Case Study Description of Practical Regression: Discrete Dependent Variables
This is the ninth in a series of lecture notes which, if tied together into a textbook, might be entitled "Practical Regression." The purpose of the notes is to supplement the theoretical content of most statistics texts with practical advice based on nearly three decades of experience of the author, combined with over one hundred years of experience of colleagues who have offered guidance. As the title "Practical Regression" suggests, these notes are a guide to performing regression in practice. This note returns to the topic of endogeneity, explaining how a predictor variable can be endogenous (and therefore its coefficient can be biased) if causality is in doubt. Through an extended example of the learning curve in medicine, the note introduces the concept of instrumental variables (IV), provides an intuitive explanation for why instruments solve the causality problem, and explains how to estimate IV and two-stage least squares regressions. The note describes statistical tests for the validity of instruments.
Swot Analysis of "Practical Regression: Discrete Dependent Variables" written by David Dranove includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Regression Practical facing as an external strategic factors. Some of the topics covered in Practical Regression: Discrete Dependent Variables case study are - Strategic Management Strategies, Financial management, Market research and Finance & Accounting.
Some of the macro environment factors that can be used to understand the Practical Regression: Discrete Dependent Variables casestudy better are - – customer relationship management is fast transforming because of increasing concerns over data privacy, technology disruption, geopolitical disruptions, digital marketing is dominated by two big players Facebook and Google, cloud computing is disrupting traditional business models, talent flight as more people leaving formal jobs, central banks are concerned over increasing inflation,
increasing government debt because of Covid-19 spendings, there is increasing trade war between United States & China, etc
Introduction to SWOT Analysis of Practical Regression: Discrete Dependent Variables
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Practical Regression: Discrete Dependent Variables case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Regression Practical, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Regression Practical operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Practical Regression: Discrete Dependent Variables can be done for the following purposes –
1. Strategic planning using facts provided in Practical Regression: Discrete Dependent Variables case study
2. Improving business portfolio management of Regression Practical
3. Assessing feasibility of the new initiative in Finance & Accounting field.
4. Making a Finance & Accounting topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Regression Practical
Strengths Practical Regression: Discrete Dependent Variables | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Regression Practical in Practical Regression: Discrete Dependent Variables Harvard Business Review case study are -
Learning organization
- Regression Practical is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Regression Practical is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Practical Regression: Discrete Dependent Variables Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Analytics focus
– Regression Practical is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by David Dranove can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Diverse revenue streams
– Regression Practical is present in almost all the verticals within the industry. This has provided firm in Practical Regression: Discrete Dependent Variables case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Low bargaining power of suppliers
– Suppliers of Regression Practical in the sector have low bargaining power. Practical Regression: Discrete Dependent Variables has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Regression Practical to manage not only supply disruptions but also source products at highly competitive prices.
Successful track record of launching new products
– Regression Practical has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Regression Practical has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
Training and development
– Regression Practical has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Practical Regression: Discrete Dependent Variables Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Cross disciplinary teams
– Horizontal connected teams at the Regression Practical are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
Ability to recruit top talent
– Regression Practical is one of the leading recruiters in the industry. Managers in the Practical Regression: Discrete Dependent Variables are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Innovation driven organization
– Regression Practical is one of the most innovative firm in sector. Manager in Practical Regression: Discrete Dependent Variables Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Digital Transformation in Finance & Accounting segment
- digital transformation varies from industry to industry. For Regression Practical digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Regression Practical has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Sustainable margins compare to other players in Finance & Accounting industry
– Practical Regression: Discrete Dependent Variables firm has clearly differentiated products in the market place. This has enabled Regression Practical to fetch slight price premium compare to the competitors in the Finance & Accounting industry. The sustainable margins have also helped Regression Practical to invest into research and development (R&D) and innovation.
Organizational Resilience of Regression Practical
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Regression Practical does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Weaknesses Practical Regression: Discrete Dependent Variables | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Practical Regression: Discrete Dependent Variables are -
Interest costs
– Compare to the competition, Regression Practical has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Regression Practical supply chain. Even after few cautionary changes mentioned in the HBR case study - Practical Regression: Discrete Dependent Variables, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Regression Practical vulnerable to further global disruptions in South East Asia.
Skills based hiring
– The stress on hiring functional specialists at Regression Practical has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers oriented approach.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Practical Regression: Discrete Dependent Variables, in the dynamic environment Regression Practical has struggled to respond to the nimble upstart competition. Regression Practical has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Workers concerns about automation
– As automation is fast increasing in the segment, Regression Practical needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
High operating costs
– Compare to the competitors, firm in the HBR case study Practical Regression: Discrete Dependent Variables has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Regression Practical 's lucrative customers.
Slow to strategic competitive environment developments
– As Practical Regression: Discrete Dependent Variables HBR case study mentions - Regression Practical takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
Slow decision making process
– As mentioned earlier in the report, Regression Practical has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Regression Practical even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Regression Practical is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Practical Regression: Discrete Dependent Variables can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
Lack of clear differentiation of Regression Practical products
– To increase the profitability and margins on the products, Regression Practical needs to provide more differentiated products than what it is currently offering in the marketplace.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Practical Regression: Discrete Dependent Variables HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Regression Practical has relatively successful track record of launching new products.
Opportunities Practical Regression: Discrete Dependent Variables | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Practical Regression: Discrete Dependent Variables are -
Lowering marketing communication costs
– 5G expansion will open new opportunities for Regression Practical in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Finance & Accounting segment, and it will provide faster access to the consumers.
Creating value in data economy
– The success of analytics program of Regression Practical has opened avenues for new revenue streams for the organization in the industry. This can help Regression Practical to build a more holistic ecosystem as suggested in the Practical Regression: Discrete Dependent Variables case study. Regression Practical can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Loyalty marketing
– Regression Practical has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Building a culture of innovation
– managers at Regression Practical can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Finance & Accounting segment.
Manufacturing automation
– Regression Practical can use the latest technology developments to improve its manufacturing and designing process in Finance & Accounting segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
Better consumer reach
– The expansion of the 5G network will help Regression Practical to increase its market reach. Regression Practical will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Reconfiguring business model
– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Regression Practical to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Regression Practical can use these opportunities to build new business models that can help the communities that Regression Practical operates in. Secondly it can use opportunities from government spending in Finance & Accounting sector.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Finance & Accounting industry, but it has also influenced the consumer preferences. Regression Practical can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Regression Practical to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Regression Practical to hire the very best people irrespective of their geographical location.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Regression Practical is facing challenges because of the dominance of functional experts in the organization. Practical Regression: Discrete Dependent Variables case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Regression Practical can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Practical Regression: Discrete Dependent Variables, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Learning at scale
– Online learning technologies has now opened space for Regression Practical to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
Threats Practical Regression: Discrete Dependent Variables External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Practical Regression: Discrete Dependent Variables are -
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Regression Practical will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Regression Practical.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Practical Regression: Discrete Dependent Variables, Regression Practical may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Finance & Accounting .
Environmental challenges
– Regression Practical needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Regression Practical can take advantage of this fund but it will also bring new competitors in the Finance & Accounting industry.
Consumer confidence and its impact on Regression Practical demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Regression Practical needs to understand the core reasons impacting the Finance & Accounting industry. This will help it in building a better workplace.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Regression Practical business can come under increasing regulations regarding data privacy, data security, etc.
Stagnating economy with rate increase
– Regression Practical can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
Increasing wage structure of Regression Practical
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Regression Practical.
High dependence on third party suppliers
– Regression Practical high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Regression Practical can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Practical Regression: Discrete Dependent Variables .
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Regression Practical in the Finance & Accounting sector and impact the bottomline of the organization.
Weighted SWOT Analysis of Practical Regression: Discrete Dependent Variables Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Practical Regression: Discrete Dependent Variables needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Practical Regression: Discrete Dependent Variables is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Practical Regression: Discrete Dependent Variables is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Practical Regression: Discrete Dependent Variables is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Regression Practical needs to make to build a sustainable competitive advantage.
