Customer ID Falsify Data 1 4.33 2 3.33 3 6.00 4 3.00 5 3.00 6 2.00 7 3.00 8 2.33 9 1.67 10 2.67 11 6.67 12 1.67 13 6.00 14 6.00 15 1.33 16 2.00 17 1.67 18 1.00 19 2.00 20 5.33 21 2.00 22 2.00 23 1.67 24 2.67 25 3.00 26 3.00 27 1.00 28 3.33 29 1.00 30 2.00 31 1.33 32 1.33 33 3.00 34 2.00 35 1.00 36 1.00 Negative Word-ofMouth 1.00 3.00 3.67 1.00 2.00 2.00 2.00 1.00 1.33 1.00 6.33 1.67 4.00 6.00 1.33 2.00 1.00 1.00 2.00 5.33 2.00 2.00 1.00 2.00 2.00 3.00 1.33 3.00 1.00 2.00 1.00 1.00 1.00 2.00 1.00 1.00 Concerned Switching about Big Data 4.00 4.00 6.00 6.00 6.75 7.00 5.00 6.00 4.50 5.00 5.25 6.00 3.50 6.00 6.00 7.00 5.25 7.00 3.50 2.00 6.00 6.00 4.00 6.00 5.00 6.00 6.75 7.00 5.50 6.00 5.00 6.00 3.25 2.00 2.00 7.00 4.00 7.00 4.50 3.00 5.00 7.00 4.25 6.00 3.75 6.00 5.00 6.00 3.75 5.00 7.00 7.00 5.25 5.00 4.00 5.00 7.00 7.00 4.75 6.00 3.25 6.00 5.25 7.00 3.50 6.00 3.50 5.00 2.00 5.00 6.25 7.00 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 1.00 6.00 2.00 4.00 1.00 5.33 1.00 6.00 3.67 2.00 3.00 2.00 7.00 3.33 3.67 4.33 5.00 3.33 7.00 5.67 2.00 2.00 5.00 4.67 2.67 1.33 1.00 5.00 4.33 5.00 6.00 4.00 4.00 1.00 5.00 3.00 5.33 7.00 1.00 1.00 2.00 3.00 1.00 2.00 1.00 5.33 1.00 2.00 3.00 2.00 4.00 4.67 5.00 4.33 3.67 2.00 5.00 3.33 7.00 2.00 4.67 4.33 3.00 1.33 1.00 4.33 3.67 2.67 2.00 4.33 4.00 1.00 5.67 1.67 2.00 3.67 2.75 6.50 3.00 3.75 4.00 5.50 6.00 6.00 3.50 2.00 4.75 5.00 6.00 4.00 4.75 4.75 5.50 4.25 7.00 6.00 4.00 4.75 5.00 4.75 5.50 5.25 4.00 5.00 4.50 4.75 5.50 4.00 4.00 7.00 6.25 3.25 5.25 4.25 2.00 7.00 6.00 3.00 2.00 4.00 7.00 7.00 2.00 6.00 6.00 5.00 7.00 7.00 5.00 5.00 6.00 7.00 7.00 6.00 5.00 5.00 6.00 5.00 7.00 7.00 7.00 5.00 6.00 7.00 5.00 6.00 4.00 7.00 6.00 4.00 2.00 7.00 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 4.67 6.33 2.00 6.00 4.33 4.00 6.00 6.33 2.67 3.67 1.00 3.67 3.33 6.00 4.00 3.33 5.33 5.00 6.33 4.00 6.00 3.00 7.00 4.33 4.00 3.33 3.33 6.00 3.67 5.00 5.67 3.33 1.33 1.33 6.00 4.00 5.33 2.00 3.33 4.33 4.33 2.00 6.00 2.00 4.00 6.00 2.67 4.00 1.33 2.00 4.67 5.00 2.00 4.67 1.00 4.33 2.67 3.00 5.00 3.33 4.67 5.00 2.00 3.00 3.00 5.67 2.00 2.00 4.67 3.67 4.33 2.00 6.00 1.00 5.00 2.00 5.75 6.25 6.25 3.50 1.50 4.75 6.25 6.50 5.00 5.75 6.25 4.25 5.00 4.75 4.25 5.25 6.00 5.00 6.50 4.00 5.00 5.50 1.00 6.00 5.00 4.50 5.50 5.50 5.75 6.00 5.00 5.00 6.50 4.75 7.00 4.50 4.25 4.00 5.00 7.00 4.00 5.00 7.00 6.00 7.00 7.00 4.00 7.00 5.00 5.00 6.00 7.00 2.00 4.00 6.00 5.00 7.00 5.00 7.00 6.00 7.00 7.00 5.00 4.00 7.00 6.00 7.00 2.00 5.00 6.00 7.00 6.00 7.00 4.00 3.00 4.00 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 2.67 3.33 3.00 7.00 4.00 3.33 2.00 6.67 5.67 2.00 3.00 3.00 6.00 5.00 4.00 3.67 4.67 5.67 3.00 4.50 2.67 1.00 5.00 4.00 5.67 1.00 2.00 4.33 3.00 6.00 2.00 4.67 3.67 4.67 4.00 3.00 6.00 4.33 5.00 1.00 3.00 5.33 2.33 3.67 2.00 5.67 5.33 1.00 3.00 4.67 5.33 5.00 3.00 4.00 6.33 4.33 6.00 4.33 4.00 4.00 6.00 2.00 4.00 1.33 5.00 5.00 3.00 2.00 2.67 3.33 3.00 4.00 4.33 4.00 4.00 4.00 6.25 4.25 6.00 5.75 4.25 6.00 5.00 1.00 6.25 4.25 4.00 5.25 6.25 5.75 5.25 3.00 2.75 2.50 6.50 3.50 6.25 5.75 1.50 3.25 5.75 2.50 4.50 5.25 4.00 4.25 6.00 3.25 6.00 6.25 6.25 5.00 5.00 5.50 4.00 5.00 6.00 7.00 5.00 5.00 5.00 7.00 5.00 3.00 5.00 7.00 6.00 6.00 3.00 5.00 5.00 7.00 7.00 3.00 7.00 7.00 2.00 6.00 3.00 1.00 4.00 4.00 6.00 5.00 7.00 4.00 6.00 4.00 6.00 5.00 4.00 6.00 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 4.33 2.00 6.00 7.00 2.33 2.00 5.00 5.00 1.67 5.00 1.00 5.33 4.33 1.67 3.67 5.67 4.00 6.00 6.33 4.00 6.33 7.00 5.33 1.00 3.00 3.00 4.33 4.33 1.00 5.00 4.00 4.67 4.33 5.00 3.33 4.00 7.00 4.00 5.00 5.67 5.00 4.67 4.00 3.00 6.00 6.00 1.00 4.33 7.00 5.00 4.67 3.33 5.00 2.00 2.00 5.33 3.00 5.33 5.33 5.00 5.00 6.33 3.00 4.00 4.33 4.67 1.00 5.00 4.00 4.33 3.00 3.00 3.33 5.33 6.33 4.00 4.00 5.50 5.25 7.00 4.75 5.75 1.00 6.50 4.50 4.75 5.50 4.25 6.00 5.50 4.00 5.50 4.00 5.75 5.50 6.00 7.00 6.00 5.25 7.00 4.25 5.50 2.00 4.00 5.00 6.00 4.00 5.00 4.75 5.75 5.00 6.00 6.00 4.00 6.00 5.00 5.00 7.00 2.00 7.00 7.00 7.00 6.00 6.00 7.00 5.00 6.00 6.00 7.00 6.00 4.00 5.00 6.00 6.00 7.00 4.00 6.00 7.00 6.00 5.00 5.00 3.00 7.00 7.00 4.00 4.00 5.00 6.00 5.00 6.00 7.00 4.00 189 190 191 192 193 194 195 196 197 3.33 2.00 6.67 3.00 5.00 4.67 1.00 6.00 6.00 3.00 2.00 4.33 2.00 5.67 3.00 1.00 4.00 3.00 4.75 3.00 4.00 6.00 6.75 5.25 4.00 6.25 5.25 6.00 4.00 7.00 6.00 7.00 4.00 5.00 7.00 6.00 Age 1 2 2 2 2 3 2 2 1 1 1 3 2 1 2 4 4 3 2 2 3 1 2 3 2 2 3 4 2 2 4 2 3 2 2 2 These data represent customer responses (1-7 Likert scale) on whether falsify their personal information, spread negative word-of-mouth, swit different firm after a privacy failure. 1. Are there mean differences in likelihood to Falsify Data, Switching, a Word-of-Mouth? 2. Do customers' concerns about big data vary across the four age age 4 2 2 2 4 3 2 1 3 1 1 4 4 3 2 2 1 2 3 1 1 3 2 2 2 2 1 2 2 4 4 4 4 2 1 1 3 2 4 2 2 4 2 4 3 3 2 2 2 2 1 4 1 4 4 3 3 2 2 4 2 2 2 2 1 2 2 2 1 3 2 2 1 2 4 4 3 1 2 1 4 4 2 2 2 2 2 4 2 4 4 2 2 3 2 2 2 4 2 1 1 2 2 1 4 2 1 1 2 2 4 3 2 3 4 1 3 1 4 4 2 2 1 2 3 2 2 1 2 1 4 3 3 2 2 2 2 4 4 4 1 2 4 2 3 2 1 2 3 3 2 2 2 4 2 2 2 2 1 2 2 kert scale) on whether they would e word-of-mouth, switch to a sify Data, Switching, and Negative cross the four age age groups? Employee ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Identify with Company Company 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 4 4 4 4 7 3 5 5 7 2 7 2 4 6 6 3 6 6 7 2 6 3 5 6 6 6 1 6 5 5 2 7 Percent Likelihood of Leaving Company 30 5 29 20 51 5 53 51 5 5 40 71 100 49 79 9 52 92 87 1 0 9 33 40 60 10 10 50 9 10 60 50 8 Loyalty PreProgram 6 5 6 6 4 1 3 6 5 6 6 6 4 3 6 1 2 6 7 2 6 4 3 4 2 2 6 2 6 2 7 2 5 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 2 3 3 3 2 1 3 3 5 1 5 6 2 3 2 1 6 4 5 5 5 5 1 5 6 1 3 6 5 5 1 5 6 6 7 6 5 40 50 27 50 45 40 75 45 51 81 100 30 10 35 50 65 49 40 49 62 34 43 56 71 23 54 60 30 81 81 67 90 0 53 29 80 40 19 3 6 4 3 5 5 5 4 3 3 6 1 6 3 5 4 3 2 3 7 3 6 6 4 5 4 6 7 2 6 6 4 3 6 4 5 2 5 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 5 7 3 6 3 2 2 7 1 5 6 2 5 7 2 6 5 2 4 1 4 3 3 5 53 12 50 100 56 99 47 63 94 33 70 96 6 100 79 40 12 45 90 81 50 50 50 100 31 10 20 45 4 2 4 5 6 7 2 6 3 4 6 7 5 4 6 5 5 7 7 7 4 5 5 2 1 4 4 6 Loyalty PostProgram Gender (1 = female) 6 5 7 5 4 7 2 6 6 7 5 6 5 4 6 6 5 6 7 6 6 6 5 5 6 5 6 5 7 5 6 5 6 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2 1 2 2 1 1 1 1 2 2 1 These data come from an HR database with employe different companies (denoted 1, 2, and 3 in the Com asked how much they identify with the company (1-7 how likely it is (in %) that they leave the company wi these three companies piloted an employee loyalty p scores about employees loyalty to the company both rollout. Please answer the following questions. 3. Did the loyalty program work differently across th was tested? Please test the loyalty scores pre-and po program for the three companies. 4. Does percent likelihood of leaving the company d 5. Does company identification differ across the thre 5 6 3 6 5 6 2 4 4 5 5 6 6 2 4 5 5 7 3 5 4 6 4 1 6 6 5 5 6 5 5 4 6 6 5 4 3 6 2 1 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 1 1 2 1 2 2 2 2 1 2 1 1 1 4 5 5 5 6 7 3 6 3 4 4 7 5 4 6 6 6 7 7 6 6 5 3 3 1 6 6 7 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 2 2 1 2 1 1 HR database with employee responses across three ted 1, 2, and 3 in the Company column). Employees were ntify with the company (1-7 Likert scale), and to estimate hey leave the company within the next year. Finally, oted an employee loyalty program. There are Likert oyalty to the company both before and after the program following questions. m work differently across the three companies where it e loyalty scores pre-and post- implementation of the mpanies. d of leaving the company differ by gender? ation differ across the three companies? Copywriter Authored Personalized Attorney Authored 5.00 4.25 5.00 2.00 6.00 5.25 5.50 6.00 6.75 6.25 4.50 6.75 6.50 4.75 3.25 3.75 6.50 5.00 6.25 6.50 6.00 2.25 5.00 6.00 6.00 5.00 6.00 5.00 6.25 5.00 7.00 6.00 4.75 5.50 6.75 6.00 3.00 3.00 6.00 3.00 4.00 1.00 2.75 4.75 3.75 5.50 2.50 6.25 3.00 6.00 3.00 4.50 5.00 2.00 3.50 3.00 6.00 5.50 5.00 5.00 2.00 3.75 4.50 5.00 1.00 1.75 5.00 5.00 2.50 3.00 4.75 3.25 6) Big Brother, Inc. wants to know how different versions: one that was written and one that was written by a professio have the ability to personalize (or not p customer. Each customer only reads one policy, th (mean scores reported). Higher scores i scores by both authorship and whether find? Graph any significant interactions. Non-Personalized 5.00 5.00 6.50 1.50 6.00 4.00 7.00 5.00 6.25 6.00 5.50 5.00 4.50 3.50 3.00 1.00 2.50 4.00 5.00 4.00 3.00 3.00 5.00 1.00 6.00 1.00 5.50 3.25 6.50 4.00 2.75 3.75 2.50 4.25 3.00 4.00 6.00 6.25 3.50 1.50 4.25 2.00 5.00 2.00 4.00 4.00 4.25 3.25 5.00 3.75 5.25 4.50 3.00 3.00 2.50 3.75 4.00 4.00 2.25 3.50 2.00 6.00 5.75 2.00 1.25 2.75 2.25 2.25 2.50 5.50 3.25 1.00 3.50 2.50 3.50 6.00 5.75 4.50 1.00 4.00 3.00 3.00 4.00 1.50 4.50 3.25 5.00 4.50 4.50 6.25 4.00 3.00 2.00 2.25 5.50 6.00 5.00 5.00 5.25 3.50 1.75 1.00 1.75 2.00 1.00 2.00 2.25 5.00 1.75 2.00 5.50 6.25 2.00 4.00 4.75 5.50 3.75 3.50 3.25 2.13 her, Inc. wants to know how its customers rate its privacy policy. They have two ersions: one that was written by the company attorneys (i.e., attorney authored) at was written by a professional copywriter (i.e., copywriter authored). They also bility to personalize (or not personalize) the policy to the unique details of the mer only reads one policy, then scores it on a series of Likert scale questions es reported). Higher scores indicate a more favorable rating. Evaluate the rating oth authorship and whether the message is personalized or not. What do you h any significant interactions