Practical Regression: Log vs. Linear Specification SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Finance & Accounting
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Practical Regression: Log vs. Linear Specification
This is the seventh 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 explains how to choose between log and linear specification. The note emphasizes the economic interpretation of a log model and how to interpret coefficients in a log regression. The note concludes by explaining how to choose between log and linear specifications on econometric grounds, including an explanation of the Box-Cox test.
Swot Analysis of "Practical Regression: Log vs. Linear Specification" written by David Dranove includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Log Regression facing as an external strategic factors. Some of the topics covered in Practical Regression: Log vs. Linear Specification 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: Log vs. Linear Specification casestudy better are - – challanges to central banks by blockchain based private currencies, increasing government debt because of Covid-19 spendings, there is increasing trade war between United States & China, there is backlash against globalization, increasing energy prices, increasing inequality as vast percentage of new income is going to the top 1%, talent flight as more people leaving formal jobs,
banking and financial system is disrupted by Bitcoin and other crypto currencies, competitive advantages are harder to sustain because of technology dispersion, etc
Introduction to SWOT Analysis of Practical Regression: Log vs. Linear Specification
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Practical Regression: Log vs. Linear Specification case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Log Regression, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Log 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: Log vs. Linear Specification can be done for the following purposes –
1. Strategic planning using facts provided in Practical Regression: Log vs. Linear Specification case study
2. Improving business portfolio management of Log 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 Log Regression
Strengths Practical Regression: Log vs. Linear Specification | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Log Regression in Practical Regression: Log vs. Linear Specification Harvard Business Review case study are -
Cross disciplinary teams
– Horizontal connected teams at the Log 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.
Effective Research and Development (R&D)
– Log 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: Log vs. Linear Specification - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Digital Transformation in Finance & Accounting segment
- digital transformation varies from industry to industry. For Log Regression digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Log Regression 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.
High switching costs
– The high switching costs that Log 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.
Training and development
– Log 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: Log vs. Linear Specification 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.
Organizational Resilience of Log Regression
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Log Regression does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Analytics focus
– Log Regression 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.
Low bargaining power of suppliers
– Suppliers of Log Regression in the sector have low bargaining power. Practical Regression: Log vs. Linear Specification has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Log Regression to manage not only supply disruptions but also source products at highly competitive prices.
Ability to recruit top talent
– Log Regression is one of the leading recruiters in the industry. Managers in the Practical Regression: Log vs. Linear Specification are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Successful track record of launching new products
– Log Regression has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Log Regression 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.
Superior customer experience
– The customer experience strategy of Log Regression in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
High brand equity
– Log Regression has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Log Regression to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Weaknesses Practical Regression: Log vs. Linear Specification | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Practical Regression: Log vs. Linear Specification are -
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Practical Regression: Log vs. Linear Specification, it seems that the employees of Log 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.
Low market penetration in new markets
– Outside its home market of Log Regression, firm in the HBR case study Practical Regression: Log vs. Linear Specification 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 Log Regression supply chain. Even after few cautionary changes mentioned in the HBR case study - Practical Regression: Log vs. Linear Specification, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Log Regression vulnerable to further global disruptions in South East Asia.
High cash cycle compare to competitors
Log Regression 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.
Lack of clear differentiation of Log Regression products
– To increase the profitability and margins on the products, Log Regression needs to provide more differentiated products than what it is currently offering in the marketplace.
Capital Spending Reduction
– Even during the low interest decade, Log 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 dependence on star products
– The top 2 products and services of the firm as mentioned in the Practical Regression: Log vs. Linear Specification 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 Log Regression has relatively successful track record of launching new products.
Slow to strategic competitive environment developments
– As Practical Regression: Log vs. Linear Specification HBR case study mentions - Log Regression 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.
Skills based hiring
– The stress on hiring functional specialists at Log Regression 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.
Products dominated business model
– Even though Log Regression 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: Log vs. Linear Specification should strive to include more intangible value offerings along with its core products and services.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Practical Regression: Log vs. Linear Specification, in the dynamic environment Log Regression has struggled to respond to the nimble upstart competition. Log Regression has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Opportunities Practical Regression: Log vs. Linear Specification | 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: Log vs. Linear Specification are -
Buying journey improvements
– Log Regression can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Practical Regression: Log vs. Linear Specification 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.
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. Log 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. Log 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.
Better consumer reach
– The expansion of the 5G network will help Log Regression to increase its market reach. Log 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.
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. Log Regression can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Log Regression is facing challenges because of the dominance of functional experts in the organization. Practical Regression: Log vs. Linear Specification 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.
Building a culture of innovation
– managers at Log Regression 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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Log 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: Log vs. Linear Specification, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Log Regression 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 Log Regression to hire the very best people irrespective of their geographical location.
Learning at scale
– Online learning technologies has now opened space for Log Regression 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.
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. Log Regression can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Creating value in data economy
– The success of analytics program of Log Regression has opened avenues for new revenue streams for the organization in the industry. This can help Log Regression to build a more holistic ecosystem as suggested in the Practical Regression: Log vs. Linear Specification case study. Log Regression can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Manufacturing automation
– Log 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.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Log Regression can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Threats Practical Regression: Log vs. Linear Specification 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: Log vs. Linear Specification are -
Stagnating economy with rate increase
– Log Regression 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.
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 Log Regression business can come under increasing regulations regarding data privacy, data security, etc.
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. Log 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 Log Regression is facing in Finance & Accounting sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
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.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Log Regression with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
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. Log 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.
Environmental challenges
– Log 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. Log Regression can take advantage of this fund but it will also bring new competitors in the Finance & Accounting industry.
High dependence on third party suppliers
– Log 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.
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 Log Regression in the Finance & Accounting sector and impact the bottomline 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.
Regulatory challenges
– Log Regression 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.
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: Log vs. Linear Specification, Log Regression 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 .
Weighted SWOT Analysis of Practical Regression: Log vs. Linear Specification 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: Log vs. Linear Specification 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: Log vs. Linear Specification 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: Log vs. Linear Specification 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: Log vs. Linear Specification 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 Log Regression needs to make to build a sustainable competitive advantage.