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 - – increasing commodity prices, central banks are concerned over increasing inflation, there is backlash against globalization, digital marketing is dominated by two big players Facebook and Google, talent flight as more people leaving formal jobs, increasing household debt because of falling income levels, increasing transportation and logistics costs,
competitive advantages are harder to sustain because of technology dispersion, geopolitical disruptions, 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 -
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.
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.
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.
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.
Learning organization
- Log 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 Log Regression is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Practical Regression: Log vs. Linear Specification Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
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 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.
Diverse revenue streams
– Log Regression is present in almost all the verticals within the industry. This has provided firm in Practical Regression: Log vs. Linear Specification 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
– Log 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 Log Regression in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
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.
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.
Highly skilled collaborators
– Log Regression 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: Log vs. Linear Specification HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
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.
No frontier risks strategy
– After analyzing the HBR case study Practical Regression: Log vs. Linear Specification, 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.
High operating costs
– Compare to the competitors, firm in the HBR case study Practical Regression: Log vs. Linear Specification 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 Log Regression 's lucrative customers.
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.
Need for greater diversity
– Log Regression 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.
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.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Log Regression is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Practical Regression: Log vs. Linear Specification 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, Log Regression needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
High 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.
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.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Practical Regression: Log vs. Linear Specification, is just above the industry average. Log Regression 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.
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 -
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.
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.
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.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Log Regression can use these opportunities to build new business models that can help the communities that Log Regression operates in. Secondly it can use opportunities from government spending in Finance & Accounting sector.
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.
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.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Log 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.
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.
Leveraging digital technologies
– Log Regression 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.
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.
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.
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 Log Regression 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.
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 -
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Log Regression 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: Log vs. Linear Specification .
Consumer confidence and its impact on Log 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.
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.
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.
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.
Increasing wage structure of Log Regression
– 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 Log Regression.
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 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. Log Regression needs to understand the core reasons impacting the Finance & Accounting industry. This will help it in building a better workplace.
Technology acceleration in Forth Industrial Revolution
– Log Regression has witnessed rapid integration of technology during Covid-19 in the Finance & Accounting industry. As one of the leading players in the industry, Log 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.
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.
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.
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.
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.