Correspondence to : muhammad.ashur@umi.ac.id Received: 24 July 2022 Accepted: 20 May 2022 Published: 1 June 2022 JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI Vol.24, No. 3, 2022, pp. 7 - 14 Published online in http://jos.unsoed.ac.id/index.php/jame ISSN: 1410-9336 / E-ISSN: 2620-8482 INTRODUCTION The most influencing factor of the rapid growth of Sharing Economy (SE) service sector has always been technology and its features. This business model has experienced dramatical rise and popularity in Indonesia (Ashoer, Murdifin, Basalamah, As’ad, & Pramukti, 2021), owing primarily to an increase in on- demand applications (Selloni, 2017). SE is a component of e-commerce in which economic activity consisting of assets and service systems is shared or implemented between individuals. Many research findings are still using terms like collaborative consumption, collaborative economy, on-demand economy, peer-to-peer economy, zero marginal cost economy, and crowd-based capitalism to describe the SE concept (Dreyer, Lüdeke- Freund, Hamann, & Faccer, 2017). In practice, the SE application platform is considered unstable and will continue to grow because it is still relatively new. This viewpoint is supported by the growth of several service providers, including Uber, Gojek, Grab, AirBnB, Traveloka, and others. According to (Li & Fang, 2022), SE businesses can save money and increase convenience is probably the biggest benefit for consumers. This service offers consumers the ability to compare prices quickly, which gives them the ability to choose cheaper options and save on expenses (Hamari, Sjöklint, & Ukkonen, 2016). In terms of convenience, many of the goods and services exchanged are provided on demand, currently, (Barnes & Mattsson, 2016) the most noticeable savings come from reducing travel and transportation costs. The benefits of sharing economy (SE) application services as a support for the availability of access to consumer needs and desires became an interesting phenomenon during the pandemic, particularly among Millennials (generation Z) (Möhlmann, 2015). Many previous studies have shown how quickly the younger generation adapts to modernization and even adopts technologies that can make their daily activities more convenient. Surprisingly, during the Covid-19 virus pandemic, young Indonesians’ online shopping habits has changed. Katadata Insight Center (KIC) online survey of 1,146 respondents aged 18-29 years from Jabodetabek, Surabaya, Medan, Bandung, Makassar, Semarang, Denpasar, and Yogyakarta reported that before the pandemic, as many as 60.3% of young people said they had never shopped online, and the rest claimed it was very rare to shop online in the new normal during the pandemic, all Student’s Purchase Intention in Sharing Economy: Empirical Study during New Normal Muhammad Ashoer1, Imaduddin2, Jayadi3, Jafar Basalamah4 1Fakultas Ekonomi dan Bisnis, Universitas Muslim Indonesia, Makassar, Indonesia Abstract This study aims to investigate how essential product quality, service quality, discounts, and perceived ease of use that influence students’ intention in using sharing economy (SE) application during pandemic period. An online survey was conducted by directly scattering e- questionnaires link generated from google formulir to collect primary data from active students in Makassar, Indonesia. We selected 132 respondents (Grab users) who have met the judgment sampling and.then analyzed the collected primary data with structural equation modeling (SEM) and partial least squares (PLS). The result demonstrated that there was positive and significant relationship between service quality, discounts, perceived ease of use and purchase intention. Meanwhile, product quality were found to be not significant in predicting students’ behavior. Practically, the Grabs’ management should reconfirm and reassure the product quality that the vendor offers via their apps to stimulate users’s buying especially in the Covid-19 crisis. Theoritically, some potential constructs needs to revisit to enrich the current SE studies. Keywords Purchase Intention, Sharing Economy, Mobile Application, PLS-SEM JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 respondents reported switching to online transactions. Young people who frequently purchase necessities online increased by 39.5 percent, while those who rarely or very rarely use the service increased by 39.2 percent and 21.4 percent, respectively (Andrea Lidwina, 2021). As a result, this raises fundamental questions about the importance of predicting young consumers’ behavior in using SE applications during the pandemic (Pazaitis, De Filippi, & Kostakis, 2017). This study offers several contributions, first, the provision of insights on the application of the TAM, TPB, and UTAUT theories in a developing country that is experiencing economic recovery during a pandemic. By incorporating the constructs and these three underpinning theories, this study empirically investigates the highly-sought phenomenon of the development of SE from an students perspective. Second, to provide a reference and expansion of scientific insights and concepts about topics affecting students’ purchasing intention in SE context (Li & Fang, 2022). The findings of this study are expected to serve as a resource for the next researcher conducting research on student purchasing preferences. This study is expected to be evaluated and considered as a reference for service improvements and innovations in sharing economy application in emerging economies. THEORITICAL REVIEW AND HYPOTHESES DEVELOPMENTS Sharing Economy The sharing economy is an economic model characterized as a peer-to-peer (P2P) activity of acquiring, providing, or sharing access to products and services, which is typically enabled through a community-based online platform (Belk, 2014). For thousands of years, communities have shared the use of assets, but the Internet and its use of big data have made it easier for asset owners and individuals wishing to use those assets to find each other. This dynamic is also known as the shareconomy, collaborative consumerism, collaborative economy, and peer economy (Akbari et al., 2022). Sharing economies enable people and organizations to profit from underutilized assets such as parked automobiles and unused rooms. Retamal (2019) concludes that there are many factors that indicate prospects for mainstreaming of cooperative consumption; e.g., high density of individuals, restricted housing sizes, traffic jams, and long commutation times. However, the study conjointly shows that there's resistance, significantly to shared-access practices like shared laundry machines or automotive sharing. The resistance stems from a lack of trust, a lack of data on a way to use the shared instrumentation, and a lack of institutional support for consumer-to-consumer business models. Antecedents of SE Purchase Intention The quality of a product, as perceived by consumers, varies from one individual to the next. Kotler et al. (2018) explain that product is the most fundamental element of the marketing mix, as consumers expect it to satisfy their needs and desires. Product quality is the state of a product based on research into its conformance with defined measurement standards. The assessment of a product’s quality will be more accurate the more applicable the criteria are. In SE context, the majority of users will interact with both the trader offering the product or service and the platform linking them. Moreover, ensuring that consumers comprehend the function of each party when they experience the apps and that they understand who to contact if difficulties emerge (Belk, 2014). Several previous research have proven that digital products offered on mobile apps play a significant role in stimulating consumers purchase intention (Narasimhan et al., 2018). Therefore, we hypothesize: H1: Product quality had a significant impact on SE purchase intention Kotler et al. (2018) define service quality as the effort made to satisfy the wants and needs of consumers, as well as the precision of delivery to meet their expectations. Because services that are directly experienced by consumers will be promptly evaluated based on whether they meet or fail to meet consumer expectations and ratings, it must explicitly reflect the requirements and expectations of consumers. In general, services are economic activities with intangible outputs that are provided by service providers, such as businesses, to service users or consumers. In SE business model, a high quality service refer to the perfection of several aspect such as application performance, riders, customer service, and others (Lovelock & Wirtz, 2011). JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 If the performance of the service meets the expectation, the customer perceives the service quality to be good, and vice versa. Therefore, a company’s service quality rests on its capacity to consistently meet consumer expectations. The authors previously reported that the perceived impact of service quality on users generates prospects for future purchases (Bilsen et al., 2019; Tong & Su, 2018). We then propose the second hypothesis as follow: H2: Service quality had a significant impact on SE purchase intention A discount is a price that is lower than the price that should be paid due to a number of factors, including early payment, quantity and quantity of purchases, and purchases made during specific seasons (Modding et al., 2022). There are currently several discounts available on all products and services that are traded. The existence of discounts offered by each company in the promotion of their products increases consumer interest in owning and purchasing these items. Kotler et al. (2018) describe a discount as an immediate price reduction for a purchase made within a specified time frame. On the basis of this explanation, it is clear that discounts are direct price reductions for specific products during a specific time period, as well as the sale of discounted prices by the seller to the consumer. Each company’s discounting is influenced by its rules and aims. Möhlmann (2015) found that savings enhances the probability of satisfaction with the service provided. In peer-to-peer sharing, lesser the expenditure, more the consumer will be motivated to engage in apps. Most of the studies have found the beneficial influence of economic benefit on intention to buy among SE consumers (Hamari et al., 2016; Möhlmann, 2015). Based on the findings of these investigations, we can formulate the hypothesis: H3: Discount had a significant impact on SE purchase intention Davis (1989) firstly elaborate ease of use as a person’s perception of a technology’s effortlessness. It also represents a person’s impression that a system is straightforward and valuable for decision-makers (Pavlou, 2003). People will likely embrace an easy-to- use information system when they believe the information system is satisfying. The ease of use element includes everything from the main way to online transaction protests (Mohamed et al., 2014). Prospective buyers often have trouble choosing based on their demands and cancel their purchases since they do not know how until the transaction procedure. Potential purchasers who know how to shop and transact online may also try. From the preceding description, it can be stated that ease of use while buying goods or services online is highly significant. With the many conveniences provided by online sellers, consumers may engage more readily, get information easily, and not be confused or uncomfortable so taht they can keep customers happy (Modding et al., 2022). In a empirical study to evaluate the consumer potential of collaborative consumption among Indonesia consumers, some researcher concluded that sharing economy consumers need certain digital skills in order to improve their buying intention (Dreyer et al., 2017). Furthermore, some studies claimed that the requirement for computer skill is needed, but it is unclear if this is a measure of perceived ease of use or something else related to technology use (Daunorienė et al., 2015). Based on the above argumentation, we posit that: H4 Ease of use had a significant impact on SE purchase intention RESEARCH METHODS Design This study takes place under causality research because it attempts to investigate the causal relationship between product quality, service quality, discounts, and ease of use and millennial consumers' purchasing interests. All students that take courses in Makassar have been chosen as the research population. The primary consideration for researchers in selecting a location is that students are currently the largest segment of consumers in the e-commerce market. As this number is uncountable, and to overcome the limitations of constraints (cost, effort and time) then judgement sampling techniques was applied. This technique concerns the use of subjective criteria or considerations in order to accurately screen respondents and obtain representatives who are relevant to the purpose of the research (Hair et al., 2019). Some of the criteria are as follows: 1) active students (not on leave or suspension); 2) knowledge and experience with the sharing JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 economy application, such as Grab; and 3) students who have used Grab services at least once in the last three months (research period). During the data collection process, the researcher is speeding up the online survey by utilizing the electronic signature feature of Google form (Google Drive). A link is created that contains information or a variable item, and it is then distributed via email and social media platforms such as Whatsapp. This method is highly recommended by experts for reducing Covid-19 through physical control of conventional of questionnaire. Aside from that, online surveys are popular because the costs are low, they are easy to use (if you have access to the internet), and they can address privacy and security concerns (Hamzah et al., 2021). During the data collection period, which lasted from October 2021 to January 2022, 209 records were added to the database. After the diversification process was completed, there were 132 responses (63.1 percent of the total responses) that were suitable for inclusion in the study. In addition, the number of samples reduces the power of the analysis model used, which is the Structural Equation Model (SEM) based on variables (PLS). Data Measurement This research is a causality study that aims to test the influence of variable product quality, service quality, discounts, and ease of use on se consumer purchasing interests. In addition, research also aims to predict and develop a theory, not to confirm a grand theory. Therefore, the proper analysis technique used is a variant-based structural equation model (SEM) or partial least square (PLS) – hereinafter called PLS-SEM. PLS-SEM aims to test predictive relationships between constructs by seeing if there is a relationship or influence between the constructs (Hair et al., 2019). PLS-SEM as a prediction model does not assume a specific distribution to estimate parameters that predict causality relationships. However, parametric techniques for testing the significance of parameters are not required and evaluation models for prediction are non-parametric. The PLS evaluation model is carried out by assessing the outer model and the inner model (Memon et al., 2021). In order to analyze this study by means of the results of questionnaire data is managed with the help of statistics to calculate the numbers obtained. This analysis is then processed so that it can be measured how quantitative influences between each variable accurately. RESULTS AND DISCUSSION Respondents The sample is a Grab customer that selected based on the criteria of the previous purposive sampling technique. After the data collection process with an online survey, the respondents who were netted in this study amounted to 132 millennial consumers. In terms of gender, the data showed that the most respondents were women as many as 80 people (61%) and men as many as 52 people (39%). Furthermore, from the age category, the most respondents were at the age of 21- 23 years as many as 85 people (64%) and at the age of 17-20 years as many as 47 (36%). Finally, 121 millennial consumers (91.6%) or the majority admitted to using GrabFood and GrabExpress services to meet their needs and desires during the pandemic. This demographic can be the initial capital of reassignment of the SE company's strategy to increase the buying intentions of young consumers. Outer Model Evaluation This phase is used to determine the model's validity and reliability. For latent construct- forming indicators, measurement models with reflexive indicators are evaluated using convergent and discriminant validity, as well as composite reliability (CR) and cronbach alpha (CA) for their indicator items. Each test has its own assessment criteria, which refer to the lowest threshold value that has been established or agreed upon in social research. Based on the results of the tests, it is possible to conclude that all items that measure product quality variables, service quality, discounts, ease of use, and purchasing interest are worth more than 0.7, and this value is greater than the distribution of values on cross loading. As a result, both convergent and discriminant validity are confirmed. Furthermore, the test results also found that the values of AVE, CA, and CR were greater than the threshold values. With this, discriminant reliability is accepted. The outer model evaluations are depicted on Table 1, 2, and 3. Table 1. Cross Loading Items KP KPE DIS KPEL MB KP1 0,867 0,619 0,698 0,511 0,449 KP2 0,869 0,598 0,663 0,507 0,415 JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 KP3 0,814 0,622 0,721 0,595 0,369 KP4 0,831 0,591 0,677 0,486 0,376 KP5 0,809 0,602 0,650 0,535 0,442 KPE1 0,608 0,824 0,642 0,515 0,538 KPE2 0,570 0,764 0,726 0,641 0,506 KPE3 0,471 0,846 0,658 0,624 0,449 KPE4 0,462 0,758 0,731 0,539 0,379 KPE5 0,543 0,794 0,675 0,566 0,478 DIS1 0,570 0,528 0,796 0,477 0,409 DIS2 0,618 0,647 0,869 0,501 0,573 DIS3 0,451 0,456 0,738 0,490 0,307 DIS4 0,504 0,546 0,834 0,507 0,322 DIS5 0,585 0,537 0,855 0,610 0,421 KPEL1 0,553 0,707 0,645 0,842 0,461 KPEL2 0,535 0,698 0,696 0,811 0,418 KPEL3 0,526 0,697 0,722 0,822 0,453 KPEL4 0,657 0,762 0,678 0,853 0,536 KPEL5 0,538 0,708 0,658 0,844 0,532 MB1 0,389 0,462 0,402 0,416 0,775 MB2 0,406 0,509 0,530 0,439 0,855 MB3 0,399 0,498 0,470 0,366 0,840 MB4 0,465 0,452 0,500 0,438 0,758 MB5 0,458 0,512 0,457 0,353 0,851 Note: DIS=Discount; KPE=Ease of use; KPEL=Service quality; KP:=Product quality; MB=Purchase Intention; AVE=Average variance extracted Source: Output SmartPLS 3.0 Table 2. Latent Variabel Correlations (Fornell-Larcker Criterion) DIS KPE KPEL KP MB VIF DIS 0,782 2,179 KPE 0,672 0,821 2,757 KPEL 0,676 0,557 0,833 2,591 KP 0,627 0,623 0,612 0,796 2,314 MB 0,519 0,596 0,580 0,492 0,884 2,225 Source: Source: Output SmartPLS 3.0 Table 3. AVE, Composite Reliability (CR), Cronbach Alpha (CA) AVE CR CA DIS 0,672 0,911 0,879 KPE 0,636 0,897 0,857 KPEL 0,696 0,920 0,891 KP 0,703 0,922 0,894 MB 0,667 0,909 0,875 Source: Source: Output SmartPLS 3.0 Inner Model Evaluation The first structural model assessment in PLS analysis involves the Goodness of Fit Model, which refers to the Q-Square predictive relevance (Q2). The number of variances of the construct described by the model is represented by the PLS R-Square value. According to the test results, the endogenous variable R-square of purchase intent was worth 0.390 (39%) and was relatively low. This can illustrate that 39 percent of the variation in purchasing intention variables can be explained by product quality variables, service quality, discounts, and ease of use, or in other words, 39 percent of the variation in purchasing intention variables can be explained by product quality variables, service quality, discounts, and ease of use, while the remaining 61 percent is explained by another variable contribution that was not discussed in this study. Figure 1. The Result of PLS-SEM Analysis Table 4. Results Of Hypothesis Testing Direct Effect Path T Stat. p Sig. JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 KP → MB 0,104 0,960 0,339 Not Sig. KPEL → MB 0,393 3,694 0,000 ** DIS → MB 0,481 4,433 0,000 ** KPE → MB 0,312 3,099 0,002 * Note: The sig. symbol indicates the level of significance; **α<0.01; *α<0.05; Not significant α > 0.05 Source: Source: Output SmartPLS 3.0 The output indicates that the SE application had a positive but insignificant influence on the influence of product quality on the purchasing interests of young consumers. Previous research (Bilsen et al., 2019) contradicts this finding. The average value of respondents' responses also supported this finding, as they considered the quality of products offered by Grab to be average. These millennial consumers also believe that buying directly from a store or app does not affect the taste of food because food is provided by a third party (Tong & Su, 2018). Instead, when freshness, cleanliness, and other factors are considered, the quality of food or drinks ordered will decrease. On the other hand, Covid-19 conditions also have a strong impact on consumer preferences and behavior (Boyer & Hult, 2006) because they are considered potentially exposed to a high risk of virus transmission when package services are provided. The results also showed that there was a positive and significant influence on the influence of service quality on the purchasing interests of young consumers through the SE application. This result is in line with previous research (Tong & Su, 2018). These findings prove that the Grab app provides service certainty amid conditions of high uncertainty during the pandemic. When booking the motor mode transportation service, Grab Bike, consumers are satisfied with the hospitality and comfort applied by the driver during the trip. In addition, drivers are also considered sprightly in responding to consumer requests, such as confirming orders according to the application, or changing travel routes. The results showed that there was a positive and significant influence on the influence of discounting (promotion) on the buying interest of young consumers through the SE application. This result is in line with previous research (Möhlmann, 2015). Discount offers are the variable whose influence is highest on consumer purchase intentions, so it is considered very important by respondents. These findings confirm that millennials (or at least parents who bear their expenses) do feel satisfaction with grab's discounted offers and this is a positive sentiment for consumers because they can recommend the Grab app to their parents or friends. The findings revealed a positive and significant influence of ease of use perception on the purchasing interests of young consumers via the SE application. This finding is consistent with previous studies (Dreyer et al., 2017). This is certainly a familiar thing in the theory of technology acceptance and adoption that consumers tend to buy when the platform is simple to use. It has also been implemented by the Grab app, so when customers order food, they can track the location of their orders, contact drivers if there is a change in order, and so on. In addition, the Grab application also has an attractive user interface which pampers the user’s eyes, such as layout design, themes and icons. In terms of accessibility, this decacorn brand are significantly reliable to support consumers’ operations and daily activities like studying, working, relaxing, or business. Hence, these advantages drive consumers’ intention to order directly through the application, wherever they are, or whatever they are doing. CONCLUSION Referring to previous exposures, variable service quality, discounts, and ease of use perception play a crucial role in stimulating millennial consumer purchasing interest in the sharing economy. However, there was no significant influence on the relationship between product quality and buying interest. In terms of practical implications, Grab is expected to establish intense communication with its business partners to strive to improve the quality of food by presenting new and interesting menus. Grab is also obliged to strictly select every vendor who wants to enter and use Grab services to maintain the quality of service. Another recommendation involves consideration of pandemic conditions where Grab is required to continue to maintain the safety of drivers and consumers when providing services. From the theoretical aspect, this study provides several recommendations for future improvements. First, the variables raised are still very JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 common so that subsequent research can add new variables and / or indicators to enrich the sharing economy research model such as social presence, Word-of-Mouth (WoM), and loyalty. Second, this study only targets the millennial consumer segment in one area. Future research needs to expand populations and samples to get more comprehensive generalizations. Finally, leveling up in designing research methods may be required, for example mediation, moderation, and multi- group analysis (MGA) tests. REFERENCES Akbari, M., Foroudi, P., Khodayari, M., Zaman Fashami, R., Shahabaldini parizi, Z., & Shahriari, E. (2022). Sharing Your Assets: A Holistic Review of Sharing Economy. Journal of Business Research, 140, 604–625. https://doi.org/10.1016/j.jbusres.2021.11 .027 Ali Memon, M., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F., & Huei Cham, T. (2021). PLS-SEM STATISTICAL PROGRAMS: A REVIEW. Journal of Applied Structural Equation Modeling, 5(1), 2590–4221. https://doi.org/10.47263/JASEM.5(1)06 Andrea Lidwina. (2021, June). Pola Belanja Online di Kalangan Anak Muda Berubah saat Pandemi. Retrieved March 15, 2022, from https://databoks.katadata.co.id/datapubl ish/2021/06/03/pola-belanja-online-di- kalangan-anak-muda-berubah-saat- pandemi Ashoer, M., Murdifin, I., Basalamah, J., As’ad, A., & Pramukti, A. (2021). Integrating Social Commerce Constructs into Mobile Application Service; A Structural Equation Model. Journal of Physics: Conference Series, 1783(1). https://doi.org/10.1088/1742- 6596/1783/1/012043 Barnes, S. J., & Mattsson, J. (2016). Understanding current and future issues in collaborative consumption: A four- stage Delphi study. Technological Forecasting and Social Change, 104, 200–211. https://doi.org/10.1016/j.techfore.2016.0 1.006 Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595–1600. https://doi.org/10.1016/J.JBUSRES.201 3.10.001 Bilsen, B., Emrah, Ö., Erdoğan, K., & Oğuz, A. M. (2019). An Investigation of Augmented Reality Applications from the Perspectives of Brand Trust and Purchase Intentions of Customers. In S. Grima, E. Özen, H. Boz, J. Spiteri, & E. Thalassinos (Eds.), Contemporary Issues in Behavioral Finance (Vol. 101, pp. 53–64). Emerald Publishing Limited. https://doi.org/10.1108/S1569- 375920190000101005 Boyer, K. K., & Hult, G. T. M. (2006). Customer behavioral intentions for online purchases: An examination of fulfillment method and customer experience level. Journal of Operations Management, 24(2), 124–147. Daunorienė, A., Drakšaitė, A., Snieška, V., & Valodkienė, G. (2015). Evaluating Sustainability of Sharing Economy Business Models. Procedia - Social and Behavioral Sciences, 213, 836–841. https://doi.org/10.1016/j.sbspro.2015.11 .486 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008 Dreyer, B., Lüdeke-Freund, F., Hamann, R., & Faccer, K. (2017). Upsides and downsides of the sharing economy: Collaborative consumption business models’ stakeholder value impacts and their relationship to context. Technological Forecasting and Social Change, 125, 87–104. https://doi.org/10.1016/J.TECHFORE.2 017.03.036 Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018- 0203 Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67(9), 2047– 2059. https://doi.org/10.1002/ASI.23552 Hamzah, M. N., Ashoer, M., & Hamzah, N. (2021). Impact of Islamic work values on in-role performance: PERSPECTIVE from Muslim employee in Indonesia. JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI , Vol. 24, No. 3, 2022, pp.7-14 Problems and Perspectives in Management, 19(4), 446–457. https://doi.org/10.21511/PPM.19(4).202 1.36 Kotler, P., Armstrong, G., & Opresnik, M. O. (2018). Principles of Marketing 17th Ed. (17th Globa). Harlow: Pearson Education, Inc. Li, C. Y., & Fang, Y. H. (2022). The more we get together, the more we can save? A transaction cost perspective. International Journal of Information Management, 62. https://doi.org/10.1016/j.ijinfomgt.2021.1 02434 Lovelock, C., & Wirtz, J. (2011). Services Marketing: People, Technology, Strategy (7th ed.). Upper Saddle River, New Jersey: Prentice Hall. Modding, B., Ashoer, M., & Basalamah, J. (2022). Esensi Pemasaran (1st ed.). Bandung: Media Sains Indonesia. Mohamed, N., Hussein, R., Hidayah Ahmad Zamzuri, N., & Haghshenas, H. (2014). Insights into individual’s online shopping continuance intention. Industrial Management & Data Systems, 114(9), 1453–1476. https://doi.org/10.1108/IMDS-07-2014- 0201 Möhlmann, M. (2015). Collaborative consumption: Determinants of satisfaction and the likelihood of using a sharing economy option again. Journal of Consumer Behaviour, 14(3), 193–207. https://doi.org/10.1002/CB.1512 Narasimhan, C., Papatla, P., Jiang, B., Kopalle, P. K., Messinger, P. R., Moorthy, S., … Zhu, T. (2018). Sharing Economy: Review of Current Research and Future Directions. Customer Needs and Solutions, 5(1–2), 93–106. https://doi.org/10.1007/S40547-017- 0079-6 Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce. https://doi.org/10.1080/10864415.2003. 11044275 Pazaitis, A., De Filippi, P., & Kostakis, V. (2017). Blockchain and value systems in the sharing economy: The illustrative case of Backfeed. Technological Forecasting and Social Change, 125, 105–115. https://doi.org/10.1016/j.techfore.2017.0 5.025 Selloni, D. (2017). New Forms of Economies: Sharing Economy, Collaborative Consumption, Peer-to-Peer Economy BT - CoDesign for Public-Interest Services. CoDesign for Public-Interest Services, 15–26. Tong, X., & Su, J. (2018). Exploring young consumers’ trust and purchase intention of organic cotton apparel. Journal of Consumer Marketing, 35(5), 522–532. https://doi.org/10.1108/JCM-04-2017- 2176.