Ch8 Test Bank

b. The probability for any individual value of a continuous random variable is zero, but for discrete random variables it is not. c. Probability for continuous random variables means finding the area under a curve, while for discrete random variables it means summing individual probabilities. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 2. Which of the following is always true for all probability density functions of continuous random variables? a. The probability at any single point is zero. b. They contain an uncountable number of possible values. c. The total area under the density function f(x) equals 1. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 3. Suppose f(x) = 0. 25. What range of possible values can X take on and still have the density function be legitimate? a. [0, 4] b. [4, 8] c. [? 2, +2] d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 4. The probability density function, f(x), for any continuous random variable X, represents: a. ll possible values that X will assume within some interval a ? x ? b. b. the probability that X takes on a specific value x. c. the height of the density function at x. d. None of these choices. ANS:CPTS:1REF:SECTION 8. 1 5. Which of the following is true about f(x) when X has a uniform distribution over the interval [a, b]? a. The values of f(x) are different for various values of the random variable X. b. f(x) equals one for each possible value of X. c. f(x) equals one divided by the length of the interval from a to b. d. None of these choices. ANS:CPTS:1REF:SECTION 8. 1 6. The probability density function f(x) for a uniform random variable X defined over the interval [2, 10] is a. 0. 125 b. 8 c. 6 d. None of these choices. ANS:APTS:1REF:SECTION 8. 1 7. If the random variable X has a uniform distribution between 40 and 50, then P(35 ? X ? 45) is: a. 1. 0 b. 0. 5 c. 0. 1 d. undefined. ANS:BPTS:1REF:SECTION 8. 1 8. The probability density function f(x) of a random variable X that has a uniform distribution between a and b is a. (b + a)/2 b. 1/b ? 1/a c. (a ? b)/2 d. None of these choices. ANS:DPTS:1REF:SECTION 8. 1 9. Which of the following does not represent a continuous uniform random variable? . f(x) = 1/2 for x between ? 1 and 1, inclusive. b. f(x) = 10 for x between 0 and 1/10, inclusive. c. f(x) = 1/3 for x = 4, 5, 6. d. None of these choices represents a continuous uniform random variable. ANS:CPTS:1REF:SECTION 8. 1 10. Suppose f(x) = 1/4 over the range a ? x ? b, and suppose P(X 4) = 1/2. What are the values for a and b? a. 0 and 4 b. 2 and 6 c. Can be any range of x values whose length (b ? a) equals 4. d. Cannot answer with the information given. ANS:BPTS:1REF:SECTION 8. 1 11. What is the shape of the probability density function for a uniform random variable on the interval [a, b]? a. A rectangle whose X values go from a to b. b. A straight line whose height is 1/(b ? a) over the range [a, b]. c. A continuous probability density function with the same value of f(x) from a to b. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 TRUE/FALSE 12. A continuous probability distribution represents a random variable having an infinite number of outcomes which may assume any number of values within an interval. ANS:TPTS:1REF:SECTION 8. 1 13. Continuous probability distributions describe probabilities associated with random variables that are able to assume any finite number of values along an interval. ANS:FPTS:1REF:SECTION 8. 1 14. A continuous random variable is one that can assume an uncountable number of values. ANS:TPTS:1REF:SECTION 8. 1 15. Since there is an infinite number of values a continuous random variable can assume, the probability of each individual value is virtually 0. ANS:TPTS:1REF:SECTION 8. 1 16. A continuous random variable X has a uniform distribution between 10 and 20 (inclusive), then the probability that X falls between 12 and 15 is 0. 30. ANS:TPTS:1REF:SECTION 8. 1 17. A continuous random variable X has a uniform distribution between 5 and 15 (inclusive), then the probability that X falls between 10 and 20 is 1. . ANS:FPTS:1REF:SECTION 8. 1 18. A continuous random variable X has a uniform distribution between 5 and 25 (inclusive), then P(X = 15) = 0. 05. ANS:FPTS:1REF:SECTION 8. 1 19. We distinguish between discrete and continuous random variables by noting whether the number of possible values is countable or uncountable. ANS:TPTS:1REF:SECTION 8. 1 20. In practice, we frequently use a continuous distribution to approximate a discrete one when the number of values the variable can assume is countable but very large. ANS:TPTS:1REF:SECTION 8. 1 21. Let X represent weekly income expressed in dollars. Since there is no set upper limit, we cannot identify (and thus cannot count) all the possible values. Consequently, weekly income is regarded as a continuous random variable. ANS:TPTS:1REF:SECTION 8. 1 22. To be a legitimate probability density function, all possible values of f(x) must be non-negative. ANS:TPTS:1REF:SECTION 8. 1 23. To be a legitimate probability density function, all possible values of f(x) must lie between 0 and 1 (inclusive). ANS:FPTS:1REF:SECTION 8. 1 24. The sum of all values of f(x) over the range of [a, b] must equal one. ANS:FPTS:1REF:SECTION 8. 1 25. A probability density function shows the probability for each value of X. ANS:FPTS:1REF:SECTION 8. 1 26. If X is a continuous random variable on the interval [0, 10], then P(X 5) = P(X ? 5). ANS:TPTS:1REF:SECTION 8. 1 27. If X is a continuous random variable on the interval [0, 10], then P(X = 5) = f(5) = 1/10. ANS:FPTS:1REF:SECTION 8. 1 28. If a point y lies outside the range of the possible values of a random variable X, then f(y) must equal zero. ANS:TPTS:1REF:SECTION 8. 1 COMPLETION 29. A(n) ____________________ random variable is one that assumes an uncountable number of possible values. ANS:continuous PTS:1REF:SECTION 8. 1 30. For a continuous random variable, the probability for each individual value of X is ____________________. ANS: zero 0 PTS:1REF:SECTION 8. 1 31. Probability for continuous random variables is found by finding the ____________________ under a curve. ANS:area PTS:1REF:SECTION 8. 1 32. A(n) ____________________ random variable has a density function that looks like a rectangle and you can use areas of a rectangle to find probabilities for it. ANS:uniform PTS:1REF:SECTION 8. 1 33. Suppose X is a continuous random variable for X between a and b. Then its probability ____________________ function must non-negative for all values of X between a and b. ANS:density PTS:1REF:SECTION 8. 1 34. The total area under f(x) for a continuous random variable must equal ____________________. ANS: 1 one PTS:1REF:SECTION 8. 1 35. The probability density function of a uniform random variable on the interval [0, 5] must be ____________________ for 0 ? x ? 5. ANS: 1/5 0. 20 PTS:1REF:SECTION 8. 1 36. To find the probability for a uniform random variable you take the ____________________ times the ____________________ of its corresponding rectangle. ANS: base; height height; base length; width width; length PTS:1REF:SECTION 8. 1 37. You can use a continuous random variable to ____________________ a discrete random variable that takes on a countable, but very large, number of possible values. ANS:approximate PTS:1REF:SECTION 8. 1 SHORT ANSWER 38. A continuous random variable X has the following probability density function: f(x) = 1/4, 0 ? x ? 4 Find the following probabilities: a. P(X ? 1) b. P(X ? 2) c. P(1 ? X ? 2) d. P(X = 3) ANS: a. 0. 25 b. 0. 50 c. 0. 25 d. 0 PTS:1REF:SECTION 8. 1 Waiting Time The length of time patients must wait to see a doctor at an emergency room in a large hospital has a uniform distribution between 40 minutes and 3 hours. 39. {Waiting Time Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/140, 40 ? x ? 180 (minutes) PTS:1REF:SECTION 8. 1 40. {Waiting Time Narrative} What is the probability that a patient would have to wait between one and two hours? ANS: 0. 43 PTS:1REF:SECTION 8. 1 41. {Waiting Time Narrative} What is the probability that a patient would have to wait exactly one hour? ANS: 0 PTS:1REF:SECTION 8. 1 42. {Waiting Time Narrative} What is the probability that a patient would have to wait no more than one hour? ANS: 0. 143 PTS:1REF:SECTION 8. 1 43. The time required to complete a particular assembly operation has a uniform distribution between 25 and 50 minutes. a. What is the probability density function for this uniform distribution? b. What is the probability that the assembly operation will require more than 40 minutes to complete? c. Suppose more time was allowed to complete the operation, and the values of X were extended to the range from 25 to 60 minutes. What would f(x) be in this case? ANS: a. f(x) = 1/25, 25 ? x ? 50 b. 0. 40 c. f(x) = 1/35, 25 ? x ? 60 PTS:1REF:SECTION 8. 1 44. Suppose f(x) equals 1/50 on the interval [0, 50]. a. What is the distribution of X? b. What does the graph of f(x) look like? c. Find P(X ? 25) d. Find P(X ? 25) e. Find P(X = 25) f. Find P(0 X 3) g. Find P(? 3 X 0) h. Find P(0 X 50) ANS: a. X has a uniform distribution on the interval [0, 50]. b. f(x) forms a rectangle of height 1/50 from x = 0 to x = 50. c. 0. 50 d. 0. 50 e. 0 f. 0. 06 g. 0. 06 h. 1. 00 PTS:1REF:SECTION 8. 1 Chemistry Test The time it takes a student to finish a chemistry test has a uniform distribution between 50 and 70 minutes. 45. {Chemistry Test Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/20, 50 ? x ? 70 PTS:1REF:SECTION 8. 1 46. {Chemistry Test Narrative} Find the probability that a student will take more than 60 minutes to finish the test. ANS: 0. 50 PTS:1REF:SECTION 8. 1 47. {Chemistry Test Narrative} Find the probability that a student will take no less than 55 minutes to finish the test. ANS: 0. 75 PTS:1REF:SECTION 8. 1 48. {Chemistry Test Narrative} Find the probability that a student will take exactly one hour to finish the test. ANS: 0 PTS:1REF:SECTION 8. 1 49. {Chemistry Test Narrative} What is the median amount of time it takes a student to finish the test? ANS: 60 minutes PTS:1REF:SECTION 8. 1 50. {Chemistry Test Narrative} What is the mean amount of time it takes a student to finish the test? ANS: 60 minutes PTS:1REF:SECTION 8. 1 Elevator Waiting Time In a shopping mall the waiting time for an elevator is found to be uniformly distributed between 1 and 5 minutes. 1. {Elevator Waiting Time Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/4, 1 ? x ? 5 PTS:1REF:SECTION 8. 1 52. {Elevator Waiting Time Narrative} What is the probability of waiting no more than 3 minutes? ANS: 0. 50 PTS:1REF:SECTION 8. 1 53. {Elevator Waiting Time Narrative} What is the probability that the elevator arrives in the first minute and a half? ANS: 0. 125 PTS:1REF:SECTION 8. 1 54. {Elevator Waiting Time Narrative} What is the median waiting time for this elevator? ANS: 3 minutes PTS:1REF:SECTION 8. 1 Ch8 Test Bank b. The probability for any individual value of a continuous random variable is zero, but for discrete random variables it is not. c. Probability for continuous random variables means finding the area under a curve, while for discrete random variables it means summing individual probabilities. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 2. Which of the following is always true for all probability density functions of continuous random variables? a. The probability at any single point is zero. b. They contain an uncountable number of possible values. c. The total area under the density function f(x) equals 1. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 3. Suppose f(x) = 0. 25. What range of possible values can X take on and still have the density function be legitimate? a. [0, 4] b. [4, 8] c. [? 2, +2] d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 4. The probability density function, f(x), for any continuous random variable X, represents: a. ll possible values that X will assume within some interval a ? x ? b. b. the probability that X takes on a specific value x. c. the height of the density function at x. d. None of these choices. ANS:CPTS:1REF:SECTION 8. 1 5. Which of the following is true about f(x) when X has a uniform distribution over the interval [a, b]? a. The values of f(x) are different for various values of the random variable X. b. f(x) equals one for each possible value of X. c. f(x) equals one divided by the length of the interval from a to b. d. None of these choices. ANS:CPTS:1REF:SECTION 8. 1 6. The probability density function f(x) for a uniform random variable X defined over the interval [2, 10] is a. 0. 125 b. 8 c. 6 d. None of these choices. ANS:APTS:1REF:SECTION 8. 1 7. If the random variable X has a uniform distribution between 40 and 50, then P(35 ? X ? 45) is: a. 1. 0 b. 0. 5 c. 0. 1 d. undefined. ANS:BPTS:1REF:SECTION 8. 1 8. The probability density function f(x) of a random variable X that has a uniform distribution between a and b is a. (b + a)/2 b. 1/b ? 1/a c. (a ? b)/2 d. None of these choices. ANS:DPTS:1REF:SECTION 8. 1 9. Which of the following does not represent a continuous uniform random variable? . f(x) = 1/2 for x between ? 1 and 1, inclusive. b. f(x) = 10 for x between 0 and 1/10, inclusive. c. f(x) = 1/3 for x = 4, 5, 6. d. None of these choices represents a continuous uniform random variable. ANS:CPTS:1REF:SECTION 8. 1 10. Suppose f(x) = 1/4 over the range a ? x ? b, and suppose P(X 4) = 1/2. What are the values for a and b? a. 0 and 4 b. 2 and 6 c. Can be any range of x values whose length (b ? a) equals 4. d. Cannot answer with the information given. ANS:BPTS:1REF:SECTION 8. 1 11. What is the shape of the probability density function for a uniform random variable on the interval [a, b]? a. A rectangle whose X values go from a to b. b. A straight line whose height is 1/(b ? a) over the range [a, b]. c. A continuous probability density function with the same value of f(x) from a to b. d. All of these choices are true. ANS:DPTS:1REF:SECTION 8. 1 TRUE/FALSE 12. A continuous probability distribution represents a random variable having an infinite number of outcomes which may assume any number of values within an interval. ANS:TPTS:1REF:SECTION 8. 1 13. Continuous probability distributions describe probabilities associated with random variables that are able to assume any finite number of values along an interval. ANS:FPTS:1REF:SECTION 8. 1 14. A continuous random variable is one that can assume an uncountable number of values. ANS:TPTS:1REF:SECTION 8. 1 15. Since there is an infinite number of values a continuous random variable can assume, the probability of each individual value is virtually 0. ANS:TPTS:1REF:SECTION 8. 1 16. A continuous random variable X has a uniform distribution between 10 and 20 (inclusive), then the probability that X falls between 12 and 15 is 0. 30. ANS:TPTS:1REF:SECTION 8. 1 17. A continuous random variable X has a uniform distribution between 5 and 15 (inclusive), then the probability that X falls between 10 and 20 is 1. . ANS:FPTS:1REF:SECTION 8. 1 18. A continuous random variable X has a uniform distribution between 5 and 25 (inclusive), then P(X = 15) = 0. 05. ANS:FPTS:1REF:SECTION 8. 1 19. We distinguish between discrete and continuous random variables by noting whether the number of possible values is countable or uncountable. ANS:TPTS:1REF:SECTION 8. 1 20. In practice, we frequently use a continuous distribution to approximate a discrete one when the number of values the variable can assume is countable but very large. ANS:TPTS:1REF:SECTION 8. 1 21. Let X represent weekly income expressed in dollars. Since there is no set upper limit, we cannot identify (and thus cannot count) all the possible values. Consequently, weekly income is regarded as a continuous random variable. ANS:TPTS:1REF:SECTION 8. 1 22. To be a legitimate probability density function, all possible values of f(x) must be non-negative. ANS:TPTS:1REF:SECTION 8. 1 23. To be a legitimate probability density function, all possible values of f(x) must lie between 0 and 1 (inclusive). ANS:FPTS:1REF:SECTION 8. 1 24. The sum of all values of f(x) over the range of [a, b] must equal one. ANS:FPTS:1REF:SECTION 8. 1 25. A probability density function shows the probability for each value of X. ANS:FPTS:1REF:SECTION 8. 1 26. If X is a continuous random variable on the interval [0, 10], then P(X 5) = P(X ? 5). ANS:TPTS:1REF:SECTION 8. 1 27. If X is a continuous random variable on the interval [0, 10], then P(X = 5) = f(5) = 1/10. ANS:FPTS:1REF:SECTION 8. 1 28. If a point y lies outside the range of the possible values of a random variable X, then f(y) must equal zero. ANS:TPTS:1REF:SECTION 8. 1 COMPLETION 29. A(n) ____________________ random variable is one that assumes an uncountable number of possible values. ANS:continuous PTS:1REF:SECTION 8. 1 30. For a continuous random variable, the probability for each individual value of X is ____________________. ANS: zero 0 PTS:1REF:SECTION 8. 1 31. Probability for continuous random variables is found by finding the ____________________ under a curve. ANS:area PTS:1REF:SECTION 8. 1 32. A(n) ____________________ random variable has a density function that looks like a rectangle and you can use areas of a rectangle to find probabilities for it. ANS:uniform PTS:1REF:SECTION 8. 1 33. Suppose X is a continuous random variable for X between a and b. Then its probability ____________________ function must non-negative for all values of X between a and b. ANS:density PTS:1REF:SECTION 8. 1 34. The total area under f(x) for a continuous random variable must equal ____________________. ANS: 1 one PTS:1REF:SECTION 8. 1 35. The probability density function of a uniform random variable on the interval [0, 5] must be ____________________ for 0 ? x ? 5. ANS: 1/5 0. 20 PTS:1REF:SECTION 8. 1 36. To find the probability for a uniform random variable you take the ____________________ times the ____________________ of its corresponding rectangle. ANS: base; height height; base length; width width; length PTS:1REF:SECTION 8. 1 37. You can use a continuous random variable to ____________________ a discrete random variable that takes on a countable, but very large, number of possible values. ANS:approximate PTS:1REF:SECTION 8. 1 SHORT ANSWER 38. A continuous random variable X has the following probability density function: f(x) = 1/4, 0 ? x ? 4 Find the following probabilities: a. P(X ? 1) b. P(X ? 2) c. P(1 ? X ? 2) d. P(X = 3) ANS: a. 0. 25 b. 0. 50 c. 0. 25 d. 0 PTS:1REF:SECTION 8. 1 Waiting Time The length of time patients must wait to see a doctor at an emergency room in a large hospital has a uniform distribution between 40 minutes and 3 hours. 39. {Waiting Time Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/140, 40 ? x ? 180 (minutes) PTS:1REF:SECTION 8. 1 40. {Waiting Time Narrative} What is the probability that a patient would have to wait between one and two hours? ANS: 0. 43 PTS:1REF:SECTION 8. 1 41. {Waiting Time Narrative} What is the probability that a patient would have to wait exactly one hour? ANS: 0 PTS:1REF:SECTION 8. 1 42. {Waiting Time Narrative} What is the probability that a patient would have to wait no more than one hour? ANS: 0. 143 PTS:1REF:SECTION 8. 1 43. The time required to complete a particular assembly operation has a uniform distribution between 25 and 50 minutes. a. What is the probability density function for this uniform distribution? b. What is the probability that the assembly operation will require more than 40 minutes to complete? c. Suppose more time was allowed to complete the operation, and the values of X were extended to the range from 25 to 60 minutes. What would f(x) be in this case? ANS: a. f(x) = 1/25, 25 ? x ? 50 b. 0. 40 c. f(x) = 1/35, 25 ? x ? 60 PTS:1REF:SECTION 8. 1 44. Suppose f(x) equals 1/50 on the interval [0, 50]. a. What is the distribution of X? b. What does the graph of f(x) look like? c. Find P(X ? 25) d. Find P(X ? 25) e. Find P(X = 25) f. Find P(0 X 3) g. Find P(? 3 X 0) h. Find P(0 X 50) ANS: a. X has a uniform distribution on the interval [0, 50]. b. f(x) forms a rectangle of height 1/50 from x = 0 to x = 50. c. 0. 50 d. 0. 50 e. 0 f. 0. 06 g. 0. 06 h. 1. 00 PTS:1REF:SECTION 8. 1 Chemistry Test The time it takes a student to finish a chemistry test has a uniform distribution between 50 and 70 minutes. 45. {Chemistry Test Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/20, 50 ? x ? 70 PTS:1REF:SECTION 8. 1 46. {Chemistry Test Narrative} Find the probability that a student will take more than 60 minutes to finish the test. ANS: 0. 50 PTS:1REF:SECTION 8. 1 47. {Chemistry Test Narrative} Find the probability that a student will take no less than 55 minutes to finish the test. ANS: 0. 75 PTS:1REF:SECTION 8. 1 48. {Chemistry Test Narrative} Find the probability that a student will take exactly one hour to finish the test. ANS: 0 PTS:1REF:SECTION 8. 1 49. {Chemistry Test Narrative} What is the median amount of time it takes a student to finish the test? ANS: 60 minutes PTS:1REF:SECTION 8. 1 50. {Chemistry Test Narrative} What is the mean amount of time it takes a student to finish the test? ANS: 60 minutes PTS:1REF:SECTION 8. 1 Elevator Waiting Time In a shopping mall the waiting time for an elevator is found to be uniformly distributed between 1 and 5 minutes. 1. {Elevator Waiting Time Narrative} What is the probability density function for this uniform distribution? ANS: f(x) = 1/4, 1 ? x ? 5 PTS:1REF:SECTION 8. 1 52. {Elevator Waiting Time Narrative} What is the probability of waiting no more than 3 minutes? ANS: 0. 50 PTS:1REF:SECTION 8. 1 53. {Elevator Waiting Time Narrative} What is the probability that the elevator arrives in the first minute and a half? ANS: 0. 125 PTS:1REF:SECTION 8. 1 54. {Elevator Waiting Time Narrative} What is the median waiting time for this elevator? ANS: 3 minutes PTS:1REF:SECTION 8. 1

Marketing Across Cultures Essay Example | Topics and Well Written Essays - 2000 words

Marketing Across Cultures - Essay Example With this manipulation of elements, a firm can achieve its objectives in the market that it targets. The elements of the marketing mix are â€Å"product, price, promotion, and place, also referred to as the four Ps† (Bennet, pp. 44, 2009). The firm does not manipulate all of these elements to a significant degree every time it is marketing across cultures. However, the selections of elements, which require manipulation, are only possible in the light of the characteristics of the culture. For this reason, examples, such as the ones stated below, can help to elaborate this fact. The management of the marketing effort is also highly affected by the differences in the cultures of the firm's different target markets. The four functions of the marketing management are analysis, planning, function, and control (Maister, pp. 133, 1997). The degree of importance which management gives to each function is dependent on the characteristics of the market that the firm targets. For example , the characteristics of a market existing in one culture may emphasize the importance of planning above all the other functions, while a market in another culture may necessitate the control function to be the one the firm invests in the most. Again, the true effect of the culture on the marketing management can only be understood through in-depth analysis and examples. According to a case study, which studied international marketing by shedding light on Ikea’s operations in Shanghai, they learned that â€Å"the company must think globally and act locally in hope of building long-term customer relationships and capturing customer value. In marketing decisions, culture does not hold an ultimate status but still calls for due attention as much as other factors, such as the marketing environment and the company’s strategic plan† (Pan, pp. iii, 2005). This is an example of such a case, were marketing across several different cultures has led to a difference in the marketing elements employed, and will thus be useful in understanding the phenomenon.  Similarly, one can deduce that the strategic management that a multinational company does is on a global scale, following the rules it abides by in any part of the world. Whereas the local marketing strategies are designed locally, depending on the target market and culture of the country of operation (Doole and Lowe, pp. 218-225, 2008). Product: One of the elements in the marketing mix, which is majorly affected by the culture to which it is being employed in, is the product itself.

A Business Research Proposal of British Airways

A Business of British Airways - Research Proposal Example The role of social media is very critical in today’s market, as business are desperate of attracting new customers and retaining the new ones as there is intensified competition in the market share of the customers (Kaplan & Haenlein 2010, 63). The airline industry is one of the most competitive markets in the service sector. Major airline operators in the industry are fighting to not only understand the needs of their customers but also in tracking their level of satisfaction with their services (Prokesch 1995, 109). As a result, the utilization of modern social technologies in reaching the customers has become a fundamental practice to enhance competitiveness among key players in the industry. A key player in the airline industry in the United Kingdom is the British Airways that owns about 47 per cent of UK airline market share (Mills 2003, 17). The purpose of the research study is focusing on the role of the social media on the British Airway Company as part of the company’s Customer Relationship Management strategy (CRM). As a result of the low differentiation of the services offered in the industry, the airline industry has become one of the markets under stiff competition in the service sector. As a result, major firms operating in the sector are adopting devise innovative strategies to improve their relationship with their customers through the Customer Relationship Management systems (Belobaba et al. 2009, par 1). The social media has developed to be one of the most effective and convenient platforms due to its popularity where customers are engaged, and communication is also enhanced. CRM has been related to understanding the needs of the customers and addressing them in a timely manner thus social media plays a key role  in enhancing effective CRM (Verhoef 2003, 33). In regards to the to the intensified competition in the