Deliverable 2 CCSU scholars investigated whether the attributes of customer service quality and mobile app design quality predict mobile shoppers’ Trust, Repurchase Intention, or Recommendation...

Deliverable 2
CCSU scholars investigated whether the attributes of customer service quality and mobile app design
quality predict mobile shoppers’ Trust, Repurchase Intention, or Recommendation Intention. From 286
mobile consumers they recorded demographic characteristics that were described in Deliverable 1.
They also measured the mobile shopping quality dimensions and customer attitudes as reported by the
mobile shoppers:
(1) Reliability: Items Q18, Q19, Q20
(2) Responsiveness: Items Q21, Q22, Q23
(3) Assurance: Items Q24, Q25, Q26
(4) Personalization: Items Q27, Q28, Q29
(5) Content quality: Items Q30, Q31, Q32
(6) Ease of use: Items Q33, Q34, Q35
(7) Aesthetics: Items Q36, Q37, Q38
(8) Risks: Items Q39, Q40, Q41, Q42, Q43, Q44
(9) Trust: Items Q45, Q46, Q47, Q48, Q49
(10) Repurchase intention: Items Q62, Q63, Q64, Q65
(11) Recommendation intention: Items Q66, Q67, Q68
The items were measured by the 5-point Likert Scale: 1=strongly disagree, 3=neutral, 5=strongly agree.
Past research indicated that the quality dimensions XXXXXXXXXXwere good predictors for mobile shoppers’
attitudes XXXXXXXXXX).
Analyze the data with a multiple regression model. You are needed to:
1. Pick one dependent variable from XXXXXXXXXXi.e., Trust, Repurchase intention, Recommendation
intention).
2. Pick four independent variables from XXXXXXXXXXi.e., Reliability, Responsiveness, Assurance,
Personalization, Content quality, Ease of use, Aesthetics, Risks).
3. Form four hypotheses between the independent variables and the dependent variable.
4. Test each hypothesis using simple linear regression. Is there a significant relationship between
the independent variable and the dependent variable for each hypothesis? Explain.
5. Conduct a hierarchical linear regression analysis. Copy and paste your results below. For
example, if you choose Trust (9) as the dependent variable and Reliability (1), Responsiveness
(2), Assurance (3), and Personalization (4) as the four predictors. You can conduct the
hierarchical linear model in three steps:
I. Enter Trust (9) and Reliability (1);
II. Add variable Responsiveness (2);
III. Add variable Assurance (3);
IV. Add variable Personalization (4).
6. Which of the four models (I, II, III, and IV) best explains the dependent variable? Explain.

Uliana Batih
Central Connecticut State University
BUS-538-81
Summer 2022
Prof. Richard Zhang
PROJECT FILE
    General/Demographic characteristics
    Overall group
    Male group
    Female group
    Other group
     
    (n=286)
    (n=140)
    (n=134)
    (n=12)
     
    n
    percentage
    n
    percentage
    n
    percentage
    n
    percentage
    Age (years)
     
     
     
     
     
     
     
     
    20-24
    33
    11.5
    12
    8.6
    20
    14.9
    1
    8.3
    25-34
    112
    39.2
    64
    45.7
    40
    29.9
    8
    66.7
    35-44
    78
    27.3
    42
    30.0
    34
    25.4
    2
    16.7
    45-54
    37
    12.9
    14
    10.0
    23
    17.2
    0
    0
    55-64
    23
    8.0
    5
    3.6
    17
    12.7
    1
    8.3
    65 or ove
    3
    1.0
    3
    2.1
    0
    0
    0
    0
    Educational attainment
    
    
    
    
    
    
    
    
    Less than high school
    1
    0.3
    0
    0.0
    1
    0.7
    0
    0.0
    High school
    57
    19.9
    31
    22.1
    23
    17.2
    3
    25.0
    Some college
    33
    11.5
    19
    13.6
    14
    10.4
    0
    0.0
    Trade/technical school
    157
    54.9
    77
    55.0
    71
    53.0
    9
    75.0
    College graduate
    10
    3.5
    3
    2.1
    7
    5.2
    0
    0.0
    Graduate school
    28
    9.8
    10
    7.1
    18
    13.4
    0
    0.0
    Number of times per week using mobile apps for shopping or information search
    
    
    
    
    
    
    
    
    1-2 times
    27
    9.4
    13
    9.3
    13
    9.7
    1
    8.3
    3-4 times
    24
    8.4
    9
    6.4
    15
    11.2
    0
    0.0
    5-6 times
    88
    30.8
    43
    30.7
    37
    27.6
    8
    66.7
    7-8 times
    30
    10.5
    20
    14.3
    10
    7.5
    0
    0.0
    9-10 times
    97
    33.9
    49
    35.0
    45
    33.6
    3
    25.0
    Over 12 times
    20
    7.0
    6
    4.3
    14
    10.4
    0
    0.0
    Number of hours per week using mobile apps for shopping or information search
    
    
    
    
    
    
    
    
    1-3 hours
    44
    15.4
    24
    17.1
    19
    14.2
    1
    8.3
    4-6 hours
    43
    15.0
    20
    14.3
    20
    14.9
    3
    25.0
    7-9 hours
    32
    11.2
    15
    10.7
    17
    12.7
    0
    0.0
    10-12 hours
    44
    15.4
    22
    15.7
    20
    14.9
    2
    16.7
    13-15 hours
    12
    4.2
    8
    5.7
    4
    3.0
    0
    0.0
    16-18 hours
    30
    10.5
    14
    10.0
    12
    9.0
    4
    33.3
    Over 18 hours
    81
    28.3
    37
    26.4
    42
    31.3
    2
    16.7
    Number of mobile purchases in the past 12 months
    
    
    
    
    
    
    
    
    1-2 times
    5
    1.7
    4
    2.9
    1
    0.7
    0
    0.0
    3-4 times
    201
    70.3
    98
    70.0
    94
    70.1
    9
    75.0
    5-6 times
    6
    2.1
    3
    2.1
    3
    2.2
    0
    0.0
    7-8 times
    55
    19.2
    26
    18.6
    27
    20.1
    2
    16.7
    9-10 times
    5
    1.7
    4
    2.9
    1
    0.7
    0
    0.0
    11-12 times
    10
    3.5
    5
    3.6
    5
    3.7
    0
    0.0
    13-14 times
    4
    1.4
    0
    0.0
    3
    2.2
    1
    8.3
    Mobile shopping experience/years
    
    
    
    
    
    
    
    
    Less than 1 yea
    23
    8
    14
    10.0
    8
    6.0
    1
    8.3
    1-2 years
    53
    18.5
    30
    21.4
    21
    15.7
    2
    16.7
    3-4 years
    76
    26.6
    27
    19.3
    45
    33.6
    4
    33.3
    5-6 years
    16
    5.6
    9
    6.4
    7
    5.2
    0
    0.0
    7-8 years
    94
    32.9
    46
    32.9
    44
    32.8
    4
    33.3
    Over 8 years
    24
    8.4
    14
    10.0
    9
    6.7
    1
    8.3
    Most frequently purchased items
    
    
    
    
    
    
    
    
    Clothing/Shoes
    210
    73.4
    94
    67.1
    106
    79.1
    10
    83.3
    Bags/Accessories
    79
    27.6
    24
    17.1
    50
    37.3
    5
    41.7
    Appliance/Furniture
    38
    13.3
    17
    12.1
    20
    14.9
    1
    8.3
     Electronics/Computers
    196
    68.5
    111
    79.3
    78
    58.2
    7
    58.3
    Food/Groceries
    142
    49.7
    58
    41.4
    80
    59.7
    4
    33.3
    Travel/Hotel/Ticket
    107
    37.4
    53
    37.9
    52
    38.8
    2
    16.7
    Books/Movies/Music
    188
    65.7
    95
    67.9
    85
    63.4
    8
    66.7
    Cosmetics/Body Care/Pharmacy
    112
    39.2
    41
    29.3
    66
    49.3
    5
    41.7
    Others
    37
    12.9
    15
    10.7
    20
    14.9
    2
    16.7
According to the table, majority of the total respondents as well as males and females belongs to the age group 25-34 and very less respondents are from elderly age group.
More than half (about 56 percent) of them are qualified with trade or technical school in each category where males pursued trade or technical school are 55 percent, female as 53 percent and others as 75 percent.
The table shows that both males and females spend the most 9-10 number of times per week using mobile apps for shopping or information search, but the respondents belong to others gender used 5-6 times per week for this.
More than 18 hours per week spend by males (about 26 percent) and females (about 31 percent) for shopping or information search using mobile apps whereas the hours spend are 16-18 for others gender.
The number of mobile purchases is 3-4 times in past 12 month in each subgroup categories but the experience of mobile shopping in terms of years is more in males for 7-8 years whereas only 3-4 years for females.
The most frequently purchased items for males are electronics/ computers (79.3 percent) followed by books/music/movies (67.9 percent) whereas females mostly bought clothing/shoes (79.1 percent). In the same manner, respondents who are of other genders mostly purchased clothing/shoes followed by books/music/movies and electronics/ computers.