References
- Anwar, M., & Graham, M. (2020). Hidden transcripts of the gig economy: labour agency and the new art of resistance among African gig workers. Environment and Planning A: Economy and Space, 52(7), 1269–1291. https://doi.org/10.1177/0308518X19894584
- Bakardjieva, M. (2005). Internet society: The Internet in everyday life. Sage.
- Basukie, J., Wang, Y., & Li, S. (2020). Big data governance and algorithmic management in sharing economy platforms: A case of ridesharing in emerging markets. Technological Forecasting and Social Change, 161, 120310. https://doi.org/10.1016/j.techfore.2020.120310
- Bishop, S. (2019). Managing visibility on Youtube through algorithmic gossip. New Media & Society, 21(11–12), 2589–2606. https://doi.org/10.1177/1461444819854731
- Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. SAGE.
- Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on instagram. New Media & Society, 21(4), 895–913. https://doi.org/10.1177/1461444818815684
- Culpepper, P. D., & Thelen, K. (2020). Are we all amazon primed? Consumers and the politics of platform power. Comparative Political Studies, 53(2), 288–318. https://doi.org/10.1177/0010414019852687
- Cusumano, M., Gawer, A., & Yoffie, D. B. (2019). The business of platforms: Strategy in the age of digital competition, innovation, and power. Harper Business.
- Dancel, R. (2015, August 8). Manila’s Messy Rapid Transit. The Straits Times. http://www.straitstimes.com/opinion/manilas-messy-rapid-transit
- Gal, M. S., & Elkin-Koren, N. (2016). Algorithmic consumers. Harvard Journal of Law & Technology (Harvard JOLT), 30(2), 309–353.
- Gillespie, T. (2017). Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem. Information, Communication & Society, 20(1), 63–80. https://doi.org/10.1080/1369118X.2016.1199721
- Gupta, A., Saha, B., & Banerjee, P. (2018). Pricing decisions of car aggregation platforms in sharing economy: A developing Economy perspective. Journal of Revenue and Pricing Management, 17(5), 341–355. https://doi.org/10.1057/s41272-018-0145-1
- Jarrahi, M. H., Sutherland, W., Nelson, S. B., & Sawyer, S. (2019). Platformic management, boundary resources for gig work, and worker autonomy. Computer Supported Cooperative Work (CSCW), 29, 153–189. https://doi.org/10.1007/s10606-019-09368-7
- Just, N., & Latzer, M. (2017). Governance by algorithms: Reality construction by algorithmic selection on the internet. Media, Culture & Society, 39(2), 238–258. https://doi.org/10.1177/0163443716643157
- Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087
- Kubitschko, S. (2018). Acting on media technologies and infrastructures: Expanding the media as practice approach. Media, Culture & Society, 40(4), 629–635. https://doi.org/10.1177/0163443717706068
- Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 1603–1612). https://doi.org/10.1145/2702123.2702548
- Lehdonvirta, V. (2018). Flexibility in the gig economy: Managing time on three online piecework platforms. New Technology, Work and Employment, 33(1), 13–29. https://doi.org/10.1111/ntwe.12102
- Lladós-Masllorens, J., Meseguer-Artola, A., & Rodríguez-Ardura, I. (2020). Understanding peer-to-peer, two-sided digital marketplaces: Pricing lessons from Airbnb in Barcelona. Sustainability, 12(13), 5229. https://doi.org/10.3390/su12135229
- Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management and uber drivers’ autonomy. Proceedings of the International Conference on Information Systems 2017. International Conference on Information Systems, Seoul, South Korea.
- Murillo, D., Buckland, H., & Val, E. (2017). When the sharing economy becomes neoliberalism on steroids. Technological Forecasting and Social Change, 125, 66–76. https://doi.org/10.1016/j.techfore.2017.05.024
- Newlands, G. (2020). Algorithmic surveillance in the gig economy: The organization of work through lefebvrian conceived space. Organization Studies, https://doi.org/10.1177/0170840620937900
- Overseas Security Advisory Council (OSAC). (2015). Philippines 2015 Crime and Safety Report. https://www.osac.gov/pages/ContentReportDetails.aspx?cid=17461 [Accessed 9 Dec. 2016]
- Patton, M. (2002). Qualitative research and evaluation methods (3rd ed.). Sage Publications.
- Petre, C., Duffy, B. E., & Hund, E. (2019). “Gaming the system”: platform paternalism and the politics of algorithmic visibility. Social Media + Society, 5(4), https://doi.org/10.1177/2056305119879995
- Philippine Statistics Authority. (2015). Highlights of the Philippine Population 2015 Census of Population. https://psa.gov.ph/content/highlights-philippine-population-2015-census-population
- Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of uber’s drivers. International Journal of Communication, 10, 3758–3784.
- Shapiro, A. (2018). Between autonomy and control: Strategies of arbitrage in the “on-demand” economy. New Media & Society, 20(8), 2954–2971. https://doi.org/10.1177/1461444817738236
- Sun, P. (2019). Your order, their labor: An exploration of algorithms and laboring on food delivery platforms in China. Chinese Journal of Communication, 12(3), 308–323. https://doi.org/10.1080/17544750.2019.1583676
- Tassinari, A., & Maccarrone, V. (2017). The mobilisation of gig economy couriers in Italy: Some lessons for the trade union movement. Transfer: European Review of Labour and Research, 23(3), 353–357. https://doi.org/10.1177/1024258917713846
- Tong, B., & Gunter, U. (2020). Hedonic pricing and the sharing economy: How profile characteristics affect airbnb accommodation prices in barcelona, Madrid, and seville. Current Issues in Tourism, 0(0), 1–20. https://doi.org/10.1080/13683500.2020.1718619
- van Dijck, J. (2009). Users like you? Theorizing agency in user-generated content. Media, Culture & Society, 31(1), 41–58. https://doi.org/10.1177/0163443708098245
- Velkova, J., & Kaun, A. (2019). Algorithmic resistance: Media practices and the politics of repair. Information, Communication & Society, 0(0), 1–18. https://doi.org/10.1080/1369118X.2019.1657162
- Walker, M. (2020). Uber and the problem of regulatory arbitrage. In T. Dundon, & A. Wilkinson (Eds.), Case studies in work, employment and human resource management (pp. 90–94). Edward Elgar Publishing.
- Waze. (2016). Waze driver satisfaction index 2016. https://inbox-static.waze.com/driverindex.pdf
- Wood, A., & Lehdonvirta, V. (2019). Platform labour and structured antagonism: Understanding the origins of protest in the gig economy (SSRN Scholarly Paper ID 3357804). Social Science Research Network. https://papers.ssrn.com/abstract=3357804
- Wood, A. J., Lehdonvirta, V., & Graham, M. (2018). Workers of the Internet unite? Online freelancer organisation among remote gig economy workers in six Asian and African countries. New Technology, Work and Employment, 33(2), 95–112. https://doi.org/10.1111/ntwe.12112
- Zhu, F., & Iansiti, M. (2019). Why some platforms thrive and others don't. Harvard Business Review, January-February 2019. https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont