Case Study Description of Practical Regression: Causality and Instrumental Variables
This is the tenth 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 uses the theory of "supplier-induced demand" from health economics to illustrate key issues including reverse causality, the role of instrumental variables in establishing causality, and the characteristics of good instruments.
Swot Analysis of "Practical Regression: Causality and Instrumental Variables" written by David Dranove includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Causality Regression facing as an external strategic factors. Some of the topics covered in Practical Regression: Causality and Instrumental 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: Causality and Instrumental Variables casestudy better are - – increasing government debt because of Covid-19 spendings, central banks are concerned over increasing inflation, customer relationship management is fast transforming because of increasing concerns over data privacy, competitive advantages are harder to sustain because of technology dispersion, technology disruption, digital marketing is dominated by two big players Facebook and Google, wage bills are increasing,
banking and financial system is disrupted by Bitcoin and other crypto currencies, increasing household debt because of falling income levels, etc
Introduction to SWOT Analysis of Practical Regression: Causality and Instrumental Variables
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Practical Regression: Causality and Instrumental Variables case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Causality Regression, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Causality Regression 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: Causality and Instrumental Variables can be done for the following purposes –
1. Strategic planning using facts provided in Practical Regression: Causality and Instrumental Variables case study
2. Improving business portfolio management of Causality Regression
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 Causality Regression
Strengths Practical Regression: Causality and Instrumental Variables | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Causality Regression in Practical Regression: Causality and Instrumental Variables Harvard Business Review case study are -
High brand equity
– Causality Regression has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Causality Regression to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Cross disciplinary teams
– Horizontal connected teams at the Causality Regression 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.
Strong track record of project management
– Causality Regression is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Low bargaining power of suppliers
– Suppliers of Causality Regression in the sector have low bargaining power. Practical Regression: Causality and Instrumental Variables has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Causality Regression to manage not only supply disruptions but also source products at highly competitive prices.
Training and development
– Causality Regression has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Practical Regression: Causality and Instrumental 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.
Effective Research and Development (R&D)
– Causality Regression has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Practical Regression: Causality and Instrumental Variables - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Diverse revenue streams
– Causality Regression is present in almost all the verticals within the industry. This has provided firm in Practical Regression: Causality and Instrumental 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.
Ability to lead change in Finance & Accounting field
– Causality Regression is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Causality Regression in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Innovation driven organization
– Causality Regression is one of the most innovative firm in sector. Manager in Practical Regression: Causality and Instrumental Variables Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Learning organization
- Causality Regression 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 Causality Regression is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Practical Regression: Causality and Instrumental Variables Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
High switching costs
– The high switching costs that Causality Regression has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
Sustainable margins compare to other players in Finance & Accounting industry
– Practical Regression: Causality and Instrumental Variables firm has clearly differentiated products in the market place. This has enabled Causality Regression to fetch slight price premium compare to the competitors in the Finance & Accounting industry. The sustainable margins have also helped Causality Regression to invest into research and development (R&D) and innovation.
Weaknesses Practical Regression: Causality and Instrumental Variables | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Practical Regression: Causality and Instrumental Variables are -
Capital Spending Reduction
– Even during the low interest decade, Causality Regression has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
High bargaining power of channel partners
– Because of the regulatory requirements, David Dranove suggests that, Causality Regression is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Aligning sales with marketing
– It come across in the case study Practical Regression: Causality and Instrumental Variables that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Practical Regression: Causality and Instrumental Variables can leverage the sales team experience to cultivate customer relationships as Causality Regression is planning to shift buying processes online.
Interest costs
– Compare to the competition, Causality Regression 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.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Causality Regression is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Practical Regression: Causality and Instrumental 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.
Increasing silos among functional specialists
– The organizational structure of Causality Regression is dominated by functional specialists. It is not different from other players in the Finance & Accounting segment. Causality Regression needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Causality Regression to focus more on services rather than just following the product oriented approach.
Low market penetration in new markets
– Outside its home market of Causality Regression, firm in the HBR case study Practical Regression: Causality and Instrumental Variables needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
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 Causality Regression supply chain. Even after few cautionary changes mentioned in the HBR case study - Practical Regression: Causality and Instrumental Variables, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Causality Regression vulnerable to further global disruptions in South East Asia.
No frontier risks strategy
– After analyzing the HBR case study Practical Regression: Causality and Instrumental Variables, it seems that company is thinking about the frontier risks that can impact Finance & Accounting strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
Slow decision making process
– As mentioned earlier in the report, Causality Regression 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. Causality Regression 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.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Practical Regression: Causality and Instrumental Variables, it seems that the employees of Causality Regression don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Opportunities Practical Regression: Causality and Instrumental 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: Causality and Instrumental Variables are -
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Causality Regression in the consumer business. Now Causality Regression can target international markets with far fewer capital restrictions requirements than the existing system.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Finance & Accounting industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Causality Regression can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Causality Regression can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Causality Regression can explore opportunities that can attract volunteers and are consistent with its mission and vision.
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. Causality Regression can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Causality Regression is facing challenges because of the dominance of functional experts in the organization. Practical Regression: Causality and Instrumental 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.
Manufacturing automation
– Causality Regression 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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Causality Regression 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: Causality and Instrumental 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.
Better consumer reach
– The expansion of the 5G network will help Causality Regression to increase its market reach. Causality Regression 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.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Causality Regression can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Low interest rates
– Even though inflation is raising its head in most developed economies, Causality Regression can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Causality Regression 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 Causality Regression has opened avenues for new revenue streams for the organization in the industry. This can help Causality Regression to build a more holistic ecosystem as suggested in the Practical Regression: Causality and Instrumental Variables case study. Causality Regression can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Loyalty marketing
– Causality Regression 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.
Threats Practical Regression: Causality and Instrumental 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: Causality and Instrumental Variables are -
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 Causality Regression business can come under increasing regulations regarding data privacy, data security, etc.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Causality Regression in the Finance & Accounting industry. The Finance & Accounting industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
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. Causality Regression 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.
Technology acceleration in Forth Industrial Revolution
– Causality Regression has witnessed rapid integration of technology during Covid-19 in the Finance & Accounting industry. As one of the leading players in the industry, Causality Regression needs to keep up with the evolution of technology in the Finance & Accounting sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
Easy access to finance
– Easy access to finance in Finance & Accounting field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Causality Regression can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Shortening product life cycle
– it is one of the major threat that Causality Regression is facing in Finance & Accounting sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
High dependence on third party suppliers
– Causality Regression 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.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Causality Regression with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
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 Causality Regression in the Finance & Accounting sector and impact the bottomline of the organization.
Environmental challenges
– Causality Regression 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. Causality Regression can take advantage of this fund but it will also bring new competitors in the Finance & Accounting industry.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
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.
Consumer confidence and its impact on Causality Regression 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.
Weighted SWOT Analysis of Practical Regression: Causality and Instrumental 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: Causality and Instrumental 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: Causality and Instrumental 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: Causality and Instrumental 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: Causality and Instrumental 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 Causality Regression needs to make to build a sustainable competitive advantage.