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This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study.

This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies’ success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing.

Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment.

This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers’ understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes.

Alice Monroe was an admissions officer at the Kellogg School of Management at Northwestern University. It was early January 2017 and Alice had enrolled in Northwestern's 403(b) retirement plan two months earlier. After spending a considerable amount of time examining the mutual funds available through the university's retirement plan, Alice had picked two to invest in: a large-cap equity growth fund and a mid-cap equity fund. (See the related case "Selecting Mutual Funds for Retirement Accounts (A).") Her initial allocations were 50% of her investment dollars in each fund.

Upon further reflection, however, she realized these initial allocations were somewhat simplistic. She recalled, from an investments class she had taken at college, the topic of modern portfolio theory, which held that by adding more funds to her portfolio she might be able to achieve greater diversification and thereby reduce the overall risk of her portfolio and/or achieve a higher expected return. Alice now was considering adding an intermediate-term bond fund and a real estate fund to her retirement account.

She hoped to use modern portfolio theory to prove that these new funds would indeed help her diversify her portfolio. If they did, she would also reassess her portfolio weights to determine the optimal allocation.

Alice Monroe, a 30-year-old married mother of two, was an admissions officer at the Kellogg School of Management at Northwestern University. She was just completing her first year of service at Northwestern and qualified for the university's 403(b) retirement plan. It was early October 2017, and she had until the end of the month to decide if and to what extent she would participate in Northwestern's retirement plan–that is, how much of her salary should she put into the retirement plan, and into which mutual fund or funds should she allocate her savings?

The case includes background on defined contribution and benefit plans as well as mutual funds. It goes into detail about Northwestern's retirement plan, including data on the performance of 15 of the plan's core mutual funds. The case also provides each fund's strategy, Morningstar Rating and Morningstar Category, expense ratio, assets under management, turnover rate, and historical performance for the last 10 years.

Using modern portfolio theory (diversification and risk-return trade-off) and with an understanding of mutual fund fees and the tax advantages of retirement savings, students will decide how much Alice should invest and in which mutual funds.

The key objectives of this case are to: (1) familiarize students with a simple version of the Markowitz optimal-asset allocation model; (2) develop students' intuition regarding optimal-asset allocation as specific inputs into the model (e.g., expected returns, standard deviations, correlations) change values; and (3) develop students' intuition regarding constraints that alternative investors may face (e.g., the presence of shorting constraints) and their impact on the optimal portfolio. The case includes an Excel spreadsheet, which contains relevant data (e.g., returns, standard deviations, correlations) on several assets and an Excel model that takes three of those assets and makes use of the Excel Solver Add-In to compute optimal weights for the three asset portfolio as well as the expected return, standard deviation, and Sharpe ratio of the optimal portfolio. Students are asked to alter many of the inputs into the model and explain the effects of those changes on the optimal portfolio.

The case is meant to be used in an introductory course on Data Visualization or Analytics in either a business school or any other context where students have had some exposure to management topics. This case primarily focuses on introducing students to the basic philosophy and techniques of exploratory data analysis (EDA). The Steaming Mug, a US based hot beverage chain is provided as a context for engaging in a hands-on exploratory data analysis exercise to understand the strengths and weaknesses of the company's operations and performance. The learning from exploring this case will help students understand that many different insights can be drawn from the given data. The students will understand that there is no “one correct path” for this analysis. The values of getting multiple groups to present their findings is that students realise that there are many other angles that could have been explored, than the path they had gone down.

Marketing, retail.

The case study is specific to the marketing demographics of Indian shoppers with respect to organized retail stores, and therefore, the inter-relationships between various design elements and the relative importance of certain parameters discussed in the text may not follow the same pattern elsewhere in the world.

The case emulates the real-life situation of an organized retail store, Super Mart, to understand the inculcation of voice of the customer in the design of organized retail stores in India. It gives insights about factors which influence the shopping intent of customers while giving information about the inter-relationships among various design characteristics. It also gives an idea about inter-dependence between design characteristics and customer requirements. This is followed by certain questions, the responses to which can be interpreted from the text and the data provided therein.

The case aims to educate its audience about the following aspects of organized retail business: factors influencing offline shopping intent of customers; relative order of importance of customer requirements with respect to organized retail stores; inter-relationships between various design elements; and future trends in the organized retail space. Such a knowledge would help hone the skills of the next generation of business leaders in the retail space.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Data used to develop the case included primary data from employees and supervisors of a commercial floorcovering manufacturing plant in Northwest Georgia. The case company is not disguised.

The survey was developed using existing instruments from the Organizational Behavior and Human Resources Literature. Instruments were listed in Exhibits 2 through 7. The survey administration had the support of the Vice President for Resources and Facilities, and employees and their supervisors were given time to complete the surveys. The data gathered was analyzed by the researcher using SPSS statistical software.

Established in 1957, J&J started as a family-owned business but had grown and diversified its product offerings by focusing on commercial flooring. It survived several economic downturns and remained competitive in a market dominated by more prominent flooring manufacturers. J&J Industries strived to empower its 800 employees with various incentive programs. Employees remained loyal to J&J; many had worked for the company for over 15 years. However, management wanted to measure the impact of empowering and initiatives on employee performance and satisfaction to determine the real power of employee incentive programs. The Resources and Facilities Vice President employed Professor Lopez, a Management Professor, to develop a survey to measure these constructs and analyze the data to guide future incentive programs. Data from the employee and supervisor survey was provided along with the statistical analysis results for interpretation and recommendations for VP Fordham.

The target audience for this case is primarily students in a research methodology course and students studying quantitative regression analysis and interpretation. The focus is predominantly on graduate-level students in Master of Business Administration or Master of Accounting programs in business. Graduate students should have completed courses in management or organizational behavior, business statistics or quantitative methods or data visualization and cleaning as background knowledge for this case. Specifically, students should understand regression analysis and know when and how the tool is used for managerial decision-making.

EuroPet S.A. was a multinational company operating gas stations in many European countries. There was a growing propensity for supermarkets to attach gas stations to their retail operations, which was developing into a major threat to EuroPet. As a result, in the mid-1990s, the company began to develop and brand its own convenience stores co-located with its gas stations. However, the company was spending much more on advertising the convenience stores than its competitors did. Management now had to decide if the increase in sales attributed to advertising efforts justified the advertising spend by analyzing the market data from one large metropolitan area: Marseille, France.

Students will learn: how to use cross-tabs and other marketing research tools to identify segmentation descriptors; how to analyze data and interpret results; and how these research results could guide new product development and positioning strategies in order to effectively target relevant customer segments.

Controladora Comercial Mexicana, a Mexican retailer, had successfully managed the bankruptcy process and was ready to emerge from its problems, primarily caused by speculation and excessive debt, and begin operations anew. Was the restructured Comerci capable of regaining its position as a premier retailer, and more importantly, was the firm capable of repaying the high level of debt that it carried following bankruptcy reorganization? How strong was the reorganized firm? Had Comerci truly left its problems behind in bankruptcy court, or would history repeat itself? How could Comerci raise funds needed for growth – through additional debt? Though asset sales?

This case was researched using publicly available information, including the company's financial statements, bankruptcy filings, news stories about the bankruptcy and financial data bases (e.g. ISI Emerging Markets, Economática, Capital IQ, etc.) to obtain information about the competitors and from financial analysts.

This case is intended for advanced undergraduate or MBA electives in finance. Students should have a basic understanding of valuation and financing before attempting this case. The case could also be used in a corporate finance or banking class to illustrate bankruptcy and credit risk, or could be used in an international business class to illustrate the differences between USA and international bankruptcies.

The financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.

The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.