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BUSM084 New Product Development And Business Ecosystems

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BUSM084 New Product Development And Business Ecosystems

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BUSM084 New Product Development And Business Ecosystems

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Course Code: BUSM084
University: Queen Mary University Of London is not sponsored or endorsed by this college or university

Country: United Kingdom


The module aims at providing students with an advanced theoretical grounding which should enable them to critically evaluate issues of innovation and new product development in relation task and knowledge partitioning within complex business ecosystems. It also aims at enabling students to appreciate the organizational relevance of theoretical frameworks relevant to new product development and to understand the theoretical differences (and practical implications) of contrasting paradigms. In particular the module aims at enabling the students to master:
1.Different approaches to define and analyse business ecosystems;2.The relationship between firms and their business ecosystems;3.The relationship between innovation and business ecosystem;4.The relationship between entrepreneurship, business ecosystems and innovation;5.The analysis of the dynamics of innovation: Technological Path Dependence, Organisational Path Dependence, Intangible Specialisation, Path Creation;6.The notions of dynamic capabilities, outsourcing and architectural knowledge;7.The notion of open innovation and its implications for new product development.
By the end of the module, students should; Demonstrate a clear understanding of the relationship between new product development, business ecosystems and supply chain configuration;Be able to critically evaluate the main theoretical approaches to innovation and business ecosystems;Be able to understand the relationship between patterns of knowledge accumulation, dynamic capabilities and the dynamics of innovation;Be able to master concepts such as path dependence, path creation, and open innovation, and understand their relevance to management;Be able to analyse the dynamics of innovation within both entrepreneurial firms and large organisations. 


Based on research that was done by (Gawer, 2014), he found out that technological platforms had been approached using two perspectives, the two significant aspects were namely economics and engineering. Economics visualize the technological platforms as double-sided markets while the engineering study of the platform visualizes it as a technological architecture. On a broader Perspective, Economics and Engineering have envisioned the technological platforms differently, but the combination of the two brings out the more significant meaning of platforms.
The economic perspective studies and evaluates the platforms by studying how the market communicates and interacts and how the primary market creates. Economies identify platforms as two-sided because the markets within which they are involved constitute of more than one agent and they are all connected by the platform. When a platform is considered to be a two-sided market, the components like price and many basic strategies are affected by indirect network effects between the two known sides of the platform(Gawer, 2014).
Many years before engineering theorists described the technological platforms as two sided architectures, the term was being studied in ongoing product development literatures, this soon lead to a greater understanding of platforms and how they affect technological innovation. This view of platforms as technological architectures has greatly helped firms to achieve economies of scale in production and design of products. Engineers have had a big say on how technology will be invented, developed and implemented. For instance, Engineers at IBM or Cisco companies are at the background of any project or development that is happening.
Classifications of platforms
Even though researcher has approached the different the technological platforms by use of two theories, there are still more platform categories that are named as depending to the functioning; Each dominant platform may contain several platforms which may hold more similarities than differences (Gawer, 2014). Each platform differs from the other by the features it has and the level of external elements it can support.  There are many kinds of platforms in the world some are discussed.
Technology platforms – Technology platforms are platforms like Amazon Web Services, Microsoft Azure, and Twilio, they provide the basic block for product creation and services which are then used to develop the product. The platforms that are found in technology platforms are not regarded to be two-sided which is because the platforms are only designed to link the customers to the owners and not customers to other customers (Corradini & De Propris, 2017). The technology platforms have no networks effect. Hence they grow linearly with the adoption of developers and not relying on the connection between the connection between the users and the owners.
Computing Platforms – the computing platforms are an exact contrast to the technology platforms, this is because they allow communication between the customers and creators of the sight. The connection between the users of the system creates a robust bi-directional effect once the system reaches the maximum levels. When the users of the system communicate, and they grow in number, they attract an investor who in turn develops an application with the hope of making more income, the applications then attract more users who I turn attract developers, and the cycle goes on. Proper launching of the system dramatically determines the success of the organization (Yin et al., 2017).
Utility Platforms – Utility platforms are platforms that mainly attract customers by providing some free form of service, once to customers are many, they introduce the third party, advertisements. The advertisements become the source of revenue for the platform. The utility platforms have no kind of networks effect; this is because the users of the system attract the advertisers but the advertisers do not necessarily attract the users of the platform. The launching process of a utility platform is a straightforward fairy thing, only one important thing is needed, A useful service that generates repeated use (Meloni, Sanjust, Delogu, & Sottile, 2014).
Interaction Networks – This is platforms whose main and essential purpose is to facilitate communication between people. The standard interaction platforms available include Facebook, Snap chat, and Twitter. Identity is the principal founder mental characteristic of the personnel when people join this kind of a platform; they join to communicate with others. Therefore the main networks effect is between the ladies.  The method of launching this kind of platform would be rather easy; all the avatar would have to do is target a group of people who communicate with each other rather often (Schel, Bauer, Vazquez, Schulz, & Bauernhansl, 2018).
Market Place – this kind of platforms are created to be two sides as they connect the demand to the supply, This kind of platform enables a transaction between the buyer and the seller. The sellers set the prices of the goods and services that are offered on this site. The networks effect in this platform happens between the seller and the buyer. Launching this kind of a platform is not very easy as the profit predictability has
On-demand service platform – On-demand service platform is a platform where the commodities need are available at the time of requests. This type of platform is designed to deliver end-to-end filled by independent service providers. This on-demand platform integrates the arts of order, discovery, payment, and fulfillment all under one roof. The most necessary traits that are heavily regarded for by the on-demand platform are availability and predictability. For this kind of a platform, the networks effect is measured in matters of accessibility after a consumer has ordered. When launching an on-demand service the ensuring that there is enough personnel to serve the first users can give the system a great start (Dimakopoulos & Sudaric, 2018).
Data harvesting platforms – are kind of platforms that gain information from users when users enter to check some information, the user has to leave some information in return. The data Harvestings networks effect is based on the data and not on the user; this is because usage generates more data which is more useful to the users. When launching the data harvesting platform, the person initiating it requires the only an initial database without a networks effect.
Content Crowdsourcing Platform – this is a form of the platform where the content from every user is collected and distributed and shared with the user base of the platform, an excellent example of this kind of platform is YouTube, yelp, etc. For this kind of a platform, the network effect is between the content of the platform and the consumer of the content of the platform. The more the content of the platform increases the more the content the consumers have to take. Launching this kind of platform is typically straightforward, the owner seed the platform with the initial content (Dimakopoulos & Sudaric, 2018).
The world of technology provides many platforms that serve many purposes in the daily living of people, platforms achieve different functionality by how they are designed, while some platforms are built for enhancing communications, others are built for education and others for entertainment. The platforms that are create also benefit different people depending on who operates them. When selecting a platform for use, one should consider the services that the platform offers and whether they match with the needed requirements.
Corradini, C., & De Propris, L. (2017). Beyond local search: Bridging platforms and inter-sectoral technological integration. Research Policy, 46(1), 196–206.
Dimakopoulos, P. D., & Sudaric, S. (2018). Privacy and platform competition. International Journal of Industrial Organization.
Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–1249.
Meloni, I., Sanjust, B., Delogu, G., & Sottile, E. (2014). Development of a Technological Platform for Implementing VTBC Programs. 17th Meeting of the EURO Working Group on Transportation, EWGT2014, 2-4 July 2014, Sevilla, Spain, 3, 129–138.
Schel, D., Bauer, D., Vazquez, F. G., Schulz, F., & Bauernhansl, T. (2018). IT Platform for Energy Demand Synchronization Among Manufacturing Companies. 51st CIRP Conference on Manufacturing Systems, 72, 826–831.
Yin, Z., Lan, H., Tan, G., Lu, M., Vasilakos, A. V., & Liu, W. (2017). Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges. Computational and Structural Biotechnology Journal, 15, 403–411.

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