Introduction
In most of the progressing states of the world, including Bangladesh, modern technology reforming the society is one of the prominent influential factors for boosting economic growth. The progress of conveyance as well as transmission techniques and ride-sharing platforms is one of them. The ride-sharing platform plays a crucial part in the fields of employment, financial growth, and social advancement. The aim of a socio-technology platform is to create a balanced environment where technology assists human roles and social structures. The theory of socio-technology platform applies to workplace design, software development, and organizational change management.
The ride-sharing service has an enormous part in supporting environmental concerns by diminishing roadblock and log jams, forming the lowest number of motor cars and automobiles on the road. On account of the attainability of smartphones and extensive access to the internet, divergent app-based services have expanded exorbitantly. At the same time, it is useful to single out the closest means of transport within the shortest feasible time. This research showed that the extensive prospects of ride-sharing platforms are opening a new door of employment and the business growth in Bangladesh. The article emphasizes that ride-sharing services have a broad chance of acceptability and opportunities in Bangladesh. The research is concentrated on to recognizing the socio-economic benefits of ride-sharing platforms and challenges from a business perspective.
While the findings of the research disclose that ride-sharing apps act as a socio-technology platform in Bangladesh; this article also focuses on the socio-economic benefits of ride-sharing apps in Bangladesh. This article also discusses the perceived benefits and the ease of use of the ride-sharing app in Bangladesh.
According to the Bangladesh Institute of International and Strategic Studies (BIISS), ride sharing is the largest segment of the gig or digital economy in Bangladesh; it presently worth $259 million.
Bangladesh is an overpopulated country, and its present unemployment rate is forecast to be 5.09% in 2024. Ride-sharing platforms lessen the unemployment problem of Bangladesh. The fact that ride-sharing created employment opportunities for more than 40,000 people connects the ride-sharing platform people in distant places in Bangladesh. This platform reduces carbon emissions and reduces traffic jams in Bangladesh.
In this research, the author discusses how ride-sharing apps perform the function of socio-technology platform in Bangladesh. The author also explains the socio-economic benefits of ride-sharing apps. Here the author used the Technology Acceptance Model (TAM) to analyze the ease of use of ride-sharing apps, the usefulness of ride-sharing apps, and the social impact of ride-sharing apps. Again, the author also explains how these factors affect the intention of people to use the ride-sharing app in Bangladesh.
In this article the author uses a goodness of fit test to measure the relationship among different variables. The author also employs the validity test in order to test the accuracy and soundness of the research findings.
The main motivation of the research is to explain how modern technology reforms our society. This article reveals that technology has led to increased productivity and economic growth in our society.
Objective of the research
Specific objective
This article discloses that a transportation network makes the market more competitive. This research proved that ride-sharing apps can optimize the use of social resources, having a revolutionary effect on the socio-technology platform. The prime objective of the research is to prove that the ride-sharing platform enables people and companies to earn profits from underutilized resources of Bangladesh.
General objective
The research was initiated to attain the following objectives:
i. To analyze the role of ride-sharing apps as a socio-technology platform in Bangladesh
ii. To assess the influence of ride-sharing platforms on socio-economic development in Bangladesh.
iii. To inspect the influence of various social factors on the ride-sharing platform of Bangladesh.
Literature review
A passenger finds his/her closest available car by ride-sharing app. The driver searches for the right passenger and matches it by ride-sharing app. Through ride-sharing app, people are connected and receive various information from distant places. Again, he/she can accept or reject the ride with a driver.
In ride-sharing services, drivers use their private vehicles to provide services or rides. Mainly rides are given to passengers on their request. Now many ride-sharing companies have made ride-sharing easy on the basis of their networking (7). Now ride-sharing apps act as a source of income; in Bangladesh, people easily use their smartphone to receive the request for a ride and earn money.
Ride-sharing apps are a good source of income in order to save fuel and time.
Today, ride-sharing services have many limitations, a lot of disputes, and ride-sharing platforms face numerous difficulties. From one point of view, ride-sharing services obtain the jealousy and anger of traditional taxi services; adverse reactions from insurance companies and governments regarding several matters and points.
Ride share service provider agencies and enterprises are expanding repeatedly, and people of Bangladesh are also connecting with these services. Traditional taxi service providers oppose ride-sharing services on various grounds, but the main reason for their protest is the direct clash of interests. Ride-sharing services have been extremely accepted and well-liked and people have gone ahead using these services owing to the fact that they are low-priced, speedy, and available at lower fares (7). However, traditional taxi operators oppose ride-sharing services on grounds of lack of rules, guidelines, and security concerns in Bangladesh.
Ride-sharing services have several benefits, and various welfare are related to/connected with the modern ride-sharing platform. It is very easy to download a ride-sharing app on the smartphone. People can turn to ride-sharing service just by accessing the ride-sharing app delivered by the service providers or ride-sharing agency. The ride-sharing company transferred the service message to the taxi-cab driver/bike driver who is located close at hand or not far away and can arrive at the passenger at the lowest possible time. The top ride-sharing companies, Uber and Lyft, operated their own software and tracking system in order to find/detect the drivers obtainable/occupied in the top closeness/adjacency (8). At the same time, the passenger gets a message/call with contact details of the driver and the anticipated/awaited arrival time. It is considered that the ride-sharing services are safe because all service providers can track their cars by using GPS services that are already installed in each car.
People can make use of ride-sharing services at any time from any location. The services are clear and simple because no financial transaction between driver and customer before the trip happened.
From the security perspective, it is the safest ride for passengers as as there is no is need or prerequisite to bear paper money to pay the driver. Again, the driver has no chance or possibility to pick up passengers with a plan to rob the passengers.
The software and technology used by the ride-sharing company can able to acquire data and particulars relating to passengers in addition to the drivers.
Now many ride-sharing agencies are operated in Bangladesh. These agencies offered various types of ride-sharing services, such as long-distance service, short-distance service, courier service, ambulance, etc. Most popular agencies are Uber and Lyft because these are well organized and systematic on the basis of availability, right lift time, dropping time, economy, money-saving, security, and risklessness. Passengers also preferred to make use of these services as they are swift and accessible at convenient price.
The research found that customers’ preference regarding ride-sharing services is upgrading not just financial benefits; furthermore, passengers are now aware of environmental concerns, and they try to protect the natural resources and environment. Now all people, either literate or illiterate, have become more conscious of about their obligations regarding environmental pollution and climate. The ride-sharing vehicle emits low carbon dioxide, and its consumption of fuel level is low. So most responsible citizens prefer ride-sharing apps on account of protecting the earth and environment. Again, the easiness of ride-sharing apps also enhances its acceptability. The passengers do not need to walk to taxis and deal with taxi drivers if they avail the ride-sharing platform (Kamar and Horvitz).
Having observed the case studies and comparative analysis of the above-mentioned topics, the auhtor found that ride-sharing platforms have numerous advantages. Ride-sharing platforms assist in saving fuel, money, saving time, and easily available, etc. Ride-sharing platforms also play a part in decreasing traffic on roads; traffic jams are now a severe issue in Bangladesh, especially in the Dhaka city (9).
Although ride-sharing services have a number of opportunities and advantages, it is definitely beneficial for people, but in parallel, some undeniable drawbacks and disputes are firmly connected to ride-sharing platforms.
Ride share services are burdened with several disputes. Online ride-sharing services are harassed by traditional taxi service providers, insurance companies, and various legislation; they are hampered by ride-sharing services in different ways. The government of Bangladesh should take all appropriate action for the purpose of eliminating complexities and difficulties of ride-sharing service and try to make the ride-sharing service more advantageous and suitable for all people. Challenges of all connected parties of ride-sharing companies should also be addressed for the purpose of making ride share services more successful (9).
In addition, carpooling and ride-sharing provide huge environmental benefits. The ride-sharing service plays a significant role in decreasing carbon emissions, alleviating traffic congestion, maximizing the efficiency of transportation infrastructure, and reducing fuel consumption. These benefits of ride-sharing platforms play a crucial role in creating a sustainable development.
Socio-technology platform and its function
Social technology is an applied field concerned with social tools and techniques. Socio-technology platform used social tools for effecting value-oriented human-environment interactions, focusing on social organizations and institutions. This platform used these techniques as a strategy for achieving self-propelling communities and sustainable development (10).
A socio-technology platform is an app that facilitates social interactions and is enabled by a communications capability, such as the Internet or a mobile device. The socio-technology platform is very useful for solving social and economic problems.
Social technology is a platform which is helping us to meet the United Nations (UN) Sustainable Development Goals (SDGs) (10). Social technology saves time and money in the achievement of the UN SDGs.
At the recent stage of social technology research, two main directions of usage of social technology have arisen:
(a) human-oriented technologies and
(b) artifact-oriented technologies.
Socio-technology is the short form of “social technology.” It is the study of processes at the intersection of society and technology. Vojinović and Abbott define it as “the study of processes in which the social and the technical are indivisibly combined.”
The positive impacts of socio-technology are extended lifespans, increased productivity, better access to information, and time-saving.
Major challenges
The ride-sharing industry of Bangladesh has a lack of regulatory scrutiny and skepticism and unhealthy competition. Another critical challenge for the ride-sharing platforms is ensuring consumer loyalty. In Bangladesh, motorcycle-based ride-sharing services are increasing, which sometimes causes road accidents.
Socio-economic benefit of ride-sharing app
Bangladesh’s ride-sharing market is likely to reach $1 billion in the next five to seven years, said analysts at a seminar in Dhaka today. According to Mohammad Mahfuz Kabir, research director of the BIISS, ride-sharing is the largest segment of the gig or digital economy, worth $259 million in the country (11).
In Bangladesh, the ride-sharing platform creates more free time. Riding in a carpool, vanpool, or on transit allows us to check email, browse the Internet, catch up on social media, read a book, or even take a quick nap. It is saving money—by ridesharing we can save money on parking and gas. Ride-sharing platforms act as a courier service in an emergency to deliver important parcels.
In summary,
• The ride-sharing apps are convenient, affordable, communicative, and speedy, as well as the ride-sharing apps lessen environmental pollution.
• Ride-sharing companies create new employment, which reduces the unemployment rate in Bangladesh.
• In Bangladesh, ride-sharing increases mobility and improves communication and the transport system.
TAM and UTUAT
According to Davis, TAM posits that there are two factors that determine a technology will be accepted by its potential users: perceived usefulness and perceived usefulness with their attitude and intention.
According to the model of UTUAT, habitual aspiration/intention of the customers is the main factor to determine the authentic use of a new technology (12).
The theory of UTUAT recommends that the perceived tendency to accept the latest technology is determine by the direct influence of four prime constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions.
Hypothesis and data processing method
Here the author used a questionnaire to evaluate the impact of ride-sharing apps as socio-technology platforms and socio-economic development. The author takes interviews of 415 customers face-to-face. In accordance with the variables of TAM and UTUAT, the below-mentioned hypotheses have developed:
H1: There is no remarkable association that exists between perceived ease of use and attitude of customers to using ride-sharing apps.
H2: There is no remarkable association that exists between perceived usefulness and the attitude of customers regarding ride-sharing apps.
H3: There is no remarkable association that exists between a customer’s attitude to using the ride-sharing app and their intention to use it.
H4: There is no remarkable association that exists between age and a customer’s intention to use a ride-sharing apps.
H5: There is no remarkable association that exists between the education level and intention to use ride-sharing apps.
H6: There is no remarkable association between customer satisfaction and attitude of customers ride-sharing apps.
H7: There is no remarkable association between social influence and the intention of customers towards using ride-sharing apps.
H8: There is no significant relationship that exists between the number of trips and earnings through the ride-sharing platform.
H9: Ride-sharing platform has no significant impact on socio-economic development in Bangladesh.
H10: There is no significant relationship between gender and their attitude towards the benefit of ride-sharing apps.
H11: There is no significant relationship between employment status and their attitude towards the benefit of ride-sharing apps.
H12: There is no significant relationship between residential status and their attitude towards the benefit ride-sharing app.
H13: There is no significant association that exists between marital status and their attitude towards the socio-economic benefit of ride-sharing apps.
H14: There is no significant association that exists between education level and the mindset of respondents towards ride-sharing platforms.
H15: There is no significant association that exists between education level and trust towards ride-sharing apps.
H16: There is no significant association that exists between type of occupation and their mindset towards ride-sharing apps.
Data analysis
The population used in this research is people with a sample of 415 respondents scattered across all over Bangladesh. Intentional random sampling is used for selecting samples. The researcher used a 1–5 Likert scale questionnaire for interview purposes. Research data analysis is performed by using the Person Correlation R test, independent t test, validity test, goodness of fit test, and the chi-square test. In this research, 1–5 Likert scale questionnaires were self-administered. The testing process was finalized when all the respondents accomplished their replies. The quantitative descriptive method was used as a research method in this paper.
The below Table 1 shows that young people are the highest number of respondents in this area. The mean value is 34.81, indicating that most of the respondents agree on that variable. The SD value of 6.57 indicates that there is a moderate stability of ideas on the specific variable.
Table 1. Distribution of respondents according to their age.
The below Table 2 shows that people with no education or graduate people are the highest number of respondents in these area; people with higher education level people are very low percentage.
Table 2. Distribution of respondents according to their education level.
The mean value is 1.75, indicating that most of the respondents do not agree on that variable. The SD value of 12.48 indicates that there is a low stability of ideas on the specific variable.
The below Table 3 shows that students are the highest number of respondents in this research. The mean value is 2.69, indicating that most of the respondents agreed on that variable. The SD value of 9.39 indicates that there is a low stability of ideas on the specific variable.
Table 3. Distribution of respondents according to their occupation.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (negative attitude), 2.5–3.4 (neutral attitude), and 3.5–5.0 (positive attitude).
A low standard deviation means there was a lot of agreement about the answers. High SD means there was a wide range of answers, indicating disagreement. Here the author gets a high standard deviation, indicating high disagreement among the answers.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (negative attitude), 2.5–3.4 (neutral attitude), and 3.5–5.0 (positive attitude). Here the author gets a high standard deviation, indicating high disagreement among the answers.
The means of all statements are greater than 3.5, respectively, which indicates a positive attitude of perceived ease of use of ride-sharing apps. The standard deviation of all the statements is greater than 10, which indicates high disagreement among the answers.
The means of all five statements are greater than 3.5, which indicates a positive attitude of perceived usefulness of ride-sharing apps. The standard deviation of all the statements is greater than 10, which indicates high disagreement among the answers.
The means of all the statements are greater than 3.5, which indicates a positive attitude of perceived benefit of the ride-sharing app. The standard deviation of all 5 statements is greater than 10, which indicates high disagreement among the answers.
The means of all the statements are greater than 3.5, which indicates a positive attitude towards ride-sharing apps. The standard deviation of all the statements is greater than 10, which indicates a high disagreement among the answers.
The means of all the statements are greater than 3.5, which indicates a positive attitude of intention towards ride-sharing apps. The standard deviation of all the statements is greater than 10, which indicates a high disagreement among the answers.
Here the author got the mean of all the statement, which are greater than 3.5, which indicates a positive attitude of social impact of ride-sharing apps. The author gets a high standard deviation, which indicates there was a wide range of answers, indicating disagreement.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (negative attitude), 2.5–3.4 (neutral attitude), and 3.5–5.0 (positive attitude). Here the author gets a high standard deviation, indicating disagreement among the answers.
The means of all the statements are greater than 3.5, which indicates a positive attitude of safety toward ride-sharing apps. The standard deviation of all the 3 statements is greater than 10, which indicates a high disagreement among the answers.
The means of all the statements are greater than 3.5, which indicates a positive attitude of trust toward ride-sharing apps. The standard deviation of all the statements is greater than 10, which indicates a high disagreement among the answers.
Test of hypothesis
H1: There is no remarkable association that exists between perceived ease of use and attitude of customers as regards using ride-sharing apps.
Outcomes in Table 16(b) expressed that there is a remarkable association that exists between perceived ease of use and attitude of customers regarding ride-sharing apps. So the null hypothesis is rejected.
H2: There is no remarkable association that exists between perceived usefulness and the attitude of customers regarding ride-sharing apps.
Consequences in Table 17(a) indicate that there is a notable relationship between perceived usefulness and attitude of customers regarding the use of ride-sharing apps. So the null hypothesis is rejected.
H3: There is no remarkable association that exists between a customers attitude towards using the ride-sharing app and their intention to use it.
The results of Table 18(a) indicate a strong evidence against the null hypothesis, “There is no remarkable association exists between customer’s attitude towards using the ride sharing app and their intention to use it”, because “t” value is positive and greater than one.
The outcomes demonstrated in Table 18(b) express that there is a remarkable association that exists between attitude and the intention to use ride-sharing apps. So the null hypothesis is rejected.
H4: There is no remarkable association that exists between age and a customer’s intention to use a ride-sharing app.
The consequences in Table 19(a) express that there is no remarkable association that exists between the age of participants and their intention to use the ride-sharing app. Because here the P-value is = 0.814 > 0.05, based on the statement the null hypothesis is true. In this research, the age category (above 50) has the top most mean of 12.09, and the age category of (16–25) has the minimum mean of 2.01.
H5: There is no remarkable association that exists between the level of technology-using skill and intention to use ride-sharing apps.
The consequences in Table 20(a) and (b) show that there is no remarkable association that exists between level of technology-using skill and their intention to use ride-sharing apps. Because here the P-value is P = 0.252 > 0.05. On that account, we reject the null hypothesis, which expresses that there is a strong positive association between the level of technology-using skill and the intention to use a ride-sharing app, and we accept the alternative hypothesis. The participants in the beginner level of technology using skills formed the highest mean of 8.21; at the same time, the respondents with expert skills comprised the lowest mean of 6.81.
H6: There is no remarkable association that exists between customer satisfaction and the attitude of respondents towards ride-sharing apps.
The consequence of Table 21(a) expressed a strong evidence against the null hypothesis based on “t” value results.
Outcomes in Table 21(b) disclose that there is a remarkable positive association that exists between customer satisfaction and customer attitude regarding the use of ride-sharing apps. Particularly, the P-value is (P = 0.00 < 0.05). The beta value (r = 0.999512075) also indicates that there is a strong favorable association that exists between attitude and customer satisfaction. So the null hypothesis is rejected.
H7: There is no remarkable association between social influence and the intention of customers towards using ride-sharing apps.
The outcomes of Table 22(a) showed a strong evidence against the null hypothesis because “t” value is greater than one.
Consequences in Table 22(b) disclose that there is a remarkable association that exists between social impact and intention of customers regarding the use of ride-sharing apps, especially the P-value of the research is (P = 0.00 < 0.05). The beta value (r = 1) also discloses that there is a strong association that exists between intention and social impact. So the null hypothesis is rejected.
Null H8: There is a significant relationship that exists between the number of trips and earnings through the ride-sharing platform.
Here the author used simple linear regression model.
Y = a + bX = 777.68 + (407.60*X)
Y1 = 777.68 + (407.60*3) = 2000.48
Y2 = 777.68 + (407.60*5) = 2482.68
Y3 = 777.68 + (407.60*8) = 4038.48
Y4 = 777.68 + (407.60*10) = 4853.68
Y5 = 777.68 + (407.60*15) = 6891.68
There is a positive relationship that exists between the number of rides and average earnings per day. If the number of rides increases, average earnings through the ride-sharing platform are slightly increasing. If the number of rides is 5 or less, then the predicted earnings are less than the predetermined earning. If the number of rides is 8 or more, then the predicted earnings are more than the predetermined earnings. This is because the fare of rides fluctuates in Bangladesh. Traffic jam, damaged of road, strike may enhance the fare of ride.
Null H9: The ride-sharing platform has no significant impact on socio-economic development in Bangladesh.
Here the author used an independent t-test
Calculated t-value = 0.158048855
Table value t = 0.00000
If the absolute value of the t-value is greater than the critical value, we reject the null hypothesis. So the null hypothesis, Ride-sharing platform has no impact on socio-economic development in Bangladesh, is rejected or not true. There is a significant impact of ride-sharing platforms on the socio-economic development of Bangladesh.
Goodness of fit-test
Null H10: There is no significant association between gender and their attitude towards the safety of ride-sharing platforms.
Here the author used the chi-square test.
The calculated chi-square value is 8.138165857.
At the 0.05 level of significance and 1 degree of freedom, the table value of the chi-square value is 3.841.
So the chi-square cal > chi-square table.
So the null hypothesis, that there is no significant association between gender and their attitude towards the safety of ride-sharing platforms, is rejected or not true. So there is a significant association that exists between gender and their attitude towards the safety of ride-sharing platforms. In Bangladesh, females still feel unsecured to travel by ride-sharing app alone.
Null H11: There is no significant association between employment status and their attitude towards the benefit of ride-sharing apps.
Here the author used the chi-square test.
The calculated chi-square value is 0.386791334.
At the 0.05 level of significance and 1 degree of freedom, the table value of the chi-square value is 3.841.
So the chi-square cal < chi-square table.
So the null hypothesis, there is no significant association between employment status and their attitude towards the benefit of ride-sharing apps, is not rejected; it is accepted. So ride-sharing apps are likely to be same beneficial to both the employed and unemployed people.
Null H12: There is no significant association between residential status and their attitude towards the benefit of ride-sharing apps.
Here the author used the chi-square test.
The calculated chi-square value is 28.92233844.
At the 0.05 level of significance and 1 degree of freedom, the table value of the chi-square value is 3.841.
So the chi-square cal > chi-square table.
So the null hypothesis, that there is no significant association between residential status and their attitude towards the benefit of ride-sharing apps, is rejected or not true. So there is a significant association that exists between residential status and their attitude towards the benefit of ride-sharing apps. Urban people have very much positive attitude towards ride-sharing apps than rural people in Bangladesh.
Null H13: There is no significant association between marital status and their attitude towards the socio-economic benefit of ride-sharing apps.
Here the author used the chi-square test.
The calculated chi-square value is 5.184188874.
At the 0.05 level of significance and 4 degrees of freedom, the table value of the chi-square value is 9.488.
So the chi-square cal < chi-square table.
So the null hypothesis, that there is no significant association between marital status and their attitude towards the socio-economic benefit of ride-sharing apps is accepted or true.
Null H14: There is no significant association between education level and mindset of respondents towards ride-sharing platforms.
Here the author used the chi-square test.
The calculated chi-square value is 18.7616696.
At the 0.05 level of significance and 8 degrees of freedom, the table value of the chi-square value is 15.507.
So the chi-square cal > chi-square table.
So the null hypothesis, that there is no significant association between education level and mindset of customers towards ride-sharing platforms, is rejected or not true. So there is a significant association that exists between the education level and mindset of respondents towards ride-sharing platforms.
H15: There is no significant association between education level and trust towards ride-sharing apps.
Here the author used the chi-square test.
The calculated chi-square value is 3.73108276.
At the 0.05 level of significance and 4 degrees of freedom, the table value of the chi-square value is 9.488.
So the chi-square cal < chi-square table.
So the null hypothesis, that there is no significant association between education level and trust towards ride-sharing apps, is accepted or true.
H16: There is no significant association between type of occupation and their mindset towards ride-sharing apps.
Here the author used the chi-square test.
The calculated chi-square value is 41.52517564.
At the 0.05 level of significance and 20 degrees of freedom, the table value of the chi-square value is 31.410.
So the chi-square cal > chi-square table.
So the null hypothesis, that there is no significant association between type of occupation and their mindset towards ride-sharing apps, is rejected or not true. So, there is a significant association that exists between type of occupation and their mindset towards ride-sharing apps.
Validity test
Content validity
For content validity test purposes, the author gives a questionnaire to customers.
The content validity rate = (Ne - N/2)/(N/2).
where Ne = number of experts voting essential
N = total number of recruited experts.
Now, the content validity rate = 0.38.
This formula yields values which range from +1 to −1; positive values indicate that at least half the respondents rated the item as essential. So here, half of the participants agreed that the ride-sharing platform is essential for earning and transport.
Convergent validity
To measure convergent validity the author used 2 related scale such as make profit through ride-sharing platform and number of ride making.
Here the author used the Pearson r-test, calculated r = 0.801326827.
So the hypothesis is adequate.
Discriminant validity
For testing discriminant validity, the author asked respondents to fill in a second questionnaire measuring self-esteem in order to test the discriminant validity of his questionnaire. Here the author tries to construct a relationship between the customer’s self-esteem and mindset towards ride-sharing platforms.
Here the author used the Pearson r-test, calculated r = −0.012298471.
The author gets the r-value is negative. It also seems to match his/her expectation about the relationship of the constructs, which is good.
Discussion and analysis
In this research, Table 4 discloses 6 demographic attributes such as gender, residence, employment status, education level, and marital status of the respondents. Table 5 presents the role of ride-sharing apps as a socio-technology platform in Bangladesh. The result of Table 5 proves that ride-sharing apps act as a strong socio-technology platform in Bangladesh. The impact of ride-sharing apps on the social and economic development of Bangladesh is analyzed in Table 6. The consequence of Table 6 showed that ride-sharing platforms play an important role in social-economic development in Bangladesh.
Table 4. Demographic attributes of the respondents.
Table 5. Role of ride-sharing app as a socio-technology platform.
Table 6. Impact of ride-sharing platform on socio-economic development in Bangladesh.
In Table 1, the author classified the respondents according to their age. The research showed that young people are the highest number of respondents in this area.
In Table 2, respondents are classified according to their education level. The consequences of Table 3 showed that no educated or graduate people are the highest number of respondents in these area; higher education level people are very low.
And in Table 3, respondents are classified according to their occupation. The result of the table showed that students are the highest number of respondents in this research.
The perceived ease of use of the ride-sharing app is disclosed in Table 7. The result of Table 7 showed that maximum respondents agreed that perceived ease of use of ride-sharing apps is very high. The perceived usefulness of the ride-sharing app is analyzed in Table 8. The results of this table showed that maximum respondents agreed that ride-sharing app is very useful. The perceived benefit of the ride-sharing app is disclosed in Table 9. In Table 10, the author disclosed the attitude of customers towards the ride-sharing apps. The consequence of Table 11 showed that maximum respondents have a positive intention towards ride-sharing apps.
Table 7. Perceived ease of use ride-sharing app.
Table 8. Perceived usefulness of ride-sharing app.
Table 9. Perceived benefit of ride-sharing app.
Table 10. Customer’s attitude towards ride-sharing app.
Table 11. Customer’s intention towards ride-sharing app.
The social influence of the ride-sharing app is disclosed in Table 12. The customers satisfaction towards the ride-sharing app is analyzed in Table 13. Table 14 showed that the safety of ride-sharing apps. The trust towards the ride-sharing app is disclosed in Table 15.
Table 12. Social influence of ride-sharing app.
Table 13. Customer satisfaction towards ride-sharing platform.
Table 14. Safety of ride-sharing app.
Table 15. Trust of ride-sharing app.
The outcomes of the “t” value of Table 16(a) proved that the difference between the means of the two groups (Perceived Ease of use & Attitude of customers) is relatively large compared to the variability within the groups, indicating a potential significant difference.
Table 16a. One way ANOVA test between perceived ease of use and attitude of customers.
The findings of the research [Table 16(b)] disclose that there is a strong association that exists between perceived ease of use and customer attitude towards ride-sharing apps. The interviewees of Bangladesh admits that they found the ride-sharing app easy to use. This author also proved that perceived ease of use has an optimistic impact on the attitude of customers towards the ride-sharing app.
Table 16b. Correlation between Perceived ease of use & Attitude.
The research also found that the greater the magnitude of “t” value, which indicate the greater the evidence against the null hypothesis – “there is no remarkable association exists between perceived usefulness and attitude of Customers regarding ride sharing app” [Table 17(a)].
Table 17a. One way ANOVA test between perceived usefulness and attitude of Customers.
This research proved that perceived ease of use affects the attitude and the intention to use a ride-sharing app (results of the research disclose [Table 17(b)] that there is a strong favorable connection that exists between perceived usefulness and the attitude of respondents regarding ride-sharing apps.
Table 17b. Correlation between Perceived usefulness & Attitude.
The outcomes of Table 18(a) established an strong evidence against the null hypothesis, “there is no remarkable association exists between customer’s attitude towards using the ride sharing app and their intention to use it” based on “t” value.”
Table 18a. One way ANOVA test between customer’s attitude towards using the ride sharing app.
The research found that that there is a remarkable association exists between attitude and the intention to use ride sharing app [Table 18(b)].
Table 18b. Correlation between Intention to use & Attitude.
The respondents authenticate that using a ride-sharing app would magnify their safety in travel and transport; they also accept that the ride-sharing app has increased their earnings. Which indicates that the perceived usefulness has a great impact on the attitude of respondents with regard to utilizing the ride-sharing app.
The author disclosed that there is no remarkable association exists between age and the intention to use a ride sharing app [Table 19(a)].
Table 19a. F-statistic Test Table.
The research found that, the age category (Above 50) has the highest mean of 12.09 and the age category of (16–25) has the minimum mean of 2.01 indicates that the later group is predominantly young with a higher proportion of individuals at the lower end of the age range compared to older individuals [Table 19(b)].
Table 19b. Age group, Mean & SD of the respondents.
Again, the research expressed (Table 20) that there is a notable connection that exists between the level of technology-using skill and their intention to use a ride-sharing app.
Table 20a. One-way ANOVA test between the level of technology using skill and customer’s intention to use ride-sharing app.
Table 20b. Technology skill of the respondents.
In this research, the author used Likert scale questionnaire, chi-square test, regression analysis, validity test, goodness of fit test, and ANOVA test for data analysis purpose. The age of participants between 16 and 25 formed over one-third (29.16%). The respondents who have an intermediate level of technology-using skill are the maximum number of the respondents using the ride-sharing platform.
The research found that an strong evidence against the null hypothesis, “There is no remarkable association exists between customer satisfaction and the attitude of respondents towards ride sharing app” based on “t Value” which is greater than one [Table 21(a)].
Table 21a. One way ANOVA test between customer satisfaction and the attitude of respondents.
Table 21(b) showed that maximum respondents agreed that they are satisfied about the service provided by ride-sharing platforms in Bangladesh.
Table 21b. Correlation between Customer’s Satisfaction & Attitude.
The research showed that social influence positively correlated with the intention towards ride sharing app in Bangladesh [Table 22(a)].
Table 22a. One way ANOVA test between social influence and intention of Customers towards.
The research also established that [Table 22(b)] ride-sharing platforms have a great impact on the socio-economic development of Bangladesh.
Table 22b. Correlation between Social influence & Intention towards ride sharing app.
The research showed that if the drivers make more ride then they earn more. So number of drive and earning are proportionately related (Table 23).
Table 23. Frequency of the ride.
Female respondents have a safety awareness towards ride-sharing platforms than male respondents (Table 24). The consequence of Table 25 showed that the ride-sharing app benefited both the employed and unemployed people in Bangladesh. Urban people are very much used ride-sharing apps than rural people in Bangladesh (Table 26).
Table 24. Gender Vs Attitude.
Table 25. Employment Status Vs Customer Attitude.
Table 26. Residential status Vs Attitude.
The result of Table 27 is that people of all types of marital status are benefited from the ride-sharing platform. Different levels of educated people have different types of mindsets towards ride-sharing platforms. Highly educated people used ride-sharing apps as a mode of transport. On the other hand, people with below-graduate level are accepting ride-sharing apps as a source of income (Table 28).
Table 27. Marital Status Vs Attitude.
Table 28. Education level Vs Mindset.
In this paper, the author disclosed that (Table 29) there is no significant association that exists between education level and trust towards ride-sharing apps. So ride-sharing apps acquire the trust of Bangladesh people of all classes.
Table 29. Education level Vs Trust.
The research showed that, there is a significant association exists between type of occupation and their mind set towards ride sharing app in Bangladesh (Table 30). Here most of the students and drivers show business mindset towards ride sharing app.
Table 30. Occupation Vs Mindset.
The research found that ride-sharing platform are a major source of earning and transport in Bangladesh (Table 31).
Table 31. Content Validity test.
The research showed that there is a strong positive correlation exists between profit making and number of ride in Bangladesh (Table 32).
Table 32. Profit Vs Number of ride.
The research found that there is a negative correlation that exists between self-esteem and the mindset of customers towards ride-sharing platforms. So people holds high esteem do not want to be rideshare drivers (Table 33).
Table 33. Self esteem Vs Mindset.
Conclusion
This research objective is to recognize the socio-economic benefit and the chance of success of ride-sharing service in Bangladesh. This research executes a quantitative method of investigation where data is compiled by means of a survey questionnaire. Findings of this research disclosed that ride-sharing service is popular and trendy in Bangladesh because the ride-sharing app is the fastest and flexible commuting and a medium of earning.
The young and prudent people are used to the service because they are well equipped with the technologies.
The research found that ride-sharing platforms act as a socio-technology platform and boost the economy of Bangladesh. Ride-sharing platforms can decrease the number of vehicles on the roads through optimizing routes as well as increasing vehicle occupancy rates.
People can save money on parking and gas by using ride-sharing platform. Ride-sharing platforms connect people of distance places. In Bangladesh, if more people take part in ride sharing, the economy would improve. Again, ride-sharing platforms have a wider service area and availability time. In Bangladesh, many ride-sharing platforms operate 24/7 and often operate in areas where traditional taxis don’t 24/7. Again, ride-sharing platforms create job opportunities in Bangladesh.
Again, the research found that in Bangladesh, ride-sharing platforms are gaining passenger satisfaction, trust, and ensuring safety.
Beyond this, still, some people do not use ride-sharing services because of ignorance or lack of technological knowledge. Again, some people are anxious over safety, security, and loyalty issues of this service. Besides that large group of people choose ride-sharing service over conventional transportation systems due to convenient payment system, flexibility, source of earning, convenience, time saving, and ease of use.
Limitation
Although this study had some limitations. Here the researcher used a perception-based research method, which had a limited number of respondents. Again, the author considered an insubstantial number of samples to assess the socio-economic prospect as well as the benefit of the ride-sharing platform in Bangladesh.
Implication and recommendation
Accordingly, this research is beneficial for the existing and upcoming time service providers of any developing country. This research is also helpful for the organizations to comprehend their customers’ options and insights into the privileges and act of assistance.
While this is an investigative research in this field, more decisive investigation can be done in future. Additionally, the research can be launched to discover respondents’ intention to become ride sharers and to measure the impact of people’s activity to enhance the business of ride sharing.
Again, future researchers can also study about any other technology-based service like home delivery service, online coaching, or teaching, etc.
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