Collaborative Consumption and Uber’s Business Model

A decade ago, the advent of digital platforms ushered the beginning of a new era of a more sustainable economy to replace that of consumerism and individualism.  On these platforms, entrepreneurs gained the inherent capability to redesign the market identities regarding the consumers and owners. Instead of relying on the results of the massive production and distribution processes, the platform users are benefiting from the shared access of innovative products, services, and skills that have been put in common. Such platforms have enhanced the maximum utilization of resources, which could have been underutilized, which saves both time and money, a technology known as Collaborative Consumption (Somers, Dewit, Baelus, 2018; Barnes and Mattsson, 2016; Benoit et al., 2017). The following paper seeks to investigate the underlying technological disruptions that have favored collaborative consumption and the business model. Uber, being one of the most renowned companies globally has integrated collaborative consumption as its foundational business model, which has maximized its profitability, reliability, and efficiency.

Literature Review: The impact of new technology on collaborative consumption and Business Model

The concept of collaborative consumption is still new across different industries in the world. Despite the increased recognition that collaborative consumption is a significant field of research and the increased number of shared economic operations, there are very few peer-reviewed articles that integrate the title “shared economy” in the academic research databases (Nguyen and Llosa, 2018). 

Collaborative consumption is a typical disruption, which forms the basis of the success of famous companies, emerging from the traditional culture of the interdependence of the humankind.  Some of the technologies and factors that have increased the adoption and integration of collaborative consumption include the growth of internet applications, high penetration of smartphones, globalization, urbanization, a shift in the global economic trends, and increased attention towards sustainability(Cusumano, 2017; Möhlmann, 2015). Similarly, McGaughey (2018) and Basu (2017) imply that renowned companies such as Uber and Deliveroo maintain a positive growth rate because of the affirmative attitude that people possess towards the efficiency and reliability of these companies.

Various technological factors have enhanced the efficiency and reliability of collaborative consumption. Huckle et al., (2016) explained that the rise of digitalization and the internet-of-things (IoT) have recently managed to minimize the gaps of collaborative consumption. Many companies rely on the disruption of digital platforms such as mobile applications and websites in balancing the forces of supply and demand; and establish confidence and trust amongst the users (Cramer and Krueger, 2016). Some of the industries that integrate collaborative consumption include music and video streaming, finance and accommodation, car sharing, and online subscription. Gabel (2016) noted that the sectors mentioned above have the possibilities of increasing the global revenue from a statistical estimate of $15 Billion to $335 Billion by the year 2025. Hence, the benefits of collaborative consumption support its significance in business.

The basis of a collaborative consumption structure is the product service systems (PSS), which is also referred to as the service-oriented economy. Somers et al., (2018) explained that, in PSS business strategy, there is recognition of the significance of utilization where consumers incur only the costs of products usage and purpose and not for proprietorship. In a similar approach, Bressanelli et al., (2018) explain that Servitization, which is strongly affiliated to the circular economy paradigm, has promoted collaborative consumption. Such an approach closes existing loops by ensuring that the designed products are reusable, repairable, and manufactural because they will be created with the sole purpose of an extended lifetime (Camacho-Otero et al., 2018; Piscicelli et al., 2015).  Therefore, business models that integrate collaborative consumption not only provide the shift towards a circular economy by encouraging an extension in the lifetime of the products but also ensure their maximization and utilization by occupying the idle capacity.

Different authors expound on the foundational basis of collaborative consumption. PricewaterhouseCoopers (2017) identifies four core pillars that serve as the theoretical background (concepts) which facilitates collaborative consumption. The components include digital programs that link the demand and supply forces, relations that offer access over proprietorship, increased collaborative forms of consumption, and copyrighted experiences that can drive an emotional connection (Fournier et al., 2013 Brödner, 2018). On the contrary, Munoz and Cohen (2017) identified seven different types of aspects of the Collaborative Consumptions models, which are platforms for a shared economy, underutilized resources, technological reliance, alternative funding, collaborative governance, and peer-to-peer interactions. Reisch and Thøgersen (2015) and Lamberton and Rose (2012) continued to expound on the four main dimensions for SE activities that entail the circulation of goods, maximization of the exploitation of resilient products, communal utilization of assets as well as the exchange of services.  The three researchers agree that collaborative consumption is a platform of the shared economy and is founded on technology and the maximization of exploitation of tangible assets.

Some of the technological innovations that will disrupt the collaborative consumption include GPS, Big Data and Machine learning. Blazquez and Domenech (2018) acknowledged that the trio helps in matching up owners with their renters in reality.  For instance, GPS is essential in the acquisition of the user location data and the resources within the area (Zheng et al., 2012). Similarly, the service provider depends on secure payment gateway to help in the easy and safe money transfer. Consequently, the algorithms are integrated into decision-making where organizations can get relevant data and customize their service platforms according to the user’s needs (Chen et al., 2017; Smichowski, 2016). Therefore, technologies that exhibit flexibility of navigation allow participants to access shared services without geographical limitations.

Company Strategies

Uber is a global multinational company, headquartered in California, and prides itself in many years of successful operations and development of an innovative disruption model. The firm operates in more than 630 cities on a global level, and earned revenues of $7.5 billion in 2017 (Uber Technologies Inc., 2018).  Caruso (2017) explains that the company provides a worldwide network for balancing the forces of demand and supply in that customers can request a ride. Uber Technologies Inc., (2018) outline the four groups of services the company provides: (1) Economy class services that include Uber X, Uber XL, and UberSelect; (2) Premium services that include Uber black, UberSuv, and UberLux; (3) Accessibility services that cater for people in wheelchairs; and (4) Carpool services for shared ride. Uber services are reliable and straightforward (Michael, 2016). The author explained that the process of requesting assistance included accessing the Uber application on the device, setting the pick-up location, asking for a car, getting picked up, and paying using supported funds transfer services. However, Laurell and Sandström (2016) explain challenges such as poor city transport infrastructure, traffic jams, unreliable drivers, and resilient customers that affect the efficiency of service offered by the company.

Uber entails a massive database of drivers to cater to clients’ requests promptly. Jones et al., (2016) discussed the efficiency of Uber algorithm whereby the client is matched with the next available driver in less than a minute. Rauch and Schleicher (2015) proceeded to explain that the algorithm enables the company to acquire and store data on the number of trips completed. The process will help to predict the forces of demand and supply and setting equilibrium prices (Jones et al., 2016). According to Basu (2017), the company also analyses how transportation is handled across cities to adjust for potential challenges and constraints. Zysman and Kenney (2018) explained another significant purpose of the algorithm, which is, to gather data on its drivers on the aspects of speed, unique data about the vehicle, and their locality. The company uses personalized data in an anonymized and aggregated fashion to monitor the features of the services that are occasionally used to analyze the frequency and patterns of service utilization. Dreyer et al., (2017) affirmed that the outcomes aid the company is focusing on areas that will promote the business process. Such vital information is essential for industry analysis and other statistics.

The company exhibits some operational policies that affect customer satisfaction. One of the most significant aspects is the inflation of prices in an unpredicted approach (Uber Technologies Inc., 2018). The above challenge is attributed to the use integration of big data. Jordan (2017) highlighted that Uber leverages predictive analytics to real-time traffic patterns, the forces of demand and supply. The mentioned strategy affects the rate of demand which results in an increased reduction in the customer base over time. The second challenge facing Uber is maintaining the privacy of information collected through big data technology. In 2015, Uber announced that information belonging to approximately 50,000 of drivers was hacked from their database (Zhen, 2018). Similarly, Rio (2016) noted that a similar breach of private documents happened in 2016 where the company lost approximately 57 million customers and drivers. Uber officially reported that 815,000 Canadian drivers and riders were affected in 2016 through malicious hacking that exposed user information (The Canadian Press, 2017). Uber needs to use new and innovative ways to sustain a high customer and driver base. The approach will maximize the consumer’s utility and remain profitable.  Machine learning is the most appropriate strategy to apply to big data analytics to sustain Uber’s operations.

Uber has adopted machine learning on different levels of operations. For example, Uber Eats has integrated technologies to estimate the delivery time of the meals through computation of time, the distance between the customer and the restaurant, and the average time needed to prepare the food (Shmueli, 2017 Gnimpieba, Z.D.R., Nait-Sidi-Moh, A., Durand, D., Fortin, J., (2015). Uber Eats Artificial Intelligence (AI) takes the delivery time for thousands of meals and predict different aspects of distribution (Breidbach et al., 2018). Other insightful facts from Machine Learning includes the company’s ability to analyze the previous data on the patterns of successful and unsuccessful processes (Dezi et al., 2018; Cusumano, 2015). Integration of big data solutions will revolutionize Uber regarding fraud detection and prevention (Zysman and Kenney, 2018). There are many examples of fraudulent behaviors that affect the operations of the company. Standard practices include payment fraud, incentive abuse, and compromised accounts. Such drawbacks have not only damaged the reputation of Uber but also affected the confidence of customers on the services offered by the company.  According to the SWOT analysis, Uber, appendix 2, one of its significant weaknesses is the increased scandals that have damaged the reputation of the brand as well as increased losses because of fraudulent behaviors and cybercrime.

Issues at Uber that new technology can be applied to improve the practice

One of the areas which could be developed through new technology is the company’s endless driver issues. First, the fact that Uber drivers usually pick and drop customers whom they know nothing about during all times of the day has caused fears of insecurity among them. Furthermore, there has been controversy about the ability of the company to handle the safety of both their customers. New technology could be used to improve the situation and assure both the drivers and consumers that once they are in the organization’s vehicles they will be safe and that should anything happen; the firm will effectively handle it.

There have been complaints that the majority of the company’s drivers are not experienced, do not have proper driving qualifications, and ill-mannered The corporation has also been accused of only concentrating on increasing the number of its drivers instead of even considering the quality and training levels of its employees. In the end, the customers end up being harassed by the drivers, and this tarnishes its reputation. Furthermore, the company cannot retain its clients if most of the time they get dissatisfied with the behaviors of their drivers (Chan, 2017). Therefore, there is an urgent need for a technological solution that can help ensure that the drivers are well trained, mannered, and have excellent customer care skills.

Customer care issues at Uber are also one of the areas which can be addressed using technology. Over the past years, there have been complaints that the company does not completely respond to its customer’s complaints and issues, a factor that has destroyed its reputation. Therefore, there is an urgent need for technological options which can help solve the problem and help the company fix its image. There is a need for a prompt response to clients’ complaints, and this can be achieved through the proper application of the technological solution.

Approaches or methods of new technology application

To address the issues at Uber, there are different methods of new technology that can be applied to find amicable solutions. The first approach involves employees or driver training using the e-learning technologies. Offering the drivers with online corporate training through e-learning is an essential step in ensuring that they learn about its corporate culture, excellent customer care services, and gain driving skills and experiences (Navimipour & Zareie, 2015). E-learning technologies are also highly customizable, and this can help the firm’s HR department to purchase software and then customize it to reflect its employee needs.

There are different technological approaches which Uber can use to address and increase customer experience and in the process reduce complaints and help build its image. Some of the methods include the use of artificial indigence (AI), virtual reality (VR), instant messaging, social media, and blockchain technologies. Although AI has been widely used in other industries, its use in the taxi business is still at it its preliminary stages, and any company that successfully implement it to solve customer related issues will be an industry disruptor. The technology will enable the company to empower its customer support conversations and in the process improves its decision-making capabilities through automation. The company will use the technology deliver to its customers proactive and actionable responses in a quick manner and the process improving their satisfaction. Through Business to Consumer (B2C) capacities, the AI can assist firms to chat with their customers in a secure manner using mobile devices (Russell, Dewey & Tegmark, 2015). The Virtual reality (VR) technology can help Uber to engage its consumers in a better way because the expertise was designed to assist in providing a complete sensory customer experience. The technology also allows customers to interact with brands in a better way and as such delights them. The ability to make its customers delighted is the first step at ensuring that clients achieve maximum satisfaction and have positive reviews about the firm.

Communication with the customers is an essential solution to the client dissatisfaction. According to Brunton et al., (2017), it can be achieved through real-time or instant messaging technologies. The company currently heavily relies on email as the primary response mode but this is time consuming, and some customers even fail to check their emails regularly. Technologies such as the HubSpot which provides shared inbox tools allows all the customer’s incoming messages to be collected and assigned instantly. The technology also provides for an on-site chat with customers, and this will amplify its communication strategies and convince them to use the Uber services again in the future.

Procedures and operations involved in obtaining new technology and its estimated outcomes

There are some operational activities and processes needed to acquire e-learning, AI, VR, and real-time messaging technologies.  First, the company will have to obtain licenses from the manufacturers of these technologies because using the techniques without the approval of their respective owners may lead to lawsuits and litigations. Most of the methods are patented and as such must be legally obtained.  The certificates may be applied for online and paid for through the same. The firm must, therefore, set aside adequate resources they shall use to purchase the new technologies. However, this may reduce its profits in the short run but shall recover the entire amount once the new technologies start producing benefits to its operations and improving the efficiency of activities.

The integration of the technologies into an integrated software system is also one of the most critical operations needed to realize their full potential. Having the techniques separately may be costly for the company to run and an integrated package will provide a cheaper alternative solution (Brougham & Haar, 2018). Furthermore, Integrated Software Applications will increase process efficiency, increased visibility, and improved IT time. The Uber employees may also become more innovative and ultimately accelerate their company growth.

Assessment and evaluation of factors that influence the application of the new technology and recommendation

The use of e-learning, AI, and instant messaging technologies in organizations will be affected by several factors. According to Mohammadyari and Singh (2015), e-learning adoption among Uber drivers may be influenced by their attitudes to new knowledge, the flexibility of e-learning packages, perceived use of the technology, and how the drivers understand the usefulness of the course contents. In this case, some of the materials that may be included in the e-learning technology comprise road safety, customer service skills, communication and negotiation skills, and even hygiene among others.

The availability of financial resources to purchase licenses of the new technologies will also affect their acquisition and implementation. Inadequate capital may restrict the company from fully obtaining the suggested techniques. However, since the company has been on good financial health over the past two years, it is assumed that it has the necessary resources to buy the new technologies. The recommendation, therefore, is that the company must put aside enough resources, both human, financial, and physical possessions to acquire to the licenses and learn how to use them with a view of meeting customer needs and convincing them to continue using the company’s services.

The adoption of instant messaging systems will primarily be affected by the perceptions of customers about the technology and shall involve perceived usefulness, enjoyment, and ease of use. Therefore, it is recommended that the firm must only purchase technologies which are considered as easy and fun to use by its clients.

Recommendations

Uber should adopt all the suggested technologies including the e-learning, AI, instant messaging, and virtual reality so that it can become more competitive and profitable. The adoption of instant messaging systems and AI will help in addressing customer complaints promptly and as such shall increase consumer satisfaction which in turn shall lead to repeat purchases. The technology, therefore, will allow the firm to keep loyal customer base and expand its market share. As the company’s share in the market increase, its effectiveness and competiveness will also increase in the same measure. The adoption of e-learning technologies will improve the competency of the company’s drivers and in the process will teach them about the need for excellent customer services. Furthermore, the drivers will contend with their work when they feel more comfortable with their skills. The productivity shall therefore improve, and the company will be more competitive in the taxi business industry.

Conclusion

Collaborative consumption is an essential form of technological disruption across all industries. The technologies such as big data analytics may produce significant potential to cause a revolution within the field of collaborative consumption by increasing its efficiency and reliability. Uber is one of the leading corporations in the taxi industry but has been experiencing problems such as increasing customer dissatisfaction with its services, heightened safety concerns among its drivers and clients, and inability to deliver high quality services.  The company can, however, use technologies such as e-learning, AI, VR, and real-time messaging technologies to change its position and become more competitive. The expected outcomes of synchronising these actions would be increased customer services and satisfaction hence improved customer loyalty and market share.

References

Barnes, S.J. and Mattsson, J., (2016) ‘Understanding current and future issues in collaborative consumption: A four-stage Delphi study’, Technological Forecasting and Social Change, 104, pp.200-211.

Basu, B., (2017) ‘UBER: Redefining Transportation’, International Journal, 5(3), 39-42.

Benoit, S., Baker, T. L., Bolton, R. N., Gruber, T., & Kandampully, J. (2017) ‘A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors’, Journal of Business Research, 79, 219-227. https://doi.org/10.1016/j.jbusres.2017.05.004

Blazquez, D., & Domenech, J. (2018) ‘Big Data sources and methods for social and economic analyses’, Technological Forecasting and Social Change, 130(1), 99-113. https://doi.org/10.1016/j.techfore.2017.07.027

Boulos, M. N. K., Wilson, J. T., & Clauson, K. A. (2018) ‘Geospatial blockchain: promises, challenges, and scenarios in health and healthcare’, Available at: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0144-x?utm_source=Other_website&utm_medium=Website_links&utm_content=DaiDen-BMC-International_Journal_of_Health_Geographics-Multidisciplinary-China&utm_campaign=AJH_USG_BSCN_DD_IJHealthGeo

Bradley, K., & Pargman, D. (2017) ‘The sharing economy as the commons of the 21st century’, Cambridge Journal of Regions, Economy and Society, 10(2), 231-247.

Breidbach, C., Choi, S., Ellway, B., Keating, B. W., Kormusheva, K., Kowalkowski, C.,  & Maglio, P. (2018) ‘Operating without operations: how is technology changing the role of the firm?’ Journal of Service Management, 2(5), 1-12.

Bressanelli, G., Adrodegari, F., Perona, M., & Saccani, N. (2018), ‘Exploring How Usage-Focused Business Models Enable Circular Economy through Digital Technologies’, Sustainability, 10(3), 639.

Brödner, P. (2018). “Super-intelligent” machine: technological exuberance or the road to subjection. AI & SOCIETY, 33(3), 335-346.

Brougham, D., & Haar, J. (2018) ‘Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace’, Journal of Management & Organization, 24(2), 239-257.

Brunton, M., Eweje, G., Taskin, N., (2017) ‘Communicating corporate social responsibility to internal stakeholders: walking the walk or just talking the talk? Bus. Strategy Environ’, 26, 31–48. real

Camacho-Otero, J., Boks, C., Pettersen, I., (2018) ‘Consumption in the Circular Economy: A Literature Review’, Sustainability, 10, 2758.

Smichowski, B. C. (2016) ‘Data as a common in the sharing economy: a general policy proposal’, Available at: https://hal.archives-ouvertes.fr/hal-01386644/document

Caruso, L. (2017) ‘Digital innovation and the fourth industrial revolution: epochal social changes?’, AI & SOCIETY, 33(3), 1-14.

Chan, C. Y. (2017) ‘Advancements, prospects, and impacts of automated driving systems’, International journal of transportation science and technology, 6(3), 208-216.

Chen, M. K., Chevalier, J. A., Rossi, P. E., & Oehlsen, E. (2017). The value of flexible work: Evidence from Uber drivers(No. w23296). National Bureau of Economic Research, Available at: http://www.anderson.ucla.edu/faculty_pages/keith.chen/papers/Final_NBER17.pdf

Cramer, J., Krueger, A.B.,(2016) ‘Disruptive change in the taxi business: The case of Uber’, Am. Econ. Rev. 106, 177–82.

Cusumano, M.A., (2017), ‘The sharing economy meets reality’, Commun. ACM 61, 26–28.

Cusumano, M.A., (2015) ‘How traditional firms must compete in the sharing economy’, Commun. ACM 58, 32–34.

Dezi, L., Santoro, G., Gabteni, H., Pellicelli, A.C., (2018) ‘The role of big data in shaping ambidextrous business process management.’ Bus. Process Manag. J. 24, 1163.

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’, Technol. Forecast. Soc. Change 125, 87–104.

Fournier, S., Eckhardt, G., Bardhi, F., (2013) ‘Learning to play in the new ‘share economy’ Harv. Bus. Rev. 91, 125–129.

Gabel, D., (2016) ‘Uber and the persistence of market power’, J. Econ. Issues 50, 527–534.

Gnimpieba, Z.D.R., Nait-Sidi-Moh, A., Durand, D., Fortin, J., (2015. Using Internet of Things technologies for a collaborative supply chain: Application to tracking of pallets and containers. Procedia Comput. Sci. 56, 550–557.

Huckle, S., Bhattacharya, R., White, M., Beloff, N., (2016) ‘Internet of things, blockchain and shared economy applications’, Procedia Comput. Sci. 98, 461–466.

Jesse, N., (2016) ‘Internet of Things and Big Data–The Disruption of the Value Chain and the Rise of New Software Ecosystems’, IFAC-Pap. 49, 275–282.

Jones, P., Hillier, D., Comfort, D., (2016) ‘Sustainability in the hospitality industry: Some personal reflections on corporate challenges and research agendas’, Int. J. Contemp. Hosp. Manag. 28, 36–67.

Jordan, J.M., (2017) ‘Challenges to large-scale digital organization: the case of Uber. J. Organ’, Des. 6, 11. Available at: https://link.springer.com/article/10.1186/s41469-017-0021-2

Lamberton, C.P., Rose, R.L., (2012) ‘When is ours better than mine? A framework for understanding and altering participation in commercial sharing systems’, J. Mark. 76, 109–125.

Laurell, C., & Sandström, C. (2016) ‘Analysing Uber in social media—disruptive technology or institutional disruption?’, International Journal of Innovation Management, 20(05), pp. 1640013.

McGaughey, E., (2018) ‘Uber, the Taylor Review, Mutuality and the Duty Not to Misrepresent Employment Status’, Ind. Law J.

Michael, K., (2016a) ‘When Uber Cars Become Driverless:\“ They Won’t Need No Driver\”. IEEE Technol. Soc. Mag. 35, 5–10.

Michael, K., (2016b) ‘When Uber Cars Become Driverless: \”They Won’t Need No Driver\” [Editorial]. IEEE Technol. Soc. Mag. 35, 5–10. https://doi.org/10.1109/MTS.2016.2554444

Mohammadyari, S., & Singh, H. (2015) ‘Understanding the effect of e-learning on individual performance: The role of digital literacy’, Computers & Education, 82, 11-25.

Möhlmann, M., (2015) ‘Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again’, J. Consum. Behav. 14, 193–207.

Munoz, P., Cohen, B.D., (2017) ‘Mapping Out the Sharing Economy: A Configurational Approach to Sharing Business Modeling (SSRN Scholarly Paper No. ID 2959941)’ Social Science Research Network, Rochester, NY.

Navimipour, N. J., & Zareie, B. (2015) ‘A model for assessing the impact of e-learning systems on employees’ satisfaction’, Computers in Human Behavior, 53, 475-485.

Nguyen, S., Llosa, S., (2018) ‘On the difficulty to define the sharing economy and collaborative consumption – literature review and proposing a different approach with the introduction of “collaborative services 8’

Piscicelli, L., Cooper, T., Fisher, T., (2015) ‘The role of values in collaborative consumption: insights from a product-service system for lending and borrowing in the UK. J. Clean’, Prod. 97, 21–29.

PricewaterhouseCoopers, (2017) ‘PwC’ [WWW Document]. PwC. URL Available at: https://www.pwc.com/gx/en.html (accessed 10.26.18).

Quinson, E. (2015) ‘Collaborative consumption through new technologies’, Available at: https://www.theseus.fi/bitstream/handle/10024/92504/FINAL%20WORD%20CORRECTED.pdf?sequence=1&isAllowed=y

Ranjbari, M., Morales-Alonso, G., Carrasco-Gallego, R., (2018) ‘Conceptualizing the Sharing Economy through Presenting a Comprehensive Framework’, Sustainability 10, 2336.

Rauch, D.E., Schleicher, D., (2015) ‘Like Uber, but for local government law: the future of local regulation of the sharing economy’, Ohio St LJ 76, 901.

Reisch, L., Thøgersen, J., (2015) ‘Handbook of Research on Sustainable Consumption’, Edward Elgar Publishing. https://doi.org/10.4337/9781783471270

Rio, M., (2016) ‘Black Mobility Matters: An Exploratory Study of Uber, Hacking, and the Commons in Baltimore’, Architecture_MPS 10, 3–29.

Russell, S., Dewey, D., & Tegmark, M. (2015) ‘Research priorities for robust and beneficial artificial intelligence’, Ai Magazine, 36(4), 105-114.

Shmueli, G., (2017) ‘Analyzing Behavioral Big Data: Methodological, practical, ethical, and moral issues’, Qual. Eng. 29, 57–74.

Somers, L., Dewit, I., Baelus, C., (2018) ‘Understanding product-service systems in a sharing economy context: a literature review’, In: Procedia CIRP. pp. 173–178.

The Canadian Press, (2017) ‘Uber says 815,000 Canadians affected by data breach as investigation launched’, CBC News [WWW Document]. Available at: https://www.cbc.ca/news/technology/uber-data-breach-how-many-1.4444088 (accessed 10.26.18).

Uber Technologies Inc., (2018) ‘Uber - Earn Money by Driving or Get a Ride Now [WWW Document]’, Available at: URL https://www.uber.com (accessed 10.26.18).

Zhen, Y. (2018). Case Study of the Uber Failure in China: A Technoethical Analysis. Available at: https://ruor.uottawa.ca/bitstream/10393/37781/1/Zhen_Yu_2018_research%20paper.pdf

Zheng, V.W., Zheng, Y., Xie, X., Yang, Q., (2012) ‘Towards mobile intelligence: Learning from GPS history data for collaborative recommendation’, Artif. Intell. 184, 17–37.

Zysman, J., & Kenney, M. (2018) ‘The next phase in the digital revolution: intelligent tools, platforms, growth, employment’, Communications of the ACM, 61(2), 54-63.

Appendix

Appendix 1: Weekly Journal Entries

Week 1

The focus on the first week was a study of different journals and articles to investigate the concept of collaborative consumption. The study was insightful by expounding on collaborative consumption as a shared economy, which is a result of globalization, the growth of the internet, urbanization, and digitalization (Nguyen and Llosa, 2018). Many researchers and scholars correlated in the belief that collaborative consumption was a disruptive innovation, which has enabled the match of demand and supply at a global level (Bradley and Pargman, 2017; Quinson, 2015; Ranjbari et al., 2018). The literature search identified finance and accommodation, car sharing, and online staffing as some of the businesses that practice collaborative consumption and have the capability to increase global revenues (Cramer and Krueger, 2016; Gabel, 2016; Huckle et al., 2016). McGaughey (2018) and Basu (2017) mentioned Uber and Airbnb as some of the most renowned companies that thrive on collaborative consumption.

Week 2

The main objective for the second week of study was to identify issues where collaborative consumption as a technology can be applied. The product service systems (PSS) forms the foundation of collaborative consumption. PSS led to a realization that collaborative research lies in th