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Air-Traffic Delays. Increased air-traffic control delays caused by higher travel demand and related airport congestion were expected to negatively influ- ence customer satisfaction. 5. Environmental Regulation. Following actions in Europe, various U.S. groups were advocating new standards and taxes on airline emissions. 6. Open Skies Agreement. Legislation allowing greater access to U.S. markets by non-U.S. carriers was expected to increase competitive pressure. Establishing a major hub in a city like Chicago or Atlanta required a huge investment for gate acquisition and terminal construction. JetBlue’s new facility at JFK in New York opened in 2009 and cost about $800 million. Although hubs created inconveniences for travelers, hub systems were an efficient means of distributing services across a wide network. The major airlines were very protective of their so-called “fortress” hubs and used the hubs to control various local markets. For example, Northwest (now Delta) handled about 80% of Detroit’s passengers and occupied nearly the entire new Detroit terminal that opened in 2002. And, Northwest’s deal with the local government assured that it would be the only airline that could have a hub in Detroit. When Southwest entered the Detroit market, the only available gates were already leased by Northwest. Northwest sub- leased gates to Southwest at rates 18 times higher than Northwest’s costs. Southwest eventually withdrew from Detroit, and then re-entered, one of only three markets Southwest had abandoned in its history (Denver and Beaumont, Texas, were the other two; Southwest re- entered Denver in 2006). Recent U.S. Airline Industry Performance Despite steadily growing customer demand, the airline industry always seemed to be one recession away from crisis. In 2013, the major airlines were on track to be profitable, a marked contrast to the heavy losses of just a few years earlier (with the exception of Southwest). The continuing consolidation in the industry was expected to lead to lower operating costs and higher ticket prices. After the September 11, 2001, terrorist attacks, domestic airlines lost about $30 billion. The continuing specter of terrorism cast a long shadow on the global airline industry. In the United States, passengers were frustrated by increasingly more-invasive security pro- cedures. Volatile fuel costs were a constant uncertainty, and new entrants continued to put pressure on the incumbents. Other pressures on the industry included: 1. Customer Dissatisfaction with Airline Service. Service problems were leading to calls for new regu- lation of airline competitive practices. 2. Aircraft Safety Maintenance. The ageing of the gen- eral aircraft population meant higher maintenance costs and eventual aircraft replacement. The introduc- tion of stricter government regulations for older planes placed new burdens on operators of older aircraft. 3. Debt Servicing. The airline industry’s debt load exceeded U.S. industry averages. Southwest Airlines Background In 1966, Herb Kelleher was practicing law in San Antonio when a client named Rollin King proposed starting a short-haul airline similar to California-based Pacific Southwest Airlines. The airline would fly the Golden Triangle of Houston, Dallas, and San Antonio and, by staying within Texas, avoid federal regulations. Kelleher and King incorporated a company, raised initial cap- ital, and filed for regulatory approval from the Texas Aeronautics Commission. Unfortunately, the other xas-based airlines, namely Braniff, Contin and Trans Texas (later called Texas International), opposed the idea and waged a battle to prohibit Southwest from flying. Kelleher argued the company’s case before the Texas Supreme Court, which ruled in Southwest’s favor. The U.S. Supreme Court refused to hear an appeal filed by the other airlines. In late 1970, it looked as if the com- pany could begin flying. Southwest began building a management team, and the purchase of three surplus Boeing 737s was negoti- ated. Meanwhile, Braniff and Texas International con- tinued their efforts to prevent Southwest from flying. The underwriters of Southwest’s initial public stock offering withdrew and a restraining order against the company was obtained two days before its scheduled inaugural flight. Kelleher again argued his company’s case before the Texas Supreme Court, which ruled in Southwest’s favor a second time, lifting the restraining order. Southwest Airlines began flying the next day, June 18, 1971.” When Southwest began flying to three Texas cities, the firm had three aircraft and 25 employees. Initial flights were out of Dallas’ older Love Field airport and Houston’s Hobby Airport, both of which were closer to downtown than the major international airports. Flamboyant from the beginning, original flights were staffed by flight attendants in hot pants. By 1996, the flight attendant uniform had evolved to khakis and polo shirts. The Luv theme was a staple of the airline from Table 1 Southwest Across the Years 1971 1999 2007 2012 Size of Fleet (End of Year) 4 306 515 Number of Employees 195 29,005 34,378 Number of Passengers Carried 108,554 52,600,000 101,947,800 Number of Cities Served 3 55 64 Number of Trips Flown 6,051 602,578 1,160,699 Total Operating Revenues (Millions $) 2.33 4,736 7,369 Net Income (Millions $) -3.8 433 645 Sources: Company press releases and Southwest Airlines Fact Sheet at http://www.southwest.com/about_swa/press/factsheet.html. 694 46,000 109,000,00 97 >1,284,800 17,100 421 designed to increase revenue by (a) bringing in new customers, including new Rapid Rewards members, as well as new holders of Southwest’s co-branded Chase Visa credit card; (b) increasing business from existing customers; and (c) strengthening Rapid Rewards hotel, rental car, credit card, and retail partnerships. Recent Service Changes In 2007, Southwest made various changes to its service offerings, including Three new fare categories, including higher-tier fares for business travelers. New boarding processes; for example, travelers could pay extra to board first. Allowing customers with high status in the frequent flier program to board first. Increased emphasis on corporate sales. Promoted the “two-bags-fly-free campaign” aggres- sively. The rationale for the 2007 changes was explained by CEO Gary Kelly: We’ve always been a business traveler’s airline. At the same time, over 37 years we hadn’t done much to try to cus- tomize the travel experience for the varieties of customer needs that we had. It was one-size-fits-all, and in today’s competitive environment we felt that was not the best way to remain on top. We had the desire to improve our overall customer experience for the business traveler. In 2011, Southwest launched its new Rapid Rewards fre- quent flyer program. Under the new program, members earned points for every dollar spent, whereas under the prior program customers earned credits for flight segments flown. The new frequent flyer program was Table 2 Operating Data Southwest’s Performance Southwest bucked the airline industry trend by earn- ing a profit for 40 consecutive years. Among the major airlines, Southwest consistently ranked first in fewest over- all customer complaints as published in the Department of Transportation’s Air Travel Consumer Report. For example, in December 2012, there were 18 complaints reported against Southwest and 140 against United. In Zagat’s 2010 airline survey, Southwest won awards for top website; best consumer on-time estimates-domestic; best check-in experience; best value-domestic; and best luggage policy-domestic. The average Southwest flight had a duration of about one hour and 55 minutes and a length of 694 miles. This was up from 462 miles in 1999 and 394 in 1996. Each plane flew about seven flights daily, almost twice the industry average. Planes were used an average of 13 hours a day, about 40% more than major carriers like Delta and United. Table 2 shows that Southwest’s cost per available seat mile was lower than the legacy Alaska Southwest American Delta JetBlue United US Airways 86.6% 80.4% 84.1% 85.796 84.3% 85.1% 14.52 14.18 16.79 11.34 17.07 Load Factor Operating cost per ASM (cents) Revenue per ASM (cents) On-time departure rank On-time arrival rank 16.71 18.22 85.7% 17.79 18.89 16.49 14.82 16.30 12.45 17.26 #2 #11 #14 #5 #13 15 #3 #2 #8 #15 #4 #12 14 #5 Calculated by dividing operating revenue by available seat miles. Source: U.S. Department of Transportation (US DOT).

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Kendalls Department store was established in 1903. Originally it was a piece[1]goods and shoe outlet. It soon branched into other merchandise and by 1908 it had become a department store. Living up to its mission statement ‘Satisfaction Guaranteed or your Money back’ Kendalls prospered.

In 1921 the second Kendalls opened in a nearby town and over the next few decades Kendalls opened several stores in many small towns with each store employing an average of six front line managers. One of the problems experiencing in this growth pattern was recruiting and selecting adequately trained store managers and department supervisors.

Until recently Kendall’s policy had been to hire people with prior managerial experience in other retail outlets. The current managing director of the chain, Loiusa Kendall, great-granddaughter of the founder, had questioned this practice for some time. She felt that it was because Kendalls had to pay more, to lure a good employee from another store and in addition, the employee had to be retrained in Kendalls methods-a further expense.

In January 2019 Louisa Kendall contracted with Alison Decker, an experienced training consultant, to outsource employee training. Alison’s first priority was to develop an in-house training program for prospective and newly promoted store supervisors.

Case study questions

1. How would you analyse the training needs for developing an effective training program at Kendall store?

2. Develop a checklist of topics that could be included in the training program of newly appointed store supervisors.

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Developing and altering organizational charts is an important skill for strategists to possess. This exercise can improve your skill in altering an organization’s hierarchical structure in response to new strategies being formed. Instructions Step 1 Develop an organizational chart for Nestlé. On a separate sheet of paper, answer the following questions: • What type of organizational chart have you illustrated for Nestlé? • What improvements would you recommend for the Nestlé organizational chart? Give your reasoning for each suggestion. Step 2 Now consider the following: • What aspects of your Nestlé chart do you especially like? • What type of organizational chart do you believe would best suit Nestlé? Why?

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The Return to Education and the Gender Gap

In addition to its intellectual? pleasures, education has economic rewards. As the boxes in Chapters 3 and 5? show, workers with more education tend to earn more than their counterparts with less education. The analysis in those boxes was? incomplete, however, for at least three reasons.? First, it failed to control for other determinants of earnings that might be correlated with educational? achievement, so the OLS estimator of the coefficient on education could have omitted variable bias.? Second, the functional form used in Chapter

5long dash—a

simple linear

relationlong dash—implies

that earnings change by a constant dollar amount for each additional year of? education, whereas one might suspect that the dollar change in earnings is actually larger at higher levels of education.? Third, the box in Chapter 5 ignores the gender differences in earnings highlighted in the box in Chapter 3.

All these limitations can be addressed by a multiple regression analysis that controls for determinants of earnings? that, if?omitted, could cause omitted variable bias and that uses a nonlinear functional form relating education and earnings. The Return to Education and the Gender Gap table summarizes regressions estimated using data on? full-time workers, ages 30 through? 64, from the Current Population Survey? (the CPS data are described in Appendix? 3.1). The dependent variable is the logarithm of hourly?earnings, so another year of education is associated with a constant percentage increase? (not dollar? increase) in earnings.

The Return to Education and the Gender Gap table has four salient results.? First, the omission of gender in regression? (1) does not result in substantial omitted variable? bias: Even though gender enters regression? (2) significantly and with a large?coefficient, gender and years of education are? uncorrelated; that?is, on average men and women have nearly the same levels of education.?Second, the returns to education are economically and statistically significantly different for men and? women: In regression? (3), the

t?-statistic

testing the hypothesis that they are the same is 7.02.? Third, regression? (4) controls for the region of the country in which the individual? lives, thereby addressing potential omitted variable bias that might arise if years of education differ systematically by region. Controlling for region makes a small difference to the estimated coefficients on the education? terms, relative to those reported in regression? (3). Fourth, regression? (4) controls for the potential experience of the? worker, as measured by years since completion of schooling. The estimated coefficients imply a declining marginal value for each year of potential experience.

The estimated economic return to education in regression? (4) is? 10.32% for each year of education for men and? 11.66% for women. Because the regression functions for men and women have different? slopes, the gender gap depends on the years of education. For 12 years of? education, the gender gap is estimated to be? 29.0%; for 16 years of? education, the gender gap is less in percentage? terms, 23.7%.

These estimates of the return to education and the gender gap still have? limitations, including the possibility of other omitted?variables, notably the native ability of the? worker, and potential problems associated with the way variables are measured in the CPS.? Nevertheless, the estimates in the Return to Education and the Gender Gap table are consistent with those obtained by economists who carefully address these limitations. A survey by the econometrician David Card? (1999) of dozens of empirical studies concludes that labor? economists’ best estimates of the return to education generally fall between? 8% and? 11%, and that the return depends on the quality of the education. If you are interested in learning more about the economic return to? education, see Card?(1999).

Read the box “The Return to Education and the Gender Gap.” The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressor Years of education 0.1001* (0.0011) -0.432** (0.024) 0.0121* (0.0017) 0.1051 (0.0012) 0.1035* (0.0009) 0.1050 (0.0009) – 0.263** (0.004) Female 0.451* (0.024) 0.0134 (0.0017) 0.0149** (0.0012) – 0.000203** (0.000027) 0.095** (0.006) 0.092 (0.006) -0.021* (0.007) Female x Years of education Potential experience Potential experience Midwest South West
1.533 (0.012) 0.208 Intercept 1.629 (0.012) 0.258 1.697* (0.016) 0.258 1.433 (0.023) 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and Wost are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northoast). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the +5% or 1% significance level. Scenario A Consider a man with 14 years of education and 5 years of experience who is from a western state. Use the results from column (4) of the table and the method in Key Concept 81 to estimate the expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience. The expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience is%. (Round your response to two decimal places.)
The Expected Effect on Y of a Change in X4 in the Nonlinear Regression Model The expected change in Y. ??, associated with the change in X1, ???, holding X2, , Xk constant, is the difference between the value of the population regression function before and after changing X1, holding X2,, XK constant. That is, the expected change in Yis the difference The estimator of this unknown population difference is the difference between the predicted values for these two cases. Let fX,. X2. Xk) be the predicted value of Y based on the estimator fof the population regression function. Then the predicted change in Yis 12k 12 k

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