal-framing motives (gain, hedonic, and normative). Being a technophile also increases the intention to adopt the U-Move app. Normative, hedonic, and gain motives are all interrelated. Gain and normative motives directly influence both Adoption Intention and Situational UseIntention. The current use of travel apps is positively influenced by Technophilia and also has a positive influence on Situational UseIntention, thus mediating the effects of Technophilia.

The total and indirect standardized effects (in

Table 4. Total and indirect effects on Situational Use Intention.

Standardized Effects on Situational Use Intention from		Estimate	p-Value
Frequency of car use (as a driver)	Total	0.084	0.004
	Indirect	0.084	0.004
Frequency of rail use (subway, light rail or heavy rail)	Total	0.306	0.000
	Indirect	0.212	0.000
Owns driver’s license (1 = yes)	Total	−0.024	0.042
	Indirect	−0.024	0.042
Gender (1 = Male)	Total	0.060	0.003
	Indirect	0.060	0.003
Age	Total	−0.017	0.011
	Indirect	−0.017	0.011
Education level	Total	−0.046	0.080
	Indirect	−0.046	0.080
Income	Total	−0.062	0.003
	Indirect	−0.062	0.003
Technophilia	Total	0.526	0.000
	Indirect	0.526	0.000
Normative Motives	Total	0.532	0.000
	Indirect	0.371	0.000
Gain Motives	Total	0.552	0.000
	Indirect	0.123	0.000
Hedonic Motives	Total	0.188	0.000
	Indirect	0.188	0.000
Current Use of Travel Apps	Total	0.071	0.002
	Indirect	0.000	1.000

below) give a clearer idea of the role and relative importance of the different variables, both endogenous and exogenous, in the adoption of travel apps. The results showed that frequent and very frequent users of rail-based transportation and car drivers are more likely to have increased situational use. However, the magnitude of the effects from the frequency of rail-based transportation is way larger. These effects are in accordance with the idea that rail-based public transportation tends to be associated with more complex trips, potentially involving mode transfers. Rail-based transportation is not available everywhere and thus it is common that its use generally involves the need to use access and/or egress modes. A travel app is more useful in more complex trips involving several transportation modes. Men and younger people are more likely also to adopt travel apps. Income and education levels have a negative influence, although the total effect of the educational level is not statistically significant. This is due to the negative effects that income has on Normative Motives and the contrasting effects that education level has on adoption intention and Normative Motives. Normative and gain motives are the most important latent constructs of the goal-framing theory, impacting the situational use of travel apps. Even if only indirectly, Technophilia is also one of the most important predictors of the situational use of travel apps, considering that Technophilia impacts gain, hedonic, and normative motives as well as the current use of travel apps and Adoption Intention. The magnitude of its standardized total effects is slightly smaller than that for the normative and gain motives. The Current Use of Travel Apps revealed itself as an important mediator of the effects of Technophilia on Situational Use Intention.

Finally, the Current Use of Travel Apps also impacts Situational Use Intention. The strong influence of normative and gain motives on both Adoption Intention (direct effects) and Situational Use Intention (total effects) has significant implications for the design of the U-Move app, indicating that its features should be tailored to address user concerns that have to do with these motives, namely making trips more efficient and healthier and thus contributing to an increase in the use of public transportation by increasing the efficiency thereof and highlighting its beneficial effects on health.

7. Conclusions

This study analyzes the effects of technophilia and goal-framing theory on the pronounced intention to use a proposed travel app. A SEM was developed to empirically test a sample of 971 respondents gathered in two suburban corridors in the Lisbon Metropolitan Area. Both corridors are served by rail transportation, but one is on the south bank of the river Tejo, thus being separated from Lisbon by a relevant geographical barrier (although there is a direct rail connection to Lisbon). The results support the idea that goal-framing theory is important for explaining the adoption of VTBC travel apps. Gain and normative motives are much more important than hedonic motives, indicating that people would adopt these apps, and possibly change their behavior, if they are valuable for them; this is in terms of tangible benefits that have to do with trip efficiency, namely saving time, money, and traveling more comfortably, which are associated with the Gain Motives, and reducing their carbon footprint and choosing healthier transportation alternatives, which have more to do with normative motives and have significant implications in terms of sustainability. These conclusions are in accordance with Dastjerdi [2], which highlight the strong role motives have in individual self-interest in the adoption of VTBC travel apps. The results also support the importance of technophilia and the current use of travel apps to influence the intention to use VTBC apps and the mobility patterns that are associated with higher levels of technophilia.

With regard to socioeconomic and behavioral aspects, as expected, younger men are more likely to adopt VTBC apps as are, more importantly, frequent users of rail-based transportation, indicating that mobility patterns associated with more complex trips (with a higher likelihood of access and egress modes and transfers) might induce a strong intention to use these technological tools, particularly if individuals perceive them as increasing trip efficiency. The implications of these results are strongly related to possible marketing actions aimed at promoting the use of the U-Move app, as well as insights in terms of the features that it is important that it possesses. In terms of the target group to which this app should be marketed, this includes both rail users and car users. For the latter group, it should be noted that the expected market penetration would be lower, as evidenced by the total effects of these two variables on Situational Use Intention (Table 4). For frequent car users, the U-Move app could be useful in inducing behavioral changes, particularly by providing individuals with accurate, reliable, and important information which could increase trip efficiency and contribute to persuasive mobility management solutions [2] aimed at increasing sustainability. This means that for this segment of potential users, the U-Move app should also be aimed at behavioral change and should encourage users to make more sustainable travel choices. For frequent rail users, it could contribute to improving their experience using the system, thus increasing their loyalty to public transportation. From the point of view of the motives to use the app, given that gain and normative motives are by far the most important, both the development of the app and its marketing campaign should focus on the functionalities related to these motives. Accordingly, the provision of accurate travel time estimates and personal relevant information in real-time about transport alternatives and system disruptions are fundamental features for increasing the attractiveness of VTBC apps.