• Conduct research on Sharing Economy e-Business platforms such as Uber www.uber.com and airbnb www.airbnb.co.uk.
• Research on “Crowd-sourcing” platforms supporting micro-work such as Amazon’s Mechanical Turk; explain a scenario about how crowd-sourcing platform can be used to semi-automate marketing related tasks that involve humans and IT systems. Discuss barriers that need to be tackled so that the automation scenario described can be effectively executed.
Conduct research on Sharing Economy e-Business platforms such as Uber www.uber.com and airbnb www.airbnb.co.uk.
“Competitive Threats acting on the e-Business”1 framework of Uber’s business
Uber has left his own success in the hands of the economic drivers (Shaw, Horton and Chen 2011). Its noted that the internet competitors of Uber claim that the company make use of dodgy, methods and even the Uber brand ambassador has requested for the pressing or either cancelling the drivers that defect the Uber, and the company has tried to respond back about the marketing tactics, which prevent the drivers from working at Uber (Shaw, Horton and Chen 2011). In the past few years, Uber is impacted by its competitors on terms of employee’s background check and the security features in its cab that has resulted into many crimes in different parts of the world (Akerlof 1970).
The rivals of the company have made the fundraising efforts through giving warning to the investors that Uber should raise its own capital through pushing the investment (Ariely 2010). It is suggested by the executives of the company that they should consider spending around $1 million in order to dig up the dirt over its media critics (Ariely 2010). Even the company is challenged by its competitors on the grounds of ethics, as many ethical issues have occurred in America for Uber (Ariely 2010). The company has tried to create the monopoly in the cab service industry, and Uber was the first company that came under the antitrust scrutiny (Ariely and Jones 2008).
Through Uber e-business they try to create the digital monopoly in the market, so they try to dominate both local and global market (Ariely and Jones 2008). The company achieves this monopoly through its search results and related advertisements sales, but through this company is not actually getting any direct revenue and they have to compete with wide range of people in the overall internet advertising market (Richard 2014). Next argument relates with the effect of network and the benefits of scale that create barriers for new entrants in the market (Richard 2014).
Components of the framework to structure the discussions about each factor affecting Uber
The Uber app includes the driver and the fleet management, along with social interaction among the passenger and the drivers, payment as well as taxi hailing (Axelrod 2013). This kind of threat tries to pose with the incumbent providers of taxi services, users and the regulators that reside in transfer of control on various aspects related to the industry like allocation of work, pricing, as well as discrimination to whoever actually controls the software (Axelrod 2013). There are certain other features like taxes, insurance, fuel, as well as maintenance and repairs that create lot of difference between the Uber services and its competitors (Axelrod 2013).
The employees working in the company are actually not happy with the low profit margin, which might lead to bad publicity of the company and competitions threat, as employees can leave their jobs to join their competitive group (Yochai 2004). Uber is also facing threat from the local authorities, due to its bad PR in many countries (Chris 2008). It’s true that due to increasing competition in the taxi services the prices will definitely go down that will result into the loss of customers, and the revenue will also get reduced (Chris 2008). As the company has appointed new employees and is entering into new market, they are facing more fraud and scandals that damage the brand and the same will impact the e-business of the company that has created monopoly in the digital world (Chris 2008). Another competitive threat possess by Uber is that they don’t follow the regulations through which the traditional drivers were abided, and this has resulted into the skirting fine (Chris 2008). Therefore, negative publicity also impacts the online business of the company (Buchanan 1999).
Competitive threats affecting Uber and suggesting initiatives to counter the threats
Uber faces threat from various factors like regulations of the government, taxi permits in different countries, initial investment made by the company, increasing competitors in the market, and the PR value of the company. It is suggested that the company should focus over the sharing economy concept in their company, as it will help them in maintaining the collaborative work in the company and employees will also responsibly handle their job in effective ways. The company should also try to bring innovative ways in enhancing their service and even focus over the customer security through its online services. This will help them in building their reputation and monopoly in the global market.
Social impact of Sharing Economy e-Business platforms
Sharing economy has many names, like, peer-to-peer, collaborative production, access economy, mesh, collaborative assumption, or production based on common peer, but it’s true that all the definitions will most likely to encompass and remain elusive (Juliet 2014). In the case of this report, all the innovative models or either technologies or e-business platforms are referred as sharing economy (Juliet 2014). The sharing economy is referred as the broad concept for the emerging business models, exchanges as well as platforms (Ipeirotis, Provost and Wang 2010). Sharing economy relates with the sharing of knowledge about the services and goods for exchanging them (Ipeirotis, Provost and Wang 2010). All this exchange is leveraged through the cheap as well as ubiquitous knowledge that could be easily made available through the help of disruptive technology (Ipeirotis, Provost and Wang 2010).
The different characteristics cover up decentralized exchange along with the self governance, focus over accessing the resource ownership, as well as companies that are becoming exchange facilitator, instead of acting as the producers (Ipeirotis, Provost and Wang 2010). It’s true that sharing economy is rapidly expanding. The peer-to-peer consumer rental market is calculated to around $26 billion, and the increase in share economy is calculated to around 25% of the people residing in the UK, who are following the collaborative activities based on the internet (Ipeirotis, Provost and Wang 2010). The above analysis depicts the consequences of potential revolution from the perspective of sharing economy development (Kulkarni, Can and Hartmann 2011). It’s evident that modern economy is full of huge capacity, and there are many idle resources lying in the modern world, these goods are that are not used, and the labor, which is not purchased by anyone (Kulkarni, Can and Hartmann 2011). In huge part of the excess capacity is actually due to the substantial cost of transaction that includes the use of resources (Kulkarni, Can and Hartmann 2011).
Sharing economy holds different social impacts and the same could be understood from the perspective of e-business platforms of Uber and Airbnb (Kulkarni, Can and Hartmann 2011). Uber takes the benefits of underutilized resources such as people who require work and idle cars that could be used for the purpose of matching the same with the people demand that require a ride (Little, Chilton and Miller 2009). It decreases the matching prices to around zero and even minimizes the overhead, which includes exchange from both sides, such as creating the services of full car sharing (Little, Chilton and Miller 2009). Likewise, there is another e-business company named Open Shed that employs the idle resources to the place, where it is considered as economically valuable. Airbnb make use of idle resources for the people, who are looking for the room for residing (Little, Chilton and Miller 2009).
From my perspective, the concept of sharing economy has tried to bring various technological changes such as computing power, constant access to the internet, along with satellite technology for the consumers, but at the same time it make use of traditional technologies such as tools, cars and houses (Little, Chilton and Miller 2009). There exists a possibility of including the principles of sharing economy along with other disruptive new technologies that is quite important from the viewpoint of society (Little, Chilton and Miller 2009). For example, how the business model of sharing economy could make use of nascent technologies like 3D printers or either consumer level drones. Actually, no answer is proposed for it, but it is expected that the entrepreneurs might come across these questions in near future (Little, Chilton and Miller 2009). Even the sharing economy has holds the same importance for the industrial companies (Little, Chilton and Miller 2009).
The theory of Ronald Coase’s of the company also posits that companies are actually built with an aim to minimize the cost of transaction (Le, Edmonds, Hester and Biewald 2010). The 20th century is in various ways considered as century of the company, in which huge corporate entities could harness the scale of economy in order to push down the provision of less efficient services that is outside the company (Le, Edmonds, Hester and Biewald 2010). It is evident that if the sharing economy tries to push the cost of transaction, then in that case how the efficient companies will get restructured, but sharing economy usually gets more efficient, when they become large (Le, Edmonds, Hester and Biewald 2010). In other industries there are economies of scale, but it’s noted that traditional scale of economy will get rise with the excess capacity through enhancing the set of resources in the economy (Le, Edmonds, Hester and Biewald 2010).
The social impact of sharing economy is that it can lead to the production of low cost goods, and the excess capacity get rise (Benjamin and Michael 2014). It’s also noted that through sharing economy economies of scale might get reduce with the excess economic capacity (Benjamin and Michael 2014). A debate has already taken place in San Francisco regarding sharing economy, and this term includes the extensive range of both offline activities as well as digital platforms from the financial companies such as Airbnb that offers services of peer-to-peer lodging to the small initiatives like tool libraries and repair collection (Benjamin and Michael 2014).
There are many companies who are eagerly trying to set up under the term ‘big tent’ in the sharing economy, as it holds the positive symbol of sharing, along with magnetism technologies of innovative digital as well as rapidly increasing volume of sharing activities (Benjamin and Michael 2014). While the question that arise is that this boost of sharing economy will rule this sector, and it is claimed that whether sharing economy is of low carbon, fairer, socially connected, transparent or either participatory (Benjamin and Michael 2014). It’s really a challenge how one could harness the sharing economy in order to increase the wealth. Sharing economy is leaving behind social impact, as Airbnbs along with their venture capitalist are trying to siphon off towards more value (Benjamin and Michael 2014). It even impact the exploitation of the labor, bring inequality for the minority communities as well as low income group, impact the regulation and taxation status that engage with the attendees, perverse the ecological impacts, lead the race towards the bottom dynamics in the coming years (Kleiner 2002).
Most of the sharing economy websites or either the e-business platforms tries to advertise the green credentials and there are many users who actually care about the lying ecological effects (Alan 2013). The ecological advantages related to sharing economy is set as quite obvious, as secondary markets minimize the demands for having new goods; therefore, its footprints might go down (Alan 2013). Airbnb mention that staying in the lying homes might decrease the demand for new hotels, just like the tool sharing that could minimize the purchase of new tools (Airbnb, ‘A greener way to travel: the environmental impacts of home sharing’ 2014). However, despite of the huge belief about the sector that supports in reducing the emission of carbon, there exist no comprehensive studies related to this impact (Airbnb, ‘A greener way to travel: the environmental impacts of home sharing’ 2014).
In the recent study related to the car sharing, it is explored that measurable minimization in the emission of greenhouse gas, but the same has happen due to the reduction of small household fraction. For most of the car sharing has enhanced the emission (Airbnb, ‘A greener way to travel: the environmental impacts of home sharing’ 2014). The basic assumption is related to the ecological effects that are made about the visible shifts through the consumers in purchasing the products, instead of new products or either staying within the private houses instead of hotels (Ostrom 1990). In order to assess the entire social effects related to the sharing economy, it is important to focus over ripple impacts, and need to understand what is done by the seller or host with the money earned through sharing economy e-business platforms (Ostrom 1990). They might make use of this money for purchasing the products that has high impact. Another question is that does the market appearance for using the products would lead towards the people for purchasing new items that they are interested in selling out (Ostrom 1990).
It’s evident that if the travelling becomes less costly, do people travel more, and all this can increase the carbon footprints and ecological effects, and the question that arises is about the impact over the economic and social level (Jeremiah, Christine and Chris 2013). These platforms are creating serious impact over new markets, which have tried to increase the commerce volume and have tried to boost the consumer’s purchasing power (Jeremiah, Christine and Chris 2013). The huge profitable companies have tried to claim for generating the income and the business for the business providers (Jeremiah, Christine and Chris 2013). If this is so, then they will try to create the economic activities that wouldn’t be there such as more travelling, more inclusion of private rides of automobiles, and shifting from one provider to the other (Jeremiah, Christine and Chris 2013).
It is explored that Airbnb are taking various trips and cheap ride services are available for diverting the people towards the private transport from the public transport (Ipeirotis 2010). This implies that the platforms have enhanced the emission of carbon, as there services are using the energy (Ipeirotis 2010). The social impact related to it is increase in employment and reduction in poverty, as there more and more companies are coming in this area (Ipeirotis 2008). It’s not easy for companies to have these services in both the ways like creating the new activity in economy and minimizes the carbon emissions from transportation vehicles as they both are linked with one another (Ipeirotis 2008).
Research on “Crowd-sourcing” platforms supporting micro-work such as Amazon’s Mechanical Turk; explain a scenario about how crowd-sourcing platform can be used to semi-automate marketing related tasks that involve humans and IT systems. Discuss barriers that need to be tackled so that the automation scenario described can be effectively executed.
“Crowd-sourcing” platforms supporting micro-work such as Amazon’s Mechanical Turk
A crowdsourcing is considered as the practice for collecting the required services, or either the content through soliciting the contribution made from the support of huge people, and mainly through the help of online community, instead of suppliers and traditional employees (Jackson, Pompe and Krieshok 2011). This process is mainly applied for subdividing the tedious work process or either to fund the startups charities or companies (Jackson, Pompe and Krieshok 2011). It cover up the efforts of various self explored volunteers or either the part time employees, in which every contributor take their own initiative for adding the little portion into the huge outcome (Jackson, Pompe and Krieshok 2011). A crowdsourcing is differentiated from the outsourcing and the work comes through the unexplained public instead of commissioned through any particular group (Jackson, Pompe and Krieshok 2011).
The term crowdsourcing was come in the year 2006 and is actually applied in different activities (Jackson, Pompe and Krieshok 2011). Crowdsorcing includes various labor divisions for the different tedious tasks that are actually spoiled for using the outsourcing based on crowd, but the same could be applied to particular requests such as crowdfunding, crowdvoting, missing person, competition based on broad, along with basic search results (Jackson, Pompe and Krieshok 2011). Presently, crowdsourcing is mainly transferred over the internet. The internet offers specific venues for the purpose of crowdsourcing, and individuals tend over the projects based on webs, in which they are actually not physically judged or either scrutinized and therefore could actually feel more comfortable in sharing (Kelty 2012). This basically permits the artistic projects that are well designed, due to the individuals are not so conscious, and are even not aware about the scrutiny towards their work (Kelty 2012). In the internet atmosphere, more and more attention is paid over particular requirements of the projects, instead of spending time in communicating with the individuals (Kelty 2012).
Microwork is referred as the platform for crowdsourcing, in which users could complete small work for which the computers failed to have aptitude, due to the lesser amount of money (Law and Von Ahn 2011). The Amazon Mechanical Turk has also created the various projects for the users to participate into it, in which each task needs some time and provides little payments (Law and Von Ahn 2011). The similar Chinese version of it that is known as Witkey cover up various sites such as k68.cn and the other one is Taskcn.com (Law and Von Ahn 2011). While selecting the task, once the particular user win they could easily learn as well as submit the popular tasks for the purpose of increasing the likelihood for getting the work selected (Law and Von Ahn 2011). The best example of it is Mechanical Turk project, in which users are allowed to explore the images of satellite in order to explore the lost researcher (Law and Von Ahn 2011).
The crowd is considered as the umbrella that is termed for the people that easily contribute in the efforts of crowdsourcing (Light 2011). Though it sometimes becomes challenging to collect the data related to the crowd demographics; a study is conducted through the help of sample of 400000 that are actually registered crowd-workers and are using the Amazon Mechanical Trunk for completing their task for the pay (Light 2011). According to the previous study conducted in the year 2008, it is explored that the users are basically American, female, young, as well as well educated along with 40% are having the income more than $40000 (Light 2011). By the year 2009, there was around 36% of the Mechanical Turk workforce that was actually Indian. It’s noted that around 2/3rd of the Indian employees were actually male and around 66% were holding the bachelor degree (Light 2011). It was noted that around 2/3rd holds the yearly income that was less than $10000 along with 27% is dependent over the income collected from the mechanical Turk for meeting the end (Light 2011).
The average of the users in US for Mechanical Turk earned around $2.30 each hour for the task in the year 2009 as compared to $1.58 for the average worker in India (Orlikowski 1992). While most of the users in India are paid less than 5 hours in every week, and there are 18% who work for 15 hours each week (Ross, Irani and Silberman 2010). This is actually less than the required wages in every country and ethical questions are raised for the researchers that make use of crowdsourcing (Ross, Irani and Silberman 2010).
Crowd-sourcing platform use of semi-automate marketing related tasks that involve humans and IT systems
Semi-automated answers relate about the specific bots for particular task such as appropriate judgment (Brynjarsdottir, Håkansson, Pierce, Baumer, DiSalvo and Sengers 2012). It’s evident that spammers could make use of the previously lying packages and try to tailor their attack in order to give the context. In the use cases, spammers could support in creating the bot, which tries to open with all the links that correspond to the content of HTML and tries to complete the appropriate task, where ever it is possible (Brynjarsdottir, Håkansson, Pierce, Baumer, DiSalvo and Sengers 2012). Or it might run the query over the search engines and explore about the proposed links that rank first. If in case bot is not sure about the results, it can even consult the human for the purpose of increasing the accuracy of the answer or either return the decreased confidence for preserving the relevant rate (Brynjarsdottir, Håkansson, Pierce, Baumer, DiSalvo and Sengers 2012). These semi-automatic approaches need to be enhanced with both the reward and time ratio and the same should target over the collection of task with the questions that are easy to answer (Brynjarsdottir, Håkansson, Pierce, Baumer, DiSalvo and Sengers 2012).
For the purpose of using the microtask platform, it is expected that the requester packages with the work in the semi-automatic tasks and tries to publish them over the groups (Kittur, Chi and Suh 2008). Amazon Mechanical Turk, which is the popular crowdsourcing platform, implies the semi-automate tasks, such as Human Intelligence task, which is the term mainly used for interchanging the microtask (Kittur, Chi and Suh 2008). A requester explains about various parameters of configuration like the number of results, which is required for Human Intelligence task, the actual time required for completing Human Intelligence task, and the restrictions placed over the workers profile such as natural language knowledge and geographical location (Kittur, Chi and Suh 2008). As most of the Human Intelligence task could be resolved easily, similarly Human Intelligence task is also trying to organize a group that could share the parameters of configuration, and the employees prefer to get assigned with the huge chuck of work in place of dealing with the question in different process (Ipeirotis, Provost and Wang 2010). Through completing the task by the employees, the requester tries to gather as well as assess the results as well as rewards that are accepted as per the scheme of pre-defined remuneration (Ipeirotis, Provost and Wang 2010).
For most of the platforms, requester could try to automate the communication with system through API, while the employees follow the task by using the interface based on web that is generated through the requester (Alonso and Baeza-Yates 2011). The entire effectiveness related to the crowdsourcing could create influence through the ways that package is by requester which is the issue in the microtask series (Alonso and Baeza-Yates 2011). This packaging cover up the interface design that also includes the instructions for completing the task along with reduced criteria of quality for the work to get accepted, along with purposeful layout along with the procedures that is used by the requestor for the purpose of evaluating the outcome and for measuring the employees performance (Alonso and Baeza-Yates 2011). Due to various employees could actually perform the similar semi-automate tasks the requester could easily implement the various types of quality assurance (Alonso and Baeza-Yates 2011).
A crowdsourcing is considered as the process in order to indirect the anonymous employees on the internet, mainly for the nominative money, and for completing the tasks, which are actually complex for the present computers but actually simple for the human beings (Alonso and Lease 2011). These examples include the annotation of image, analysis of sentiments, appropriate judgment, as well as translation of language (Alonso and Lease 2011). Presently, the platform of crowdsourcing such as Amazon Mechanical Turk permits the requester for creating the tasks in context of the web pages and decides over how the pay should be per task, and participants are restricted through declaring the filters over the rate of acceptance, and country (Alonso and Lease 2011). Once these tasks get completed, the requester again tries to back the outcome in context of the raw files that are supposed to be filtered over the wrong answers and decide over whether or not to actually pay for every answer (Alonso and Lease 2011). One specific crowdsourcing appeal is to complete the huge collection of the task, which couldn’t be done by the requester in specific period of time (Alonso and Lease 2011).
There are certain barriers related to tackling the scenario of automation that could be executed effectively, these are given below:
Starting cost- Installation in the beginning, changes in team, configuration cannot be made without incurring the cost (Eickhoff and de Vries 2012). All these components will cost real time as well as money in the starting that could create the disruption (Eickhoff and de Vries 2012). It’s important that businesses should need to invest their time that is needed for initializing the regular delivery through ensuring that the customization of the business infrastructure and objectives are put in place and are even operational (Eickhoff and de Vries 2012).
Consideration of organizational culture- If the business is trying to accustom towards developing the software with the use of different methodologies or models like spiral or waterfall, they should try to overcome the learning curve before the model is implemented for continuous delivery (Heymann and Garcia-Molina 2011). In order to handle the automation scenario, organizations should around for training staff towards the tweaking process, and therefore, firms should try to maintain the previous operations at the time of transition in the continuous delivery process (Heymann and Garcia-Molina 2011). It often happens that the team members try to accustom their hands on the mistrust over increasing use of automation, which continuously delivers the entails (Heymann and Garcia-Molina 2011). To overcome his barrier, it’s important to work with the experts in order to ensure success (Heymann and Garcia-Molina 2011). Therefore, in this manner successful results at the onset will get build and even build the confidence in the process as well as minimizes the skepticism (Heymann and Garcia-Molina 2011).
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