Keywords

Service Quality, Car-hailing, Gig Economy, Loyalty, Offline Service, Online Service, Gig Economy.

Introduction

For businesses, the Gig Economy provides a technological and economic means of transformation, but it also raises new obstacles for business model innovation because of its roots in cooperative consumption (Belk, 2014). It also transforming the way people interact is the use of information technology to enable peer-to-peer exchange and the generation of value from previously underutilised private property (Belk, 2014). In online platforms based on the Gig Economy, commodities and services, both tangible and intangible, are common resources (Cheng, et al. 2017). Using mobile commerce networks, this study aims to supply non-physical services.

In the Gig Economy, there are many service providers that do not have any formal qualifications for work, which has caused havoc with traditional ways of service management .Ride-sharing services such as Uber and Ola are just two instances of companies that have successfully adapted their business models to accommodate them. Ride service users can book and pay for their rides, as well as leave feedback, all online. For pick-ups and drop-offs in offline mode, drivers use their own personal vehicles. Managing mobile ride-hailing services involves managing both online and offline businesses while driving, thanks to the Gig Economy.(Tripathi et al.,2022a)

More and more people are recognising the importance of receiving high-quality service. It have been the focus of previous studies on service quality and responsiveness. But the scope of these inquiries is restricted to solely online and offline offerings. Only a few studies have examined both online and offline methods of interacting with customers Curiosity is often cited as a driving force behind people using platforms in the shared Economy (Davidson, et al. 2018).

We need to Watch and see when the novelty wears off for users, will they continue to utilise the service.More research is needed in this area to better understand how service quality and customer loyalty are interwoven in this new era.

Before moving on to methodology and research design, we present our theoretical framework and the study's hypotheses. When we're done, we'll go over everything we found during our investigation. Lastly, we'll talk about the ramifications of our findings and the potential future routes they could take us in.

Cenceptual Context

Gig economy: Giving something away for free or at a cheap cost as a social exchange is what it means to "share" Information technology has spawned a plethora of new concepts that can be easily communicated in the digital realm. Instead of encouraging ownership, the Gig Economy is a relatively new economic and technical development. (Tripathi et al., 2022b)

The Gig Economy can be viewed from a variety of perspectives, including those of social commerce and ideology. Individuals can now work together to make better use of unused goods thanks to peer-to-peer markets . There are less market entry barriers for service providers, making it easier for individuals to become service providers. Government certification is not required for all service providers. It is possible to have a flexible work schedule thanks to platforms. Gig Economy services are non-standard and different as a result. (Tripathi et al., 2022c)

These platforms are distinguished by the social connections between service providers and clients, as well as the satisfaction that consumers get from using these services. Many platforms in the online Gig Economy claim to offer a new sort of social service by bringing together strangers. Mobile markets, which are driven by the Gig Economy, have a different need for high quality service than traditional internet markets because of the social nature of commerce.

Online and offline actions are necessary to do business as part of the Gig Economy. As a result, our research broadens the scope of service quality research by considering it in the context of both online and offline service delivery.

Service Quality

When we talk about customer satisfaction, we're referring to the difference between what customers expect and what they receive (Parasuraman, et al. 2005). The quality of customer service has been the topic of numerous studies in the past. SERVQUAL is measure to adjourn upto what degree or extent customer is satisfied (Parasuraman et al., 2005). Five characteristics of customer service, according to SERVQUAL, are reliability, assurance, tangibles, empathy and responsiveness. There has been a long history of research showing the importance of these elements in the development of any product or service. Research on service quality in similar contexts has raised the question of whether similar elements of service quality were present in other study situations. (Yadav et al., 2022c).

The research community's C2C connectivity and service quality are still deficient. On the platform, drivers who have signed up to provide automobile services have an established relationship with customers based on service quality in Gig Economy-driven mobile commerce marketplaces. As a result, it's possible that present research on service quality need to be reevaluated. You can use a smartphone or tablet to make a purchase. No other research has looked at both online and physical customer service simultaneously, as far as we know.

User Loyalty and Retention

The degree to which a customer feels emotionally and logically committed to a product or service is known as "user loyalty". It has always been a good indicator of success for a client if they consistently express their goal or take action.

Customers' contentment and quality of service are key factors in building brand loyalty . Happiness after adoption is influenced by a person's expectations and how well they perform, according to the expectancy-disconfirmation theory(Yadav et al.,2022b). It's more probable that customers will be satisfied with the car-hailing mobile commerce service they've purchased if it meets or exceeds their expectations (positive disconfirmation). The theory of planned behaviour says that an individual's attitude toward behaviour, subjective norms and impression of behavioural control all influence their eventual behaviour.According to theory, customer service, customer happiness, and customer loyalty are three of the most commonly researched post-use behaviours. It has been shown that customer satisfaction acts as a bridge between client loyalty and consumer satisfaction .

They have been examined in diverse situations and linked in various ways. It is possible that the perception of a business-to-business connection may be influenced by factors such as confidence and commitment. It has been discovered that in the retail industry, consumer loyalty is linked to factors like switching costs and attractiveness of alternatives. We will use the basic model of service quality, customer happiness, and customer loyalty as a research framework to better understand consumer loyalty and its antecedents. The following connections exist between car-hailing services, in our opinion.

H1. Satisfaction Perception is influenced by Service Quality

H2. User Loyalty is influenced by Service Quality

H3. Higher User loyalty due to higher Satisfaction mediated by Service Quality

H4. Loyalty is mediated by Service quality.

Perceived Attitude towards Gig Economy

Is an individual's predisposition to either like or dislike a company's latest product or service in the Gig Economy. When it comes to attitude, it is more of a pre-decision construct than a result of customer happiness .

Attitudes, whether positive or negative, can impact decisions A positive attitude of the Gig Economy helps users to be more supportive of service providers. Mistakes made by service providers are also minimised or ignored (Diallo & Seck, 2017). Customers are more likely to use the new form of car-hailing, regardless of the quality of the online and offline service, if they have a positive attitude toward Gig Economy.

H5. Attitude moderates the service quality effect on loyalty.

H6. Positive attitude moderates the service quality effect on loyalty.

Research Design

There were two phases of the research. A qualitative inquiry into the determinants of service quality in car-hailing commerce based on the Gig Economy was started first. The research model's hypotheses were then put to the test using quantitative survey data in the second step. An experimental sequential design was implemented. On the basis of our qualitative findings and an already-existing framework, we worked to interpret the quantitative data. This section describes the qualitative and quantitative aspects of the overall design.

Qualitative Investigation

Through this research, we hope to learn more about how the Gig Economy's car-hailing business is managed, as well as what factors influence the quality of service provided both online and offline. Semi-structured interviews were conducted in order to answer our first research question. For our study, we questioned 71 people who have used hail service sharing on mobile car-hailing apps to find out their experiences.

The interview process was broken down into three distinct stages. To begin, we inquire about the respondent's personal and professional background, as well as his or her involvement in the ride-sharing industry and past experiences. Finally, we solicited their thoughts on the advantages and disadvantages of online and offline services, as well as their frequency of use and any proposals for new business models.. A digital audio recorder was used to capture all interviews, which were then transcribed verbatim. Our interviewees gave us their consent to record before we started.

There were four stages to our data analysis procedure, which included the following:

The first step is to code the qualitative data. All the data sources were coded together and classified in this step. Data from the interviews were coded with the letter Ik (where k is the number of interviewers).

Step 2: In this the system of open coding is applied.. A collection of opinions summarising steps and consumer feedback was designed to better understand how car-hailing services work. In order to ensure that all of the important keywords and phrases were included, one of the authors spent a considerable amount of time reading over the texts. While the data was being preliminarily processed, both coders double-checked their results. Additionally, the previous literature was used to construct a master list of variables. It was decided to rename selected terms in the texts, and to classify same narratives in a club. As a result, we choose to retain some of the original definitions of the phrases we drew from the qualitative text.

To design a structure, identify the most important concerns and incorporate the findings. In this process, you'll go through two stages. We started by identifying the general stages of mobile car-hailing services powered by the Gig Economy(Yadav et al.,2022a). Our next step was to combine basic resources into higher-order creations by using axial coding. Short descriptions were used to code problems.

Finally, combine theory development and visual mapping to provide a more complete picture of your findings. To explain the early findings, we sought to include many theories.

For our online service quality elements. Mobile car-hailing platforms that have strong structural assurance can protect a user's privacy, money, and security by providing institutional guarantees. It's been reported that a woman was harassed by the driver of a car with constant phone calls, according to an interviewee. When I use the car-hailing service, I'm worried that my personal information is being leaked from the platform .

Platforms that are responsive are ones that can respond immediately to the needs of their users.. "Sometimes it is quite irritating," one interviewee said. Despite my desperate need for a vehicle, no one would give me a lift. There is no guarantee that drivers will be on time even if they have my order. There is room for improvement in the platform's matching algorithm, in my opinion. Offline service quality is measured by the degree to which the platform's descriptions match the information provided offline. Users' enjoyment is influenced by their mental processing of information which does not line up, according to the theories of schema congruity and cognitive fit "I encountered a number of situations with the mismatch of online profiles," said one respondent. On the platform, a different model car with a different licence number is shown with a separate licence plate.

'Competence' is a term that describes the one’s ability to perform in continuity such as the delivery of promised services and the performance of competent work. The driver of a car-hailing service in India had no understanding how to go to the interviewee's destination, according to an interviewee. As a result of his continuous detours, I was unable to arrive to my final destination. In my opinion, the driver was inadequate to transport me." (Parasuraman et al. 2005) proposed a slightly different definition of empathy than we do . Eccentricity is defined as an entity's personality and compassion toward clients in the context of ride-sharing services. As one respondent put it: "There are instances when the drivers don't seem to care. When I request a car-hailing service, I simply want to snooze throughout the ride because I'm already exhausted after a long day at work. In any case, he won't stop talking to me. It's a pain in the neck. Also, the AC may be set to an extremely low temperature at times by the drivers. It's just too much for me. In spite of my repeated pleas, they still refuse to turn up the AC temperature."

As per the qualitative coding, we included the conceptual linkages in our study model. Virtual and physical influences on performance or service satisfaction are included in this model along with an attitude toward the Gig Economy. The following section describes the empirical testing that was done to determine the relationships between these factors.

Quantitative Questionnaire

The input in the questionnaire was derived from past studies and a total of twenty six items were used in the construct using Likert scale. The Likert scale ranged from "1 = strongly disagree" to "5 = strongly agree" for each item. As a result, all of the indications were labelled as reflective provides a list of the elements as well as the sources for each one. Respondents were selected from different cities from India. There were a total of 312 completed questionnaires. Each questionnaire was thoroughly checked and those which were not appropriate for further study were discarded. With 126 men and 168 females, 294 samples were collected. The demographic information of the respondents. All participants received monetary compensation for completing the surveys. In addition to full-time students and consultants, the study drew in executives and other professionals in a variety of other fields as well. Ride-hailing services were used an average of 20 times by the participants.

More than half of those polled are between the ages of 18 and 30. Some 22% of participants are between the ages of 31 and 40. According to this age distribution, it's okay since young people are more open to new experiences.

Data Analysis

Measurements

SPSS 21 and Smart PLS 3.0 were used for data analysis. A three-step procedure was used to assess the validity and reliability of the convergent, discriminant, and combined measures. For the scales, Cronbach's reliability and experimentation factor analysis (EFA) were utilised. Factors with a loading of less than 0.5 were excluded to preserve data quality.

The link between latent components and observable items was subsequently investigated through the use of confirmatory factor analysis (CFA). AVE (average variance extracted) and composite re-liability were also evaluated for these factors (CR) displays the initial measurements' results.

When it comes to evaluating the quality of an online service, two first-order constructs (structural assurance and platform responsiveness) aren't closely linked or distinct. The repeated indication strategy was used to estimate higher-order structures using PLS.. To assess offline service quality, three first-order characteristics are used, none of which are strongly associated or distinguishable (in-formation congruity, competence, and empathy). Each construct's AVE was measured.

Our model's validity has been established. The common method bias (CMB) was further examined using Harman single factor analysis (SF). There isn't a clear winner here. To put it another way, our model doesn't include CMB.

Hypotheses Test

The PLS method is widely used in a variety of relevant . To test the hypotheses, we used SmartPLS 3.0 for data analysis. We also used it to evaluate the conceptual model's predictive utility model's overall fit.

The results of the tests performed on the various hypotheses. Almost all of our six hypotheses were right in our investigation. Consumer contentment has an R² value of 0.335, whereas brand loyalty has an R² value of 0.261.

To explore if online and offline service quality served as a mediating factor in creating loyalty, researchers looked at both direct and indirect impacts of pleasure on loyalty. Customers were not taken into consideration when testing the model in the beginning.

No significant correlation between online and offline service quality was found, despite the fact that offline service quality was found to have an effect on customer loyalty (β = 0.241, p = 0.001). Second, we looked at customer satisfaction and the links between it and other factors, like as loyalty. Both offline and online service quality significantly impact customer satisfaction and satisfaction also impacts customer loyalty. Loyalty is directly influenced by online service quality (β = 0.279, p 0.01). Offline service quality has a significant impact on customer loyalty (β = 0.212, p 0.001). Customer happiness and loyalty can be influenced by the quality of the offline service as well as by the online service, both of which have an indirect effect on each other. Service quality and customer loyalty are intertwined through customer satisfaction. Consequently, hypothesis 4 is only partially accepted.

Then, we examined the impact of attitudes about the Gig Economy on our findings. Moderating and dependent variables were grouped together in one location. Loyalty is not influenced by attitudes toward the Gig Economy (β = 0.052).

No evidence was found to support H4, which indicated that a person's belief in a "Gig Economy" moderates the association between online service quality and customer loyalty. In we depicted this moderating impact. Customer loyalty does not change as the quality of online service improves at low levels of trust in the Gig Economy.

Discussion and Conclusion

According to our findings, there are three main takeaways: For a car-hailing service powered by the Gig Economy, two aspects of online and three aspects of offline service quality were analysed. Customers' satisfaction and brand loyalty were found to be strongly influenced by structural assurance and platform responsiveness. As a result of these three factors, customers are more likely to be satisfied and loyal to your company. The quality of both online and offline services was evaluated using data from a questionnaire.

Second, we examined the impact of the Gig Economy on the quality, happiness, and consumer loyalty of car-hailing mobile commerce. In the context of our study, the connections were validated data indicated that both of the direct impacts were approved. Loyalty is a direct result of client satisfaction (0.229, p 0.01). In the relationship between high-quality service and customer loyalty, satisfaction acts as a mediator. Another way of putting it is that customer satisfaction is intertwined with customer loyalty and customer satisfaction.

We looked and examined the relationship between level of srvice and client loyalty, devoid of whether the said offering was offered online or offline, to find out more information about this. Car-hailing apps like Uber and Ola are an example of service innovation in the Gig Economy. Our study found that customer happiness and service quality impacts the enhancement of loyalty from the client.

5.2. Implication and Future Scope

Implication on Theory Building

Studies have shown that customer happiness, loyalty, and quality of service are all linked. As part of this study, we looked again at the impact of service quality on satisfaction, as well as how satisfied customers are more likely to become repeat customers. New insights from both the customer and the service provider's point of view are presented in this study.

By performing exploratory investigations into the elements that influence the quality of online and offline customer service, this study contributes to previous research. Some of the criteria we identified in terms of service quality overlap with prior, such as platform responsiveness and competency, which we found in our research. Human psychology and information congruity go hand in hand (. Empathy and structural certainty, for example, are constructs that serve to integrate prior literature. These new findings will help researchers better understand how and how much customer loyalty is influenced by things like the quality of a company's service.

The findings show that people's attitudes toward the Gig Economy have a significant impact on outcomes. Our findings on the Gig Economy and mobile commerce are a welcome addition to what has already been discovered. In this study, service quality is evaluated from both an online and an offline standpoint. Preliminary findings from this study show that the expectancy-disconfirmation theory as well as the theory of planned behaviour may be applied to the Gig Economy, thanks to in-depth interviews conducted before the questionnaire was created. Chauffeured car-hailing services.

Practical Implications

Companies place a high value on service quality management. Our study's research model is applicable to all organisations in the age of the Gig Economy that conduct online to offline business. Using our approach, firms can have a better theoretical understanding of service management that will help them increase the number of registered users and sustain customer loyalty. As a result, car-hailing platforms can benefit from aspects such as online and offline service quality by better serving their existing customers while also attracting new ones. Passengers who are dissatisfied with the service they receive can quickly stop utilising it. Car-hailing service providers in the Gig Economy rely heavily on amateur drivers who are also platform members. Providing standardised driver training could be difficult for platforms, which in turn raises the likelihood of clients being dissatisfied with the service they receive from service providers in car-hailing processes. As a result, drivers are recommended to follow possible suggestions for better service, such as examining the platform's information carefully.

Limitations and Future Research

When evaluating the findings of this study, keep in mind some of the study's limitations. To begin with, the survey only included mobile car-hailing users in India. Other put it another way, the findings may not be readily transferable to countries with diverse cultural and platform circumstances. In order to see if our findings can be extrapolated to other car-hailing commerce customers, we'll need to do further study and interview a broader range of people.

Cross-sectional data may hinder our capacity to investigate dynamic changes and seasonal influences on user loyalty behaviour. To be clear, we just tested the bare minimum of assumptions about customer pleasure, contentment with the customer experience, and customer loyalty. Our model does not take into account factors like trust, which could have an impact on a person's level of loyalty. In the actual world, the impact of service quality on customer loyalty may be overstated. We support future studies that use longitudinal data to follow shifts in perceptions among survey participants. Furthermore, we plan to incorporate additional variables to better understand the relationship between service quality and customer loyalty. By evaluating the attractiveness of different options, policy shifts and behaviour patterns we can gain a deeper understanding of service quality management structures.

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Received: 21-Nov-2022, Manuscript No. AMSJ-22-12915; Editor assigned: 22-Nov-2022, PreQC No. AMSJ-22-12915(PQ); Reviewed: 20-Dec-2022, QC No. AMSJ-22-12915; Revised: 03-Jan-2023, Manuscript No. AMSJ-22-12915(R); Published: 24-Jan-2023