Application of constructs and frameworks to real -world problem
Telematic Business Model
Johnson et al ,2008 described the importance of a business model and its application to a viable business. Using this construct, a startup Telematic firm is proposed to launch technology product for insurance industry and fleet management. The business model consists of four interwoven and related elements, together, create and deliver value. Business model is the framework for a thriving enterprise to align to changes through its operation mechanism. There are diverse uncertainties in business and its architecture of operation give an edge when it is obvious the business needs sustenance or prevented from collapsing. Innovation comes through the understanding of the key factors of the business and utilizing it to capture value for both the firm and its customer. Traditionally, firms posit a value propositions(Anderson,2006), but the interaction and influence of the customer bring an innovation to the product or service being rendered. A firm must innovate for the business to thrive and scale in the business model. The growth engine in this instance are the four key areas of the business model and each is mutually independent on one another to create and deliver value. There is need for clarify of functions of values in a business development. Understanding of the existing business model creates awareness for strength and limitations where adjustments may be inevitable. Characteristically, a startup like SRcom Telematics Inc looks unattractive to both internal and external stakeholders at the beginning of operation(Blank,2013), hence a need of blueprint to figure out the business existence. A good construct from business model is concerned with opportunity to meet the customer needs at a profit. Four (4) elements were identified from this literature.
Customer proposition value- This is a way to create value for the customers for the job done. In this scenario, the value for SRcom Telematics firm target customers are the auto insurance companies and the motorists. Because of the functional capability of the product, the insurance monitors and use driving data record for premium underwriting and also to predict the user risk levels- medium, low or high risk. However, the motorist needs such as speed limit compliance, mapping and navigational use are met using the telematic technology.
Customer proposition value can arguably be the most important tool in the product marketer’s toolset, because it provides the basis for understanding how the product will be realistically valued by the customer. Understanding how the product will provide unique value to the customer will be key to selling the product (Hudadoff, 2009).
Profit formula- This is the blueprint on how company creates and capture values to itself while meeting the customer needs. It is a payoff of the customer value proposition in term of financial gain, brand and market size. Large customer base increases the volume of sales, thereby bringing down the cost and associated expenses. The revenue model is therefore the product of price and volume of sales. However, the profit formula suggests and support how resources are utilized to aid expected volume of sales and achieve profit. Hence, profit formula derives value for the company.
Key Resources- In SRcom Telematics, our key resources include talents, technology, sales channel and brand. They are core resources required to deliver value to the customers. Business resources also include the capital from stakeholder to ensure smooth running of the business. All these together must be harnessed for proper take off and value proposition forecasting. These resources allow the enterprise to reach markets, maintain relationships with customer segments and earn revenues. The key resources constitute the major inputs that the company will use to create its value proposition, service its customer segment and deliver the product to the customer (Strategyzer, 2017).
KEY PROCESS: This is how the company able to deliver value through a repeatable process, creating a community platforms as sales channels (Edelman,2013 and Muegge, 2013). Other process that are facilitate productivity such as training of staff, development and R &D constituent the key process for the firm. It may sometimes include the company norms, metric and rules for proper management of the affairs of the business. Management looks at the key process and key resource to Innovate and advance value proposition for the company and the company. Executives look at key process and resource to deliver value for the customer. This can be subdivided into operational and managerial process. The former considers in depths of daily affairs of the company, from sales channels to branding. To succeed in business, managers need to understand how the opportunity relates with existing business model. An acceptable solution in the market will change over time, hence there the company need the capability to innovate in a competitive market. Business model make this change easy. According to Bob Higgins, a venture capitalist, business failed when it is back with technology. Success comes when we back business model in an organization.
Key processes will ensure that the company maintains a competitive advantage and deliver results that are directed towards specific and measurable business goals. Critical success factors that will achieve the laid down objectives of the company must be identified. Metrics to identify benchmarks must be established and a constant process of auditing must also be set in place to assess productivity (Falite, 2013).
“The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” ― Alvin Toffler
With a colored Mandala portrait in focus, learning technology innovation management from TIMG 5001 course is a total paradigm shift from class learning where students are just given a set of instructions to have a half-baked knowledge on the topic taught. An artistic coloring of mandala could be interpreted for the degree of value, perspectives and reflections for managerial grooming or better still an entrepreneur. Often, the objectives of the class have always been to add value to student in term of knowledge, leadership, integrity and networking (TIMG section 2 lecture). The resonating focus of the TIMG 5001 course not only impacted knowledge to students but also create a radically development in potentials and managerial thinking that will eventually give an edge to one’s professional pursuit. This course was loaded with opportunities, spanning the technology domains and management for a successful new startups entrepreneur. In this stream, students access domains in engineering, business school and industry experts.
The focus of TIMG 5001 lecture has been taught in the capacity of managers and entrepreneurs. It coalesced into studying process development of products and services. As a product development manager, this course has extensively covered products and services lifecycle. Shane and Ulrich, (2004) described the concept development has a constituent of working principles and elements in pursing products and services. This encompasses of aggregate of innovative ideas toward new products or services. Also, it focuses on guidelines for performing testing of new products and services. The scope of service and product development include its development, introduction, its growth, maturity and declining phase in the market. Product manager applies his knowledge of products functionalities to market needs to capture value for both customer and the firm. Product development can be view as a rational plan (Brown and Eisenhardt, 1995). A well planned and implemented makes a successful product development. Success in product development can be attributed to team organization of work and product effectiveness as depicted in the model.
Another lesson for product development managers is the ability to make decision in pursue of product strategy and planning, product development organization and project management (Krishnan and Ulrich,2001). Effectiveness of a product development manager is in the ability to make decision from market discovery and product strategy. This decision should be with the context of product Lifecyle to impact on the productivity and services. Other variable such as the team composition, investment, infrastructures and tools are the choice of decision for an effective product development manager. The effectiveness of a manager can be deduced from the product effectiveness in the market.
The decision making provides the basis for the design and qualification of both the development and the operational systems during the development process (Powell & Buede, 2006). Product development managers may need to come up with decision matrices unique to their environment in order to make good product decisions. It can be a combination of technical feasibility and brand alignment or customer preference. e.g. when trying to add several possible new features to the product, stakeholders may need to be involved in the decision-making process (Beltowska, 2014).
A product development manager is an entrepreneur in situ be the experience and capability. Having possessed a vast experience in product innovation management, effectiveness and ability can be seen in the area of competency of decision making, strategy and performance, organization design and venture financing (Shane and Ulrich, 2004). Entrepreneur decision on venture finance can be used to explain how to exploit business opportunities.
Research proposals and Thesis to be examined.
Adegboyega,(2015) make a formal presentation of perspectives in the cyber attacks and botnet breakdowns. . Dore & Shaw (2017) also presented views to Al barriers to implementation but most of the writing were informal. Suresh et al (2017) also gave an insight to blockchain application in healthcare industry but the not capture the inputs of the other stake holders in the industry. Adegboyega ,(2015) provided the research from management point of view.
Relevance of topic modeling in academic and research work
Topic modeling can be described as a method of finding and tracing associated group of words from a very large body of unlabeled text. For students, research time is of the essence and topic modeling provide the tools of managing search time in research domains. Topic modeling techniques lecture gave insights on justification and discovery of large data for research and academic purposes. As a tool in continuous improvement for students, research topic analysis is made easy with various possibilities of transforming source data into models for general use. This can be done using topic modeling software to identify relevant words in a research with topic labeling. Frequently occurring words within the same document often receive the same label. Topic subjects are found in the clusters of words that have similar meanings and degree of association. Trend of a research topic can be studied at a glance within a short time and across a global perspective.
Topic Modelling is an unsupervised Machine Learning (ML) technique that does not require a training dataset of manually tagged documents from which to learn; this implies that it can work directly with the targeted document (Liu et al, 2016).
Topic modeling can prove to be a useful method to enhance a researcher’s ability to interpret information. It can help to:
Building research topic
Students can make topic model tools such as machine language called mallet to coin and re-coin academic research topics for thesis writing. Mallet format is applicable to statistical enumerations of a probable research topics. Documents can be imported into the mallet format through a topic command to build a model. A hands-on example was performed during the lecture for cloning of topics which are similar in nature to the research problem.
While still an active research field in machine learning, topic modeling offers an effective means of data mining where samples represent documents (Zhao et al, 2015). Given a collection of unstructured text documents, topic modeling assumes that there are a certain number of latent topics in the collection of documents (corpus) and that each document contains multiple topics in different proportions (Zhao et al, 2015). Several topic models have been developed such as Latent Semantic Indexing (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA). (Zhao et al, 2015)
Topic output probability.
Using this tool for analysis, new documents may present in form of probability estimation. LDA is the most commonly used topic modeling method. It is an unsupervised probabilistic method for modeling a corpus (Zhao et al, 2015).
Essentially, LDA takes the assumption that each document can be represented as a probabilistic distribution over latent topics, and that topic distribution in all document share a common Dirichlet prior, therefore each latent topic in the LDA model is also represented as a probabilistic distribution over words and the word distributions of topics share a common Dirichlet prior as well (Zhao et al, 2015).
Peer Review report
Practitioner article: Peter Walma, 2017. Designing self -driving vehicles with everyone in mind. TIM Review. TIMG 5001
Reviewer Name and Address: Adeseun Ilori, Review board Member, TIM Review, Carleton University, Ottawa.
Review Date: December 22,2017
Designing self -driving vehicles with everyone in mind article is of scope of society concerns in the viability and usefulness of the technology. The author elaborated much more on the society perspectives toward the safety concern, acceptability of the technology by the policy makers and the coverage of the self-driving vehicles through the auto insurance and life insurance under writing. The author likely objective was to look in the designing features that answers the general concerns of the society in tandem to the existing policy. Most importantly, the author mirrored the existing auto policies in comparison to self-driving vehicles to highlight the major concern. The author also veered through the adoption of self-driving vehicles without a referencing to the contextual and diffusion theories (TAM) that was pioneer by Davies, (1989) who gave instances to the influencing adoption factors to a technology product. The author contributions did not cover the green ecosystem to the society by the self-driving vehicle.
The following observations were also made:
The article slighted some practical ethical issues such as decision making in an accident scenario and placing importance on individual life. The author also mentioned the application of telematics instruments to aid compliance to the existing regulation and make an alternative savings in term of a colossal loss.
Article strengths: The article sensitize the audience abstraction of self-driving vehicles in relation to the sighted concerns.
Weakness: One of the weakness is the author sentence structure which either informal to semi informal in nature. The organization of ideas were not coherently related to excite the audience mind. The topic structure needs some restructuring to capture the contextual theme of the article
Area of concentration for improvement: The author should concentrate on the contextual theme of self-driving vehicles to excite the audience who are of technology perspectives and how the technology answers the society questions. Also, there is a need of improvement in the management context of self-driving vehicles and how it relates to weighted values for the stakeholders.
The research topic should resonate with the streams to convince the readers usefulness of the self-driving vehicles in the society and also provide more of management perspectives of the technology to meet TIM review context guidelines in the subsequent edition.
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