Discussion: Impact of Ethics, CSR, and Positive Social Change on Strategy

When a company goes through a controversy involving an ethical or legal violation, negative consequences are bound to follow. Customers may choose different brands because they do not want their purchases to go toward supporting a company that is involved in bad practices. Similarly, employees of the company may leave for a more upstanding company for a number of reasons. They might not want to work for an organization that allowed those violations to occur, or they may feel their employment is in jeopardy because the violations could lead to the company’s undoing. By taking a proactive approach and incorporating ethical values into strategy, companies can better ensure that unethical or illegal actions are less likely to happen and will be in a better position to regain people’s trust if violations do occur.

In this Discussion, you will consider legal precedents and ethical matters, and you will examine their impact on a business’s strategy.

To prepare for this Discussion:

• Review this week’s Learning Resources on ethics, CSR, and business strategy.
• Consider the role of legal precedents such as the Foreign Corrupt Practices Act (FCPA) in limiting unethical and illegal business practices. Consider also other ethical matters about which a company might develop policies and practices.
• Consider global legislative or regulative input on ethical matters about which a company might develop policies and practices (e.g., SOX, King IV, etc.).
• Review the Academic Writing Expectations: Capstone Courses document, provided in this week’s Learning Resources.

Post a 225- to 300-word (3- to 4-paragraph) analysis of the impact of ethics, social responsibility, and positive social change on a business’s strategy. In your analysis, respond to the following:

• Is it enough for a business simply to follow the law? What is the role that ethics should play in a business strategy? What are the benefits of incorporating ethics into a business’s strategy?
• How are practices and policies related to corporate social responsibility and positive social change a reflection of a business’s ethics? What are the potential benefits and risks of having CSR and/or positive social change as part of a business strategy?
• To support your response, be sure to reference at least one properly cited scholarly source.

Refer to the Week 5 Discussion Rubric for specific grading elements and criteria. Your Instructor will use this grading rubric to assess your work.

Making Advanced Analytics Wo r k   For YouA practical guide to capitalizing on big data by Dominic Barton and David CourtBig  data  and  analytics  have rocketed to the top of the corporate agenda. Executives look with ad-miration at how Google, Amazon, and others have eclipsed  competitors  with powerful new busi-ness models that derive from an ability to exploit data. They also see that big data is attracting serious investment from technology leaders such as IBM and Hewlett-Packard. Meanwhile, the tide of private-equity and venture-capital investments in big data continues to swell.The trend is generating plenty of hype, but we be-lieve that senior leaders are right to pay attention. Big data could transform the way companies do business, delivering the kind of performance gains last seen in the 1990s, when organizations redesigned their core processes. As data-driven strategies take hold, they will become an increasingly important point of com-petitive differentiation. According to research by Andrew McAfee and Erik Brynjolfsson, of MIT, com-panies that inject big data and analytics into their operations show productivity rates and pro  tability that are 5% to 6% higher than those of their peers (see “Big Data: The Management Revolution” in this issue). Even so, our experience reveals that most compa-nies are unsure how to proceed. Leaders are under-standably leery of making substantial investments in big data and advanced analytics. They’re convinced that their organizations simply aren’t ready. After all, companies may not fully understand the data they al-ready have, or perhaps they’ve lost piles of money on data-warehousing programs that never meshed with business processes, or maybe their current analytics programs are too complicated or don’t yield insights that can be put to use. Or all of the above. No wonder skepticism abounds.Many CEOs, too, recall their experiences with customer relationship management in the mid-1990s, when new CRM software products often prompted great enthusiasm. Experts descended on boardrooms promising impressive results if new IT systems were built to collect massive amounts of customer data. HBR.ORGOctober 2012 Harvard Business Review 79

SPOTLIGHT ON BIG DATAIt didn’t turn out that way. Too many C-suites were blind to the practical implications of new CRM tech-nologies—namely, that to capitalize on them, or-ganizations would have to make complex process changes and build employees’ skills. The promised gains in performance were often slow in coming, because the systems remained stubbornly discon-nected from how companies and frontline managers actually made decisions, and new demands for data management added complexity to operations. To be fair, most companies eventually managed to get their CRM programs on track, but not before some had suff  ered sizable losses and several CRM champi-ons had lost career momentum.Given this history, we empathize with executives who are cautious about big data. Nevertheless, we believe that the time has come to de  ne a pragmatic approach to big data and advanced analytics—one tightly focused on how to use the data to make bet-ter decisions.In our work with dozens of companies in six data-rich industries, we have found that fully exploiting data and analytics requires three mutually support-ive capabilities. (See the exhibit “How to Benefit from Big Data.”) First, companies must be able to identify, combine, and manage multiple sources of data. Second, they need the capability to build ad-vanced analytics models for predicting and optimiz-ing outcomes. Third, and most critical, management must possess the muscle to transform the organiza-tion so that the data and models actually yield better decisions. Two important features underpin those activities: a clear strategy for how to use data and analytics to compete, and deployment of the right technology architecture and capabilities.Equally important, the desired business impact must drive an integrated approach to data sourcing, model building, and organizational transformation. That’s how you avoid the common trap of starting with the data and simply asking what it can do for you. Leaders should invest sufficient time and en-ergy in aligning managers across the organization in support of the mission. 1. Choose the Right DataThe universe of data and modeling has changed vastly over the past few years. The sheer volume of information, particularly from new sources such as social media and machine sensors, is growing rap-idly. The opportunity to expand insights by combin-ing data is also accelerating, as more-powerful, less costly software abounds and information can be ac-cessed from almost anywhere at any time. Bigger and better data give companies both more-panoramic and more-granular views of their business environ-ment. The ability to see what was previously invis-ible improves operations, customer experiences, and strategy. But mastering that environment means up-ping your game,   nding deliberate and creative ways to identify usable data you already have, and explor-ing surprising sources of information.Source data creatively. Often companies al-ready have the data they need to tackle business problems, but managers simply don’t know how the information can be used for key decisions. Op-erations executives, for instance, might not grasp the potential value of the daily or hourly factory and customer-service data they possess. Companies can impel a more comprehensive look at information sources by being speci c about business problems they want to solve or opportunities they hope to exploit. For example, a banking team that needed to improve the efficiency of its customer-service operations created a 360-degree view by combining information from ATM transactions, online queries, customer complaints, and so on. That allowed dupli-cative interactions to be identi  ed, thereby reducing costs and streamlining the customer experience.Managers also need to get creative about the po-tential of external and new sources of data. Social media are generating terabytes of nontraditional, unstructured  data  in  the  form  of  conversations,  photos, and video. Add to that the streams of data   owing in from sensors, monitoring processes, and external sources that range from local demograph-ics to weather forecasts. One way to prompt broader thinking about potential data is to ask, “What deci-A shipping  rm improved on-time performance of its  eet by tapping data on weather and port availability that it hadn’t realized were available.A shipping  rm improved on-time performance of its  eet by tapping data on weather and port availability that it hadn’t realized were available.80  Harvard Business Review October 2012

Making advanced analytics Work for yousions could we make if we had all the information we need?” Using that logic, one shipping company im-proved the on-time performance of its fleet by tap-ping specialized weather forecast data and live infor-mation about port availability that it hadn’t realized were available.Senior executives can take the lead here. The CEO of one major packaged-goods company told us that he views data as a strategic asset whose value he takes into account when assessing potential acquisi-tions. But leaders at all levels must also be attuned to novel approaches to gathering and husbanding information. As business practices in the internet era continue to evolve, inspiration can often arise from a scan of the external environment. A corporate fi-nance executive, for instance, might look to a com-pany such as Kabbage, a start-up that supplies work-ing capital to online businesses. To slash the time required to underwrite loans, Kabbage asks mer-chants to opt in to sharing their customer-feedback ratings, Facebook interactions, and electronic ship-ping records. Those with the strongest feedback and highest business volume receive greater financing.Get the necessary IT support. Legacy IT struc-tures may hinder new types of data sourcing, storage, and analysis. Existing IT architecture may prevent the integration of siloed information, and managing unstructured data often remains beyond traditional IT capabilities. Many legacy systems were built to de-liver data in batches, so they can’t furnish continuous flows of information for real-time decisions. Fully resolving these issues often takes years. However, business leaders can address short-term big data needs by working with CIOs to prioritize requirements. This means quickly identifying and connecting the most important data for use in ana-lytics, followed by a cleanup operation to synchro-nize and merge overlapping data and then to work around missing information. Such short-term tactics may lead companies to vendors that focus on analyt-ics services or emerging software. New cloud-based technologies may also offer ways to scale computing power up or down to meet big data demands cost-effectively. Together those approaches establish an IT infrastructure that propels innovation by facilitating collaboration, rapid analysis, and experimentation.2. Build Models that Predict and optimize Business outcomesData are essential, but performance improvements and  competitive  advantage  arise  from  analytics  models  that  allow  managers  to  predict  and  opti-mize outcomes. More important, the most effective approach to building a model rarely starts with the data; instead it originates with identifying the busi-ness opportunity and determining how the model can improve performance.Unfortunately, not all model building follows this course. One approach that gets inconsistent results, for instance, is simple data mining. Corralling huge data sets allows companies to run dozens of statisti-cal tests to identify submerged patterns, but that pro-vides little benefit if managers can’t effectively use the correlations to enhance business performance. A pure data-mining approach often leads to an endless search for what the data really say. One company followed a more targeted strategy to optimize complex product pricing. At its core was a model based on the historical price elasticity of its products, sales data, competitors’ responses, and other variables. To improve its chances of suc-cess, the company began the modeling process by positing which factors affected sales volumes (for instance, competitors’ pricing and promotions) and then asked what data and which model would best deliver insights that were useful for making business decisions. We have found that such hypothesis-led modeling generates faster outcomes and also roots idea in BriefThe trend toward big data is growing rapidly, and senior leaders can’t afford to dismiss it as hype. Advanced analytics is likely to become a decisive competitive asset in many industries and a core element in companies’ efforts to improve performance.It’s a mistake to assume that acquiring the right kind of big data is all that matters. Also essential is developing analyt-ics tools that focus on business outcomes and that are relevant and easy to use for everyone from the C-suite to the front lines. That requires transforming your organization’s culture and capa-bilities, not in a rush to action but in a deliberative effort to weave big data into the fabric of daily operations.hbr.orgoctober 2012 harvard business review 81

SPOTLIGHT ON BIG DATAmodels in practical data relationships that are more broadly understood by managers.Remember, too, that any modeling exercise has inherent risk. Although advanced statistical methods indisputably make for better models, statistics ex-perts sometimes design models that are too complex to be practical. For example, a predictive model with 30 variables may explain historical data with high ac-curacy, but managing so many variables will exhaust most organizations’ capabilities. Companies should repeatedly ask, “What’s the least complex model that would improve our performance?”3. Transform Your Company’s CapabilitiesThe lead concern expressed to us by senior execu-tives is that their managers don’t understand or trust big data–based models. One large retailer intended its model to optimize returns on advertising spend-ing, but despite considerable investment, it wasn’t being used. The reason soon became evident: The frontline marketers who made key decisions on ad spending didn’t believe the model’s results and had little familiarity with how it worked.Many companies grapple with such problems, often because of a mismatch between the organi-zation’s existing culture and capabilities and the emerging tactics to exploit analytics successfully. In short, the new approaches don’t align with how companies actually arrive at decisions, or they fail to provide a clear blueprint for realizing business goals. Tools seem to be designed for experts in mod-eling rather than for people on the front lines, and few managers   nd the models engaging enough to champion their use—a key failing if companies want the new methods to permeate the organization. Bot-tom line: Using big data requires thoughtful organi-zational change, and three areas of action can get you there.Develop business-relevant analytics that can be put to use. Like early CRM misadventures, many initial implementations of big data and ana-lytics fail simply because they aren’t in sync with the company’s day-to-day processes and decision- making norms. The aforementioned case of a com-pany that aimed to optimize prices illustrates how to avoid those common pain points. The company started with an analytics task force that convened a series of meetings with pricing and promotions managers to better understand the types of deci-sions  they  made  when  setting  prices—and  how  How to Benefi t from Big Data123To improve performance with advanced analytics, companies need to develop strengths in three areas.Multiple Data SourcesPrediction and Optimization Models Organizational TransformationCreatively source internal and external data.Focus on the biggest drivers of performance.Create simple, understandable tools for people on the front lines.Upgrade IT architecture and infrastructure for easy merging of data.Build models that balance complexity with ease of use.Update processes and develop capabilities to enable tool use.those choices ultimately aff  ected revenue and cus-tomer  retention.  Model  designers  also  inquired  about the types of business judgments that manag-ers make to align their actions with broader com-pany goals. These conversations ensured that both pricing analytics and resulting scenario tools would complement existing decision processes. The mod-eling allowed the company to reach its ultimate goal: more-effective management of price and volume trade-off  s as product launches proliferated.Embed analytics into simple tools for the front lines. Managers need transparent methods for using the new models and algorithms on a daily ba-sis. By necessity, terabytes of data and sophisticated modeling are required to sharpen marketing, risk management, and operations. The key is to separate the statistics experts and software developers from the managers who use the data-driven insights. One large industrial company, for instance, sought to bet-ter forecast workforce needs to re  ect local market variations. Historically, as the company had tried to keep labor costs low, it had often found itself short-staff  ed in some markets, leading to signi  cant over-time costs and service snafus. To remedy the problem, the company convened a small working group of analysts and IT programmers who developed a series of predictive models that 82  Harvard Business Review October 2012

Making advanced analytics Work for youliteracy. Managers must come to view analytics as central to solving problems and identifying oppor-tunities—to make it part of the fabric of daily opera-tions. Efforts will vary depending on a company’s goals and desired time line. Adult learners often benefit from a “field and forum” approach, whereby they participate in real-world, analytics-based work-place decisions that allow them to learn by doing.At one industrial services company, the mission was to get basic analytics tools into the hands of its roughly 200 sales managers. Training began with an in-field assignment to read a brief document and col-lect basic facts about the market. Next managers met in centralized, collaborative training sessions during which they figured out how to use the tools and mar-ket facts to improve sales performance. They then returned to the field to apply what they had learned and,  several  weeks  later,  reconvened  to  review  progress, receive coaching, and learn about second-order analysis of their data. This process enabled a four-person team to eventually build capabilities across the entire sales management organization.Adjusting culture and mind-sets typically re-quires a multifaceted approach that includes train-ing, role modeling by leaders, and incentives and metrics to reinforce behavior. One large consumer-products company applied such an approach suc-cessfully.  It  created  a  sophisticated  program  to  improve the profitability of promotional spending with its retailers. The launch included training—led by company management—and a new promotions-analysis tool for sales representatives. However, af-ter an initial whirlwind of activity, the program and use of the tool fizzled. The obstacle was that com-pany incentives and reporting protocols for sales managers tracked sales and sales growth, not prof-its. As a result, the managers considered the profit-focused program to be bureaucratic overhead that was unrelated to their key sales goals. After a series Using a simple tool to deliver complex analytics substantially improved workforce planning and reduced the need for new hires and overtime.forecast workforce availability on the basis of factors such as vacation time, absenteeism, and work rules in labor contracts. The models incorporated millions of new data points on thousands of employees across dozens of locations. But rather than providing man-agers with reams of data and complex models, they created a simple visual interface that highlighted projected workforce needs and necessary actions. Ultimately, that approach of using a simple tool to deliver complex analytics substantially improved workforce planning and reduced the need for new hires and overtime.Develop capabilities to exploit big data.Even with simple and usable models, most organiza-tions will need to upgrade their analytical skills and of discussions with the managers, the company re-launched the program, offered new incentives for improving profits, and tailored reports to profit-related data. Although ongoing training and coach-ing was necessary, the efforts gradually produced a shift in mind-set such that the power of promotions analytics is now used to further the common goal of increasing profitability.the era of big data is evolving rapidly, and our ex-perience suggests that most companies should act now. But rather than undertaking massive over-hauls of their companies, executives should con-centrate on targeted efforts to source data, build models, and transform the organizational culture. Such efforts will play a part in maintaining flexibility. That nimbleness is essential, given that the informa-tion itself—along with the technology for managing and analyzing it—will continue to grow and change, yielding a constant stream of opportunities. As more companies learn the core skills of using big data, building superior capabilities may soon become a decisive competitive asset. hbr reprint R1210EhbR.oRgoctober 2012 harvard business Review 83

Harvard Business Review Notice of Use Restrictions, May 2009Harvard Business Review and Harvard Business Publishing Newsletter content on EBSCOhost is licensed forthe private individual use of authorized EBSCOhost users.  It is not intended for use as assigned course materialin academic institutions nor as corporate learning or training materials in businesses. Academic licensees maynot use this content in electronic reserves, electronic course packs, persistent linking from syllabi or by anyother means of incorporating the content into course resources. Business licensees may not host this content onlearning management systems or use persistent linking or other means to incorporate the content into learningmanagement systems. Harvard Business Publishing will be pleased to grant permission to make this contentavailable through such means. For rates and permission, contact permissions@harvardbusiness.org

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Please read the following Harvard Business Review – MIT Sloan Management Review Article:

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Making Advanced Analytics Work For You
by Dominic Barton and David Court
Harvard Business Review, October 2012

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Please, answer for your INITIAL posting and discuss the following questions:

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1. Detailed – Comprehensive Summary for THIS article.

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How to Write a Summary of an Article

https://owlcation.com/academia/How-to-Write-a-Summary (Links to an external site.)

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Your Detailed – Comprehensive Summary for THIS article post should be no less of 1,700 words.

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2. Which are the three most CRITICAL ISSUES of THIS article? Please explain why? and analyze, and discuss in great detail …

For EACH Critical Issue please post at least two strong comprehensive paragraphs

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3. Which are the three most relevant LESSONS LEARNED of THIS article? Please explain why? and analyze, and discuss in great detail …

For EACH Lesson Learned please post at least two strong comprehensive paragraphs

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4. Which are the three most important BEST PRACTICES of THIS article? Please explain why? and analyze, and discuss in great detail …

For EACH Best Practice please post at least two strong comprehensive paragraphs

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5. How can you relate THIS article with the TOPICS COVERED in class? Please explain, analyze, and discuss in great detail …

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6. Do you see any alignment of the concepts described in THIS article with the class concepts reviewed in class? Which are those alignments and misalignments? Why? Please explain, analyze, and discuss in great detail …

I expect high caliber reviews with top analyses and interesting insights for THIS article !!

If you have any questions, please let me know.

1. What was the indian removal act? explain how this act was justified by president Andrew Jackson.

2. Discuss the role of the of the mass media in transmitting and perpetuating ethnic stereotypes. Provide some examples from current television and motion pictures.

3. Select an American ethnic group and discuss the extent to which its members have been assimilated. Discuss various social dimensions in which this group has displayed either strong, mild, or weak assimilation.

4. Explain why blacks rather than other groups were chosen to be slaves in american society

5. Explain how a European-based racist ideology was developed to support the slave system.

 

2 pages each question total of 10 pages. No plagiarism.

Please answer completely at least two pages each question double space font 12 Times New Roman. You must use personal examples and utilize content of the course. Answers will be incomplete without the theories that we did in the course.  Please read the instructions carefully for the exam.  Plagiarism will not be tolerated and I will read every word. 90 % of your exam answers must be your own words and if that is not the case it will be reflected in your grade earned. Any citation from sources must be clearly cited.  Please do not use other student’s work. Copy and paste will not tolerated. I expect many personal examples and the theories that were taught in the course on the exam.  Please follow all my instructions.

1.This is applied Sociology.  Based on Max Weber’s Multidimensional class model of wealth, power, and prestige, how do entertainers and reality TV personalities, such as Lady Gaga and Kardashians or rich entertainers in geral, become wealthy and well known worldwide? Where do individuals such as these fit in Weber’s system of Stratification.

2. Discuss why employment be based on meritocracy, need, or affirmative action-type policies designed to bring about greater diversity?

3. What is the difference between income and wealth? and (b) why do we need to understand these differences in order to realistically comprehend the meaning of social class?

4. Compare and contrast Max Weber’s and Karl Marx’s approach to defining social class? What are the strengths and weaknesses of each method?

5. What are the examples of horizontal and vertical mobility?

b. Which do you think is more prevalent in the United States today? Why? Discuss.

6a. Study the article on Percy Julian struggles and explain the factors influencing his successes despite all odds?

b. Discuss ways does inequality, class and race impact social and intellectual development in America

c. What Lesson should we learn from the experiences of Percy Julian?

1. Analysis on the Threats Defense Argument
   Overview
   The information you provided in your PowerPoint presentation on Threats to the Global Environment has led to productive debates at the United Nations General Assembly! Hence, they are now asking you to create an additional analysis report to respond to the issues raised in these debates.
   Your fourth project as a consultant for the United Nations is to develop a report that addresses the issues raised by some of the member states of the United Nations.
   Instructions
   Step I. Consider the Issues
   In the Presentation on the Threats to the Global Environment, you were asked to create a PowerPoint identifying the four most critical threats out of eight threats provided:
   • Energy sources.
   • Globalization.
   • Lack of educational opportunities.
   • Inappropriate use of technology.
   • Civil war.
   • Poor health of entire population.
   • Cultural taboos.
   • Climate change.
   • There are four remaining threats that you did not discuss in your PowerPoint. This assignment will focus on two of those four. Here are the specific tasks you will complete:
   • Review the remaining four threats that you did not use in Presentation on the Threats to the Global Environment.
   • Pick two of the four to focus on in Analysis on the Threats Defense Argument assignment.
   • Identify the reasons why you think these two threats are less critical than the four threats you chose for your PowerPoint presentation.
   • Step II. Prepare Your Report
     The U.N. has given you the following guidelines.
     Introduction
     Briefly introduce the topic of the analysis (about 100–150 words):
   1. State the topic and intent of the paper.
   2. Identify the two threats you will discuss in the paper in the order in which they appear in the paper.
   3. Section I. Threat 1
   4. Write an opening statement describing the perception of the threat.
   5. Write one page giving three reasons that explain why you saw this threat as less critical than the four you chose for your presentation in the Presentation on the Threats to the Global Environment assignment.
   6. Support these reasons with at least three credible sources.
   7. Section II. Threat 2
   8. Write an opening statement describing the perception of the threat.
   9. Write one page giving three reasons to explain why you saw this threat as less critical than the four you chose for your presentation in the Presentation on the Threats to the Global Environment assignment.
   10. Support these reasons with at least three credible sources.
   11. Conclusion
   • Offer a summary (one page or less) of your defense of your choices that the United Nations can use to address their prioritization concerns.
   • Formatting Requirements
   • Your paper may consist of up to four pages (not including the cover or reference pages).
   • Create headings for each section of your paper as follows:
     • Introduction.
     • Threat 1 (include the name of your chosen threat).
     • Threat 2 (include the name of your chosen threat).
     • Conclusion.
   • Use and cite 4–6 credible sources in your analysis. You may use the same source for more than one threat as long as you use a minimum of four different sources. A list of potential resources is available at the end of this course guide.
   • This course requires the use of Strayer Writing Standards. For assistance and information, please refer to the Strayer Writing Standards link in the left-hand menu of your course. Check with your professor for any additional instructions.
     The specific course learning outcome associated with this assignment is as follows:
   • Assess the current threats to the global environment.
   • Grading for this assignment will be based on answer quality, logic/organization of the paper, and language and writing skills.
2. By submitting this paper, you agree: (1) that you are submitting your paper to be used and stored as part of the SafeAssign™ services in accordance with the Blackboard Privacy Policy; (2) that your institution may use your paper in accordance with your institution’s policies; and (3) that your use of SafeAssign will be without recourse against Blackboard Inc. and its affiliates.