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 - – technology disruption, there is backlash against globalization, talent flight as more people leaving formal jobs, wage bills are increasing, cloud computing is disrupting traditional business models, increasing energy prices, geopolitical disruptions,
increasing transportation and logistics costs, digital marketing is dominated by two big players Facebook and Google, 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 -
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.
High switching costs
– The high switching costs that Regression Practical 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.
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.
Ability to lead change in Finance & Accounting field
– Regression Practical 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 Regression Practical in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
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.
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.
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.
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.
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.
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.
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 -
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.
Aligning sales with marketing
– It come across in the case study Practical Regression: Discrete Dependent 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: Discrete Dependent Variables can leverage the sales team experience to cultivate customer relationships as Regression Practical is planning to shift buying processes online.
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.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Practical Regression: Discrete Dependent Variables, is just above the industry average. Regression Practical needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
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 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.
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.
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.
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.
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.
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.
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 -
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.
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 Regression Practical in the consumer business. Now Regression Practical can target international markets with far fewer capital restrictions requirements than the existing system.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Developing new processes and practices
– Regression Practical can develop new processes and procedures in Finance & Accounting industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.