PLATFORM-BASED TRANSPORTATION COMPANY IN INDONESIA: PERSPECTIVE BRAND SWITCHING BEHAVIOUR ON GENERATION Z

Internet growth and the expansion of platform-based transportation companies in Indonesia are co-occurring. This study attempts to identify the factors that encourage platform-based transportation companies' customers to switch to competing platforms. This quantitative research method employed the Structure Equation Model (SEM) analysis method implemented in Warp PLS 5.0. This study's population comprises platform-based transportation users in Jakarta, the Indonesian capital. Through purposeful sampling, 200 research samples from Jakarta Generation Z were chosen. According to the results of this study, perceived price and experience dissatisfaction influence the Generation Z brand transition into platform-based transportation. In the interim, electronic service quality does not influence Generation Z brand-switching behavior in Indonesia. The relationship between perceived price and experience dissatisfaction with switching behavior is mediated by switching intention. This research contributes to transportation-based platform companies to reduce brand switching, especially among Generation Z.


INTRODUCTION
The development of digital technology influences consumer behavior related to purchasing decisions for goods and services.The Android phone is quite crucial in today's world when nearly no human life technology is not tied to electronic devices unlike the smartphone (Adibowo et al., 2023).Utilizing mobile apps makes it simpler for organizations to acquire digital services (Rahayu et al., 2023).This utilization creates chances for entrepreneurs and facilitates customer decision-making over product purchases (Chen et al., 2017).The emergence of platform-based transportation in Indonesia is one of the impacts of the development of the Internet.In 2011, the emergence of a platform-based transportation company was a pioneer of online transportation in Indonesia.However, this service started using applications on Android and iOS in 2015 and managed to attract the public's attention.Global changes in communication and information technologies have affected people's behavior patterns.An endless (borderless) universe has emerged due to the development of information technology and essentials, swiftly changing social, cultural, economic, and legal trends.Platform-based transportation switching patterns have links to the safety of the platform itself.It seems necessary to reconsider the current mobility and transportation system unavoidable, given the problems with global sustainability and the increase in urbanization (Veer et al., 2023).Traffic levels increase due to rapid worldwide urbanization (Utriainen & Pollanen, 2018), which may adversely impact day-to-day life (Mulley, 2017).
The platform-based industry urgently needs to implement cyber law enforcement.In these social dynamics today, the responsive legal theory states that law is interpreted as an instrument to serve human needs, especially for various social changes and social justice in society (Nonet et al., 2008).Arrangements regarding platform-based transportation in Indonesia are partially regulated in several regulations, including the Electronic Information and Transaction Law (UU ITE), Burgerlijke Wetboek (BW), and the Road Traffic and Transportation Law (UU LLAJ).The World Economic Forum reports that access to technology enhances the quality of life and speeds up growth at all levels, including those related to health (mHealth), education (eLearning), and finance (mobile financial services).Internet users are categorized into digital immigrants and natives (Prensky, 2001).Digital natives are the generation or people who were born before technology was invented.They can use technology because it comes naturally, like breathing (Tapscott, 2013).Digital immigrants, on the other hand, are generations or people born after the technology was developed.These generations quickly embraced the Internet in various ways after being attracted by it (Prensky, 2001).
Generation Z is the first actual internet generation, according to Grail (2011), whose traits explain this.In contrast to Generation Y, which is still adjusting to the Internet's technical transition, Generation Z was born while technology was already widely available (Grail, R., 2011).Technology, adaptability, intelligence, and tolerance for cultural variety all contribute to this generation's charming disposition.They network online and connect to everyone on the earth as well.Nonetheless, given their need for instantaneous communication and waning awareness of the value of privacy, this generation may be defined in some ways by their continual live uploads to social media.Generation Z has the most significant impact on their community compared to prior generations because of its exposure to various online resources.Instead of being silent, this generation will write on social media about their positive or negative experiences with something (Sladek & Grabinger, 2014).

LITERATURE REVIEW
Research on platform-based transportation has also been carried out in other countries, such as the Netherlands, on the Maas application.A smartphone must be owned and used regularly to properly use Mobility as a Service (MaaS) applications.One limitation of the long-term use of MaaS could be the exclusion of transit users with lesser digital literacy (Alyavina E et al., 2022).The resources a person has at their disposal to use MaaS, or the UTAUT-concept Model of "Facilitating Conditions," is thus also thought to affect the desire to use MaaS.There are various perceived risks of using MaaS, such as worries about data privacy and concerns about receiving dependable, safe, and trustworthy service as opposed to those that benefit the service provider, which could also potentially affect a person's intention to use MaaS (Polydoropoulou et al., 2020;Alyavina et al., 2022) regarding possible health risks.Bansal et al. (2005) created a paradigm to examine migration behavior while defining switching behavior as a consumer replacing or swapping current service providers.Changing behavior is necessary to keep customers (Bansal et al., 2005).
According to earlier studies, consumer comprehension and retention strategy are tightly associated (Hsieh et al, 2012;Kuo R. Z., 2020;Tang & Chen, 2020).Switching behavior Consumer behavior is described as the investigation of human actions throughout the selection, acquisition, use, evaluation, and disposal of products and services that are supposed to satisfy their diverse needs (Mou & Benyoucef, 2021).Many hypotheses are widely employed in previous studies when analyzing consumer behavior because each individual is different and has a variety of driver elements that affect their behavior.In addition, various theories can be used to comprehend client behavior about the objectives of the behavior they wish to comprehend.According to Sun (2017), a consumer's switching behavior is typically defined as an action in which they choose an alternative to their current, trusted service providers (Sun et al., 2017).Due to the wide range of goods and services available, this behavior cannot be disputed, and numerous theories and frameworks have attempted to account for it from various angles.Some investigations have proposed integrated switching intention models and employed empirical approaches to support previous therapies (Chen & Yang, 2019;Bansal & Taylor, 2002;Burnham et al., 2003).The model created by (Bansal & Taylor, 2002) was adopted by Service performance and switching cost were the two variables that Keaveney (1995) reduced from eight to two.They then examined their connection to customers' intention to switch brands by combining addition, they created a Service Provider Switching Model (SPSM) that combined these variables with three main determinants from the planned behavior theory (attitude, subjective norms, and behavioral control) (Chen & Yang, 2019).Relational interactions between the three key factors influence customers switching intention and can be changed (Bansal & Taylor, 2002).
The main element influencing perceived value is the perceived quality of the product by the user (such as the quality of the information, the system, and the service (Kuo et al., 2009).Highquality services include reliability, responsiveness, certainty, and personalization.If an internet transportation service provider has the capacity and the will to do so, it can supply customers with quality services (such as dependability, responsiveness, and assurance) (Shin D.H., 2014;Zhou, 2013).Users' perceptions of the value of an online transportation service provider change if it cannot deliver services on time or offer dependable and customized services.Zhou's study on cell phones (2013) looked at service quality and found that it has a favorable impact on consumers' experiences, while unreliable and poor internet connections make consumers less happy.Poor service quality might decrease users' perceived happiness with mobile-based services (Tam & Oliveira, 2017).As a result, we put forth the following hypothesis: i) H1: E-Service Quality (ESQ) positively affects Switching Intention (SI); ii) H2: E-Service Quality (ESQ) positively affects Switching Behavior (SB).
Price perception is usually synonymous with the perception of the costs incurred to buy the product and the perceived quality of the product (Chi et al., 2021;Chen & Keng, 2019).According to (Benneke & Zimmerman, 2014), customers view prices in personally meaningful (inexpensive or expensive) ways rather than focusing on accurate pricing.Customers who value time and effort will be more concerned about making time and effort sacrifices, affecting how they perceive price (Yu et al., 2018).i) H3: Perceived Price (PP) positively affects Switching Intention (SI); ii) H4: Perceived Price (PP) positively affects Switching Behavior (SB).
Several studies demonstrate that when users are unhappy about the good or service they are utilizing, their unhappiness impacts how they decide to switch.According to Bhattacharjee et al. (2012), social media flipping was fueled by users' unhappiness with the current known goods or services and ledge that possibly superior goods or services may be available.Investigated the switch patterns of social networking site users and investigated the variables affecting users' intention to switch (Xu et al., 2014).The research hypothesis is as follows: i) H5: Experience Dissatisfaction (ED) positively affects Switching Intention (SI); ii) H6: Experience Dissatisfaction (ED) positively affects Switching Behavior (SB).
For distinguishing intentions and behavior, this study adopted the Theory of Planned behavior (TPB), which explains that behavior arises from individuals because of the intention that is influenced by internal and external factors of the individual alone (Chen & Yang, 2019).Hypothesis: i) H7: Switching Intention (SI) mediates the relationship between E-Service Quality (ESQ) and Switching Behavior (SB); ii) H8: Switching Intention (SI) mediates the relationship between Perceived Price (PP) and Switching Behavior (SB); iii) H9: Switching Intention (SI) mediates the relationship between Experience Dissatisfaction (ED) and Switching Behavior (SB).

METHODOLOGY
The research in this study is quantitative.A quantitative approach in management and business research is often used because it can measure opinions, attitudes, and behavior (Sekaran, 2014).Based on the research objectives, the use of explanatory causal aims to obtain an explanation of the variables studied, including switching behavior, switching intention, e-service quality, perceived price, and experience dissatisfaction.The integration of measurement and structural models was facilitated by using Partial Least Squares (PLS), a latent structural equation modeling technique.PLS use a component-based approach.
Additionally, PLS makes it possible to model latent constructs as formative or reflective indicators necessary for our approach (Fu, 2011;Keil et al., 2000).The sample used in this study was 200 Z generation in the capital city of Indonesia, Jakarta, who use Platform-based transportation and switch brands using a purposive sampling technique.All question items are measured with a Likert scale of 1-5.

DISCUSSION
The validity and reliability of each indicator employed in each variable in this study, both exogenous and endogenous variables, are related to the measurement of the model in the outer model.Testing a study's validity is very important to see how appropriate a variable is used in research.The results of a study that meets validity are expected to be able to answer questions in the research conducted.In Table 1, the average variances extracted (AVEs) values, as seen from the diagonal values (ESQ = 0.783; PP = 0.867; ED = 0.886; SI = 0.906; SB = 0.680), are found to be greater than the other values.Therefore, discriminant validity is fulfilled.Based on the output in Table 2, the results obtained from the composite reliability coefficients are above 0.7, so they are declared reliable.Source: author's work.
Table 2 demonstrates the reliability value calculated using Cronbach alpha and composite reliability.The E-Service Quality (ESQ) variables reliability value, which is 0.905, falls into the extremely high category in the composite reliability value.The Perceived Price (PP) variable is 0.901, the extremely high category.The composite value of the reliability value of the Experience Dissatisfaction (ED) variable is 0.936, with a very high-reliability category.The reliability of Switching Intention (SI) is 0.932 in the extreme category.Meanwhile, the Switching Behavior (SB) variable is 0.719, which is sufficient.Based on the composite reliability test results, almost all variables are in the very high category, such that the information obtained can be utilized to test hypotheses.The Cronbach's alpha value of the EESQ, which is 0.873 included in the high group.The reliability value of the PP variable is 0.833 in the high category.At the same time, the value of the ED variable is 0.909, which is included in the extreme category.The SI variable reliability value of 0.890 is included in the high category.Meanwhile, the SB variable of 0.414 is included in the very low category.Based on the results of the reliability test of Cronbach's alpha, each value range has a different dependability depending on its category, allowing the data to be collected to test various hypotheses.
The output results of the PLS test from the data obtained in the field show that the criteria for the goodness of fit model (shown in Table 4) have been met, with an APC value of 0.246 and ARS 0.433, AARS 0.423 with a P value below 0.05 so that the model is declared fit.Considering the outcomes of the test for the hypothesis in Table 5, namely, the direct effect of ESQ on SI, the inner weight coefficient value is 0.05, with a p-value of 0.25.There is no significant direct relationship between E-Service Quality and Switching Intention because the pvalue is greater than 0.05, which means that E-Service Quality platform-based transportation companies does not affect the intention to switch to another transportation platform for Generation Z in Jakarta.Testing the direct influence between PP on SI obtained a coefficient of inner weight value of 0.44, having a p-value <0.01.Because the p-value < 0.05; then there is a significant direct effect between PP on SI, meaning that the higher Perceived Price will result in higher Switching Intention of generation Z platform-based transportation users in Jakarta.The immediate result of ED on SI obtained a coefficient of inner weight value of 0.27, p-value <0.01.The p-value was less than 0.05; then, ED and SI have a substantial positive relationship, meaning that higher Experience Dissatisfaction will result in higher Switching Intention of generation Z platform-based transportation users in Jakarta.A value of 0.08 for the coefficient of inner weight was observed when examining the direct impact of ESQ on SB, having a p-value of 0.13.The p-value is more than 0.05; consequently, there is no substantial relationship between ESQ on SB, meaning that E-Service Quality in the platform-based transportation companies does not affect the Switching Behavior of generation z in Jakarta.
A value of 0.07 is the coefficient of inner weight achieved when the direct impact of PP on SB was tested, having a p-value of 0.17 > 0.05; no notable direct effect is present between PP on SB, meaning that a higher Perceived Price will not result in Switching Behavior of generation Z platform-based transportation users in Jakarta.At the same time, the direct effect of ED on SB obtained a coefficient of inner weight value of 0.35, having a p-value < 0.01, then a significant direct impact between ED on SB, meaning that higher Experience Dissatisfaction will result in higher Switching Behavior of generation Z platform-based transportation users in Jakarta.Testing the direct influence between SI on SB obtained an inner weight coefficient value of 0.46, with a p-value <0.01.The p-value was less than 0.05; then a significant direct impact exists between SI on SB, meaning that the higher Switching Intention will result in the higher Switching Behavior of generation Z platformbased transportation users in Jakarta.Table 5 provides test findings for the indirect effect.Based on Table 6 and Figure 1, it is known that the side effect of ESQ on SB through SI is -0.021.Given that the p-value for this study was 0.335 > 0.001, or p-value > 0.05, it can be said that, SI does not mediate the relationship between ESQ and SB.In other words, E-Service Quality does not indirectly have an impact on Switching Behavior through Switching Intention.As for the PP variable, it is evident that the indirect effect of PP on SB through SI is 0.203 with a p-value <0.001, so it can be concluded that SI mediates the connection between PP and SB.In other Evaluation of structural models and testing of hypotheses are shown in Figure 1. Figure 1 shows that SI mediates fully because perceived price does not directly influence switching intention.The more significant influence of a variable can be seen in the total effect, which is shown in Table 6.The impact of PP on SB is 0.137, while ED on SB is 0.479.The total effect of PP on SB is 1.9 percent.In comparison, the total effect of PP on SB is 22.9 percent, meaning Experience Dissatisfaction has a more significant effect than Perceived Price.These results are expected for a platform-based transportation company to pay more attention to the trigger factors of Experience Dissatisfaction to reduce the effect on consumers so that consumers do not switch brands from Platformbased transportation to other brands.
Based on the analysis finds, it is understood that the relationship between supply and demand, specifically as a provider of public transportation fleets and users, as well as managing electronic systems, is the basis for the legal position of providing online applicationsbased public transportation applications under ITE law.Providers of online apps for public transportation have a responsibility to run electronic systems in a trustworthy and responsible manner in this capacity.Application organizers may be created by the state, individuals, corporations, and the community, subject to further regulation.The ITE Law also regulates the obligations of online transportation services.Specifically, business actors who offer products through electronic systems must provide accurate and complete information about the contract terms, manufacturers, and products.

CONCLUSION AND RECOMMENDATION
Considering the outcomes obtained, therefore, it can be said that the behavior of brand switching from a Platform-based transportation company to other brands in Generation Z in Jakarta is influenced by Perceived Price, Experience Dissatisfaction, and E-Service Quality does not affect switching behavior.From these results, it can be concluded by the researchers that it was not just the price factor that made Generation Z switch brands.They tended to switch brands because of an unpleasant experience using Platform-based transportation services, and Platform-based transportation E-Service Quality was not a factor causing brand switching for Generation Z.The Switching Intention succeeded in mediating the relationship between Perceived Price and Switching Behavior.In addition, Switching Intention also managed to mediate the relationship between Experience Dissatisfaction and Switching Behavior.Based on the results obtained, we recommend future research, preferably on platform-based transportation services and public transportation in different countries according to different characteristics and cultures, to compare the results.

ACKNOWLEDGEMENT
We want to express our gratitude to the Department of Management at Universitas Pendidikan Indonesia in Bandung, Indonesia, for inspiring us to pursue a career in marketing through their lectures.We would also like to thank our colleagues at the Department of Management at Universitas Sultan Ageng Tirtayasa in Serang, Indonesia, for their unwavering support.Lastly, We are grateful to Beasiswa Pendidikan Indonesia (BPI) for providing the financial assistance to pursue our doctoral studies.

Figure 1 :
Figure 1: Result of the research model Source: Authors' finding

Table 2 :
Reliability results

Table 3 :
Model fit

Table 4 :
The p-value of the Hypothesis Test on the Inner Model

Table 5 :
Testing results of the indirect effect

Table 4 : Continued Platform-based transportation company in Indonesia: Perspective… Arum Wahyuni Purbohastuti et al. www
.ieeca.org/journal 873 words, Perceived Price indirectly significantly affects Switching Behavior through Switching Intention.In the ED variable, it is evident that the impact of ED on SB through SI are 0.125 with a pvalue of 0.006 (which is less than 0.05).Consequently, SI mediates the relationship between ED and SB.In other words, Experience Dissatisfaction indirectly significantly affects Switching Behavior through Switching Intention.

Table 6 :
Total effects