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 - – talent flight as more people leaving formal jobs, increasing commodity prices, wage bills are increasing, banking and financial system is disrupted by Bitcoin and other crypto currencies, digital marketing is dominated by two big players Facebook and Google, there is increasing trade war between United States & China, there is backlash against globalization,
customer relationship management is fast transforming because of increasing concerns over data privacy, competitive advantages are harder to sustain because of technology dispersion, 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 -
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.
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.
Operational resilience
– The operational resilience strategy in the Practical Regression: Discrete Dependent Variables Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
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.
Strong track record of project management
– Regression Practical is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
High brand equity
– Regression Practical has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Regression Practical to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Effective Research and Development (R&D)
– Regression Practical 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: Discrete Dependent 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.
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.
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.
Superior customer experience
– The customer experience strategy of Regression Practical in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
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.
Highly skilled collaborators
– Regression Practical has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Practical Regression: Discrete Dependent Variables HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
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 -
Low market penetration in new markets
– Outside its home market of Regression Practical, firm in the HBR case study Practical Regression: Discrete Dependent Variables needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
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.
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.
Capital Spending Reduction
– Even during the low interest decade, Regression Practical 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 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.
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.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Practical Regression: Discrete Dependent Variables, it seems that the employees of Regression Practical 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.
Products dominated business model
– Even though Regression Practical has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Practical Regression: Discrete Dependent Variables should strive to include more intangible value offerings along with its core products and services.
Need for greater diversity
– Regression Practical has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
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.
High cash cycle compare to competitors
Regression Practical has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
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 -
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.
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.
Using analytics as competitive advantage
– Regression Practical has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Practical Regression: Discrete Dependent Variables - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Regression Practical to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
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.
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.
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.
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.
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. Regression Practical 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. Regression Practical 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.
Leveraging digital technologies
– Regression Practical can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Buying journey improvements
– Regression Practical can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Practical Regression: Discrete Dependent Variables suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
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.
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 -
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.
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.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Regression Practical 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 Regression Practical in the Finance & Accounting sector and impact the bottomline of the organization.
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.
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.
Technology acceleration in Forth Industrial Revolution
– Regression Practical has witnessed rapid integration of technology during Covid-19 in the Finance & Accounting industry. As one of the leading players in the industry, Regression Practical 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. Regression Practical can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
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.
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.
Regulatory challenges
– Regression Practical needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Finance & Accounting industry regulations.
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 .
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Regression Practical 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.
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.
