Mobile Phone Consumption Patterns Among Students and Professionals in Qatar


Ameena Mohammed Al-Emadi
MBA Student, College of Business Administration,
University of Qatar, Doha
&
Muna Abdullah Al-Ansari
MBA Student, College of Business Administration,
University of Qatar, Doha

December 23, 2012

Mobile Services

Abstract

This research paper explores the use of mobile phones among students and professionals in the State of Qatar. This paper will focus on the differences of mobile consumption among students and professionals and figure out if there is an addictive pattern on the usage of mobile phones among them. In order to get the desired results, electronic questionnaire was used for data collection. The population consisted of students and professionals, males and females. The questionnaire was randomly distributed.  The electronic survey was open for 7 days and it was distributed through different electronic social media such as: email, Twitter, “QatarShares” Forum, BlackBerry and WhatsApp applications. With the different means of technologies used for collecting the result of the survey, total number of responses was 213 responses. To serve the objective of this research paper, responses from outside the state of Qatar or from someone who is neither a professional nor a student were excluded. The study focuses on identifying the differences among students and professionals in the following areas: Number and brand of mobiles owned, Subscription Model, Frequency of Charging/Paying the bills (Post-paid/Pre-paid), Service Provider, Level of Usage of each of the following services (Calls, SMS, Social Media tools, Video and Photo Capturing, Application Stores, Games, emails, Documents Reader).

Introduction:

These days technology becomes part of our lives. With emerge of technology, new innovations helped our lives to become easier and brought us together and closer. Although that digital cellular phone call was invented in early 1990’s, it has been evolving rapidly since then and it has changed people’s life forever. Due to the availability of digital cellular phone everywhere, many people have opted to cancel landline and replace them with cell phones instead; Moreover people of all ages are using cell phones these days, which contributed to an increase in the subscription worldwide. According to ITU World Telecommunication/ICT, there are almost 6 billion mobile-cellular subscriptions in 2011 The use of mobile phones shifted from just a normal phone call, to a smart device with different multi options, where you can send text message, capture a photo or a video, surf the internet, access your emails, make video calls, access your social media accounts, play games, and much more. The evaluation that occurs in mobile phone’s manufactures, telecommunication services and facilities played a major role in the distribution of cell phones worldwide.. According to ITU, in 2011, 142 million mobile-cellular subscriptions were added in India. These subscriptions are twice as many as in the whole Africa, more than what is in the Arab countries, CIS and Europe together. ictQATAR institute in Qatar highlighted in its annual report “Qatar’s ICT Landscape 2011”  the  high penetration of mobile phones in each household. It stated that in Qatar each household owns 3.9 mobile phones. Also it mentioned that mobile phone penetration within Qatar’s households increased in 2010, with 99 percent of Qatar’s households now equipped with at least one mobile phone, up from 98 percent in 2008, the case looks the same at the individual level as it reached 99% in 2010, with overall penetration increment of 6% points from 2008. The penetration rate range is between 97%-99% in each age group range from less than 20 years up to more than 50 years old in the state of Qatar as per the household and individual Survey (Qatar, 2010, n=1400), which is a very high number for analysis. This research paper will help to investigate if there are any differences in the mobile phone consumption between the two most effective groups in the community, students and professionals. It will also provide answers to some unexplored questions.

Motivation and problem statement

As mobile phone penetration in Qatar is increasing at individuals of different levels of occupation and education, by all age groups and genders, there is no detailed research at State of Qatar level that explores this field in more details. Despite the annual market research conducted by Supreme Counsel of Information and Communications Technologies (ictQATAR). Taking into consideration that Qatar government opened the telecommunication market for a second operator, and maybe a third operator in the near future, which participated in the increment of mobile penetration in the State of Qatar. Professionals claim that they are working beyond their working hours, due to the availability of their mobile phone to reply to emails and to receive late phone calls. Teachers and parents, on the other hand, believe that mobiles are destructive tools that shouldn’t be provided to students, especially with the integration between those smart devices and social networks, according to a new infographic that was recently published by social media strategist Khaled ElAhmad, it stated that Qatar was the third-highest Twitter penetration rate in the Arab World after Kuwait and Bahrain. This research tries to analyze if there is a significant differences between the consumption of mobile phones among students of high schools/universities and professionals in the State of Qatar, therefore we decided to build a survey that targets only these two categories, and collect data that help in analyzing users consumption patterns in terms of number and brand of mobiles owned, subscription model, the frequency of charging/paying bills, and today’s main mobile services or functions of mobiles available in the Qatari market. Also we will try to answer a question, whether occupation is the main factor affecting the usage of these categories or there are other unknown factors.

Research methodology:

Survey Design and Data Collection:

The survey designed based on benchmarking with other international researches concerning the same topic by analyzing the Qatari mobile service market, in terms of service providers, subscription models, and supported services by the local operators; the survey was available in two languages (Arabic and English) to ensure highest number of responses. The researchers attempted to keep the survey short and focused to avoid filling it up with dummy data. The survey was published online using an online survey website called “http://kwiksurveys.com“, which provides the functionality of exporting to excel in both forms numeric and full text. Also the tool supports Arabic language and moreover it’s fully integrated with social networks such as Twitter, and Facebook. The survey was tested by researchers to ensure that the required data are captured correctly. Later it was published using Twitter, email, “Qatarshares” Forum, and broadcast through BlackBerry and WhatsApp. The survey was open for 7 days. Total number of responses was 213. 2.82% of the responses were excluded from the collected sample, since it was presenting group of respondents who don’t live in Qatar, Also6.57% of the collected data were removed since it wasn’t presenting the two categories understudy. The final sample consisted of the following categories:

Category

N

Proportion

Students

54

27.98%

Professionals

139

72.02%

Total

193

100.00%

The collected data then was exported in to a Numeric format in Excel, where value mapping was done and required aggregations was performed. Data also used to create required charts, then it was uploaded into IBM SPSS Statistics software version 19, where the following analyzing techniques were applied:

  1. Compare Independent Samples T-Test: This test was used in order to identify if there is any differences in using each of the specified services and other variables such as service providers, number of mobiles owned, frequency of charging/paying the bills, and subscription model among the two selected samples of students and professionals.
  1. Multinomial Logistic Regression Analysis: this analysis helped the researchers to analyze the following:
    1. Level of mobile consumption of each service under study, scaled from 1-6 (Poorly-Rarely, Medium, Heavily, Addictive, and N/A).
    2. Determine the main factors affecting the usage; is it occupation or if there are other factors such as Gender, Age, Service Provider, Number of Mobiles Owned, Subscription Model.
    3. Determine most mobile services that is in addictive usage level in Qatar.

Preliminary studies and exploratory analysis:

Is there any difference in mobile consumption between Students and Professionals?

Table 1 below shows the demographic factor of the responds in current study. The highest numbers of respondents was 36.27%, which belongs to the age group of 27-36 years.  57.51%, which belongs   to the male gender. However, 42.49% belongs to the female gender. 27.98% respondents were from students while 72.02% respondents were from professionals.

Category Value N Percentage
Age 17-2122-2627-3637 and above 37517035 19.1726.4236.2718.14
Gender MaleFemale 11182 57.5142.49
Occupation StudentsProfessionals 54139 27.9872.02

Table1: Demographic information

Table 2 shows the differences of mobile phone usage between students and professionals. Independents sample T test is used to find out the differences between the two groups. An equal variance t test dose not reveal a statically reliable difference for number of mobiles owned (Samsung, i-Phone, BlackBerry, Nokia, LG, Motorola, Siemens, Sony Ericsson, Other Mobiles) with students (M=1.43, std. dev.= 0.602) and professional (M=1.56, std. dev.=0.661), df(191)=-1.308 , p=0.193  tailed; which suggests that there is not a significant difference of use of different Number of Mobiles Owned between students and professionals . An equal variance t test reveal a statistically reliable difference for subscription model (post paid/ pre-paid) with students (M= 1.31, std. dev.= 0.469) and professional (M= 1.69, std. dev.= 0.464), df(191)= -5.038 , p=.000) tailed; which suggests that there is a significant difference for subscription Model (post-paid/pre-paid) between students and professionals. Similarly, an equal variance t test reveals a statistically reliable difference for frequent of charging or paying (daily, weekly monthly, every 2 months) with students (M=2.74, std. dev.=0.678) and professionals (M= 2.96, std.dev.=0.588), df(191)=-2.193, p=0.029 tailed; which suggests that there is a significant difference for frequent of charging or paying (daily, weekly monthly, every 2 months)  between students and professionals. An equal variance t test dose not reveal a statically reliable difference for Service Provider (Q-tel, Vodafone) with students (M= 1.67, std. dev.= 0.869) and professional (M= 1.83, std. dev.= 0.960), df(191)= -1.119 , p= 0.265  tailed; which suggests that there is not any significant difference of use of service provider between students and professionals.  Also, An equal variance t test dose not reveal a statically reliable difference for receive calls with students (M= 3.17, std. dev.=0.885) and professional (M= 3.42, std. dev.= 0.816), df(191)= -1.871, p= 0.063  tailed; which suggests that there is not any significant difference of use of receive calls between students and professionals. An equal variance t test dose not reveal a statically reliable difference for make calls with students (M= 3.30, std. dev.=0.903) and professional (M= 3.50, std. dev.= 0.871), df(191)= -1,469, p= 0.144 tailed; which suggests that there is not any significant difference of use of make calls between students and professionals. In addition, An equal variance t test dose not reveal a statically reliable difference for send SMS with students (M= 2.46, std. dev.=0.905) and professional (M= 2.64, std. dev.= 1.00), df(191)= -1.135, p=0.258 tailed; which suggests that there is not any significant difference of use of Send SMS between students and professionals. Moreover, An equal variance t test dose not reveal a statically reliable difference for receive SMS with students (M= 2.93, std. dev.=0.866) and professional (M= 2.94, std. dev.= 0.938), df(191)= -0.112, p=0.911 tailed; which suggests that there is not any significant difference of use of receive SMS between students and professionals. But, An equal variance t test reveal a statistically reliable difference for Camera with students (M= 3.93, std. dev.= 1.113) and professional (M=3.22, std. dev.= 1.196), df(191)=3.772 , p=.000 tailed; which suggests that there is a significant difference for Occupation and Camera between students and professionals. An equal variance t test dose not reveal a statically reliable difference for Video with students (M=2.67 std. dev.=0.911) and professional (M= 2.45, std. dev.= 1.072), df(191)= 1.293, p=0.198 tailed; which suggests that there is not any significant difference of use of Video between students and professionals. Also, An equal variance t test dose not reveal a statically reliable difference for and Bluetooth with students (M= 1.89, std. dev.=1.003) and professional (M= 1.86, std. dev.=1.146), df(191)= 0.184, p=0.854 tailed; which suggests that there is not any significant difference of use of Bluetooth between students and professionals. However, An equal variance t test reveal a statistically reliable difference for Twitter with students (M= 3.80, std. dev.= 1.630) and professional (M=2.76, std. dev.= 1.654), df(191)=3.940, p=.000) tailed; which suggests that there is a significant difference for Twitter between students and professionals. Also, An equal variance t test reveal a statistically reliable difference for Instagram with students (M= 3.30, std. dev.= 1.880) and professional (M=2.71, std. dev.= 1.807), df(191)=1.993, p=.048 tailed; which suggests that there is a significant difference for Instagram between students and professionals. Moreover, An equal variance t test reveal a statistically reliable difference for WhtsApp with students (M= 4.24, std. dev.= 1.063) and professional (M=3.76, std. dev.= 1.462), df(191)=2.187, p=0.030 tailed; which suggests that there is a significant difference for WhatsApp between students and professionals. An equal variance t test reveal a statistically reliable difference for YouTube with students (M= 3.52, std. dev.= 1.255) and professional (M=3.02, std. dev.= 1.294), df(191)=2.415, p=.017) tailed; which suggests that there is a significant difference for YouTube between students and professionals. In addition, An equal variance t test reveal a statistically reliable difference for Games with students (M= 2.83, std. dev.= 1.514) and professional (M=2.24, std. dev.= 1.333), df(191)=2.682, p= 0.008 tailed; which suggests that there is a significant difference for Games between students and professionals. An equal variance t test reveal a statistically reliable difference for Calendar/Organizer with students (M= 3.43, std. dev.= 1.126) and professional (M=2.83, std. dev.= 1.328), df(191)=2.893, p=.004) tailed; which suggests that there is a significant difference for calendar/ organizer between students and professionals. An equal variance t test dose not reveal a statically reliable difference for Email Application with students (M= 3.65, std. dev.=1.456) and professional (M= 3.37, std. dev.=1.431), df(191)= 1.189, p=0.236 tailed; which suggests that there is not any significant difference of use of Email Application between students and professionals. Also, An equal variance t test dose not reveal a statically reliable difference for Google maps with students (M= 2.04, std. dev.=0.971) and professional (M= 2.02, std. dev.=1.113), df(191)= 0.090, p=0.929 tailed; which suggests that there is not any significant difference of use of Google map between students and professionals. An equal variance t test dose not reveal a statically reliable difference for Application Store with students (M= 3.24, std. dev.=1.243) and professional (M= 2.85, std. dev.=1.388), df(191)= 1.811, p=0.072 tailed; which suggests that there is not any significant difference of use of Application Store between students and professionals. Moreover, An equal variance t test dose not reveal a statically reliable difference for Document Reader (PDF, Word) with students (M= 2.59, std. dev.=1.296) and professional (M= 2.42, std. dev.=1.335), df(191)= 0.826, p=0.410 tailed; which suggests that there is not any significant difference of use of Document Reader (PDF, Word) between students and professionals.

Group Statistics

t-test

Variable

Occupation

Mean

Std. Dev.

t

df

Sig (2-tailed

Number of Mobiles Owned Student 1.43 0.602 -1.308 191 0.193
Professional 1.56 0.661
Frequent of Charging or Paying Student 2.74 0.678 -2.193 191 0.029
Professional 2.96 0.588
Subscription Model Student 1.31 0.469 -5.038 191 0.000
Professional 1.69 0.464
Service Provider Student 1.67 0.869 -1.119 191 0.265
Professional 1.83 0.960
Receive Calls Student 3.17 0.885 -1.871 191 0.063
Professional 3.42 0.816
Make Calls Student 3.30 0.903 -1.469 191 0.144
Professional 3.50 0.871
Send SMS Student 2.46 0.905 -1.135 191 0.258
Professional 2.64 1.000
Receive SMS Student 2.93 0.866 -0.112 191 0.911
Professional 2.94 0.938
Camera Student 3.93 1.113 3.772 191 0.000
Professional 3.22 1.196
Video Student 2.67 0.911 1.293 191 0.198
Professional 2.45 1.072
Bluetooth Student 1.89 1.003 0.184 191 0.854
Professional 1.86 1.146
Twitter Student 3.80 1.630 3.940 191 0.000
Professional 2.76 1.654
Instagram Student 3.30 1.880 1.993 191 0.048
Professional 2.71 1.807
WhatsApp Student 4.24 1.063 2.187 191 0.030
Professional 3.76 1.462
You Tube Student 3.52 1.255 2.415 191 0.017
Professional 3.02 1.294
Email Applications Student 3.65 1.456 1.189 191 0.236
Professional 3.37 1.431
Games Student 2.83 1.514 2.682 191 0.008
Professional 2.24 1.333
Calendar/ Organizer Student 3.43 1.126 2.893 191 0.004
Professional 2.83 1.328
Google maps Student 2.04 0.971 0.090 191 0.929
Professional 2.02 1.113
Applications Store Student 3.24 1.243 1.811 191 0.072
Professional 2.85 1.388
Document Reader (PDF, WORD) Student 2.59 1.296 0.826 191 0.410
Professional 2.42 1.335

Table2: Compare Independent Samples T-Test Result

Level of mobile consumption in State of Qatar

Mobile Service

Level of Usage

N

Marginal Percentage

Receive Calls Poorly

1

0.50%

Rarely

17

8.80%

Medium

114

59.10%

Heavily

37

19.20%

Addictive

23

11.90%

N/A

1

0.50%

Make Calls Poorly

1

0.50%

Rarely

14

7.30%

Medium

109

56.50%

Heavily

37

19.20%

Addictive

31

16.10%

N/A

1

0.50%

Send SMS Poorly

13

6.70%

Rarely

95

49.20%

Medium

55

28.50%

Heavily

19

9.80%

Addictive

10

5.20%

N/A

1

0.50%

Receive SMS Poorly

4

2.10%

Rarely

62

32.10%

Medium

82

42.50%

Heavily

32

16.60%

Addictive

13

6.70%

N/A

0

0.00%

Camera Poorly

12

6.20%

Rarely

30

15.50%

Medium

69

35.80%

Heavily

30

15.50%

Addictive

52

26.90%

N/A

0

0.00%

Video Poorly

24

12.40%

Rarely

87

45.10%

Medium

54

28.00%

Heavily

15

7.80%

Addictive

13

6.70%

N/A

0

0.00%

Bluetooth Poorly

88

45.60%

Rarely

72

37.30%

Medium

16

8.30%

Heavily

8

4.10%

Addictive

6

3.10%

N/A

3

1.60%

Twitter Poorly

59

30.60%

Rarely

23

11.90%

Medium

29

15.00%

Heavily

22

11.40%

Addictive

52

26.90%

N/A

8

4.10%

Instagram Poorly

81

42.00%

Rarely

14

7.30%

Medium

18

9.30%

Heavily

18

9.30%

Addictive

52

26.90%

N/A

10

5.20%

WhatsApp Poorly

19

9.80%

Rarely

14

7.30%

Medium

31

16.10%

Heavily

39

20.20%

Addictive

84

43.50%

N/A

6

3.10%

You Tube Poorly

26

13.50%

Rarely

28

14.50%

Medium

66

34.20%

Heavily

40

20.70%

Addictive

28

14.50%

N/A

5

2.60%

Email Applications Poorly

22

11.40%

Rarely

35

18.10%

Medium

40

20.70%

Heavily

30

15.50%

Addictive

62

32.10%

N/A

4

2.10%

Games Poorly

61

31.60%

Rarely

61

31.60%

Medium

33

17.10%

Heavily

16

8.30%

Addictive

14

7.30%

N/A

8

4.10%

Calendar / Organizer Poorly

29

15.00%

Rarely

40

20.70%

Medium

57

29.50%

Heavily

40

20.70%

Addictive

23

11.90%

N/A

4

2.10%

Google maps Poorly

70

36.30%

Rarely

74

38.30%

Medium

33

17.10%

Heavily

8

4.10%

Addictive

6

3.10%

N/A

2

1.00%

Applications Store Poorly

38

19.70%

Rarely

30

15.50%

Medium

58

30.10%

Heavily

42

21.80%

Addictive

19

9.80%

N/A

6

3.10%

Document Reader (PDF, WORD) Poorly

58

30.10%

Rarely

49

25.40%

Medium

44

22.80%

Heavily

26

13.50%

Addictive

12

6.20%

N/A

4

2.10%

Table3: Mobile services usage percentage, Scale 1-6 (Poor-Addictive & N/A)

The table above illustrates the level of consumption for each of the 17 identified mobile service under study, It’s observed that overall traditional mobile usage such as Making and Receiving Calls is at medium level. Whereas sending SMS is declining as 49% of the respondents said that they rarely use this service, also around 29% of them said that their usage is considered to be medium, this could be due to addictive usage of alternative text messaging mobile applications such as “WhatsApp” which scored 43.5% a, there is also addictiveness among Students and Professionals in Qatar in using  “Email applications”,  “Twitter”, as the usage is reached 32%, and 26.90% respectively. Although that 30.60% of respondents said that they are poorly using “Twitter” which evident the significance differences between students and professionals as explained in section 4.2.1. Below charts illustrate level of usage for both “email applications” and “Twitter”, for more detailed graphs, refer to Appendix B section. The usage range between Poor–Rare for the following features: “Google Maps”, “Documents Reader”, and “Games” as the percentages are 36.30%-38.30%, 30.10%-25.40%, 31.60%-31.60% for each service respectively. Whats App and Twitter Consumption Level There is also a significant decline in using Bluetooth, as illustrated in below chart. The usage level is at poor and rare with a 45.60% and 37.30%. Bluetooth Consumption Level Both “YouTube” and “Application Store” usage range are between Medium – Heavy by 34.20%-20.70% for “You Tube” and by 30.10%-21.80% for “Application Store”. “Calendars/Organizers” highest usage level is medium with 29.50%, “Camera” on the other hand, considered being of Medium to Addictive use as it reached 35.80% and 26.90% respectively and this also explain the reason of significance differences shown in the results of section 4.2.1, whereas “Video” ranged between rare to medium by 45.10% and 28.00%. Finally “Instagram” highest usage level is Poor and Addictive by 42.0% and 26.9%

Does Occupation is The Main Factor Behind Mobile Consumption Among Students and Professionals in the State of Qatar?

In the following section we will determine if occupation is the main factor affecting the consumption of mobile phones in the State of Qatar or if there are other unkown factors. Below are the 17 services that we have done a Multinomial Regression analysis on it.

Mobile Services

1.Document Reader2.Application Store3.Google Maps

4.Email Applications

5.YouTube6.WhatsApp

7.Instagram8.Twitter9.Bluetooth

10.Video

11.Camera

12.Receive SMS

13.Send SMS14.Make SMS15.Receive Calls

16.Calendar/Organizer

17.Games

Table4: Mobile services under study

Below are list of variables that were analyzed, in order to determine their effect on the usage of above mobile services, where the significant level of 5% is constant for all the performed analysis;

Variable Values
Occupation StudentProfessionals
Gender MaleFemale
Service_Provider QTelVodafoneQtel & Vodafone
Number_of_Mobiles 1 – 4
Subscription_Model Pre-PaidPost-Paid

Table5: Variables / Factors tested

Likelihood Ratio Tests

 

Model Fitting Criteria   Likelihood Ratio Tests  

#

Service

Effect

-2 Log Likelihood of Reduced Model

Chi-Square

df

Sig.

1

Document Reader

Intercept 336.809a

0

0

.
Occupation

349.977

13.168

5

0.022

Gender

344.493

7.684

5

0.175

Age

362.378

25.568

15

0.043

Service_Provider

350.206

13.397

10

0.202

Number_of_Mobiles

350.669

13.86

15

0.536

Subscription_Model

354.364

17.555

5

0.004

2

Application Store

Intercept 368.897a

0

0

.
Occupation 380.090b

11.193

5

0.048

Gender 370.988b

2.091

5

0.836

Age

391.575

22.678

15

0.091

Service_Provider 378.920b

10.023

10

0.438

Number_of_Mobiles

386.891

17.994

15

0.263

Subscription_Model 378.863b

9.966

5

0.076

3

Google Maps

Intercept 276.227a

0

0

.
Occupation 284.800b

8.572

5

0.127

Gender 283.265b

7.038

5

0.218

Age 311.900b

35.672

15

0.002

Service_Provider

290.828

14.6

10

0.147

Number_of_Mobiles

297.2

20.972

15

0.138

Subscription_Model 283.607b

7.379

5

0.194

4

Email Applications

Intercept 335.332a

0

0

.
Occupation 348.469b

13.137

5

0.022

Gender 354.186b

18.854

5

0.002

Age 372.135b

36.803

15

0.001

Service_Provider 352.505b

17.173

10

0.071

Number_of_Mobiles

355.308

19.977

15

0.173

Subscription_Model 341.509b

6.177

5

0.289

5

YouTube

Intercept 369.632a

0

0

.
Occupation

374.603

4.971

5

0.419

Gender

376.763

7.131

5

0.211

Age

393.11

23.479

15

0.074

Service_Provider

377.303

7.671

10

0.661

Number_of_Mobiles

381.776

12.144

15

0.668

Subscription_Model

378.744

9.113

5

0.105

6

WhatsApp

Intercept 345.653a

0

0

.
Occupation

353.211

7.558

5

0.182

Gender

367.551

21.898

5

0.001

Age

363.883

18.23

15

0.251

Service_Provider

357.181

11.528

10

0.318

Number_of_Mobiles

356.474

10.822

15

0.765

Subscription_Model 351.854b

6.202

5

0.287

7

Istagram

Intercept 316.802a

0

0

.
Occupation

322.367

5.564

5

0.351

Gender

330.457

13.655

5

0.018

Age

343.058

26.256

15

0.035

Service_Provider

341.162

24.36

10

0.007

Number_of_Mobiles

342.332

25.53

15

0.043

Subscription_Model

334.711

17.909

5

0.003

8

Twitter

Intercept 352.209a

0

0

.
Occupation 366.958b

14.749

5

0.011

Gender 356.442b

4.234

5

0.516

Age 384.396b

32.187

15

0.006

Service_Provider 365.784b

13.575

10

0.193

Number_of_Mobiles

381.498

29.289

15

0.015

Subscription_Model 355.417b

3.208

5

0.668

9

Bluetooth

Intercept 269.114a

0

0

.
Occupation

275.25

6.136

5

0.293

Gender

281.686

12.572

5

0.028

Age

284.934

15.82

15

0.394

Service_Provider

284.865

15.751

10

0.107

Number_of_Mobiles

285.117

16.003

15

0.382

Subscription_Model

270.262

1.148

5

0.95

10

Video

Intercept 291.878a

0

0

.
Occupation

301.068

9.19

4

0.057

Gender

295.179

3.301

4

0.509

Age

305.379

13.5

12

0.334

Service_Provider

306.838

14.959

8

0.06

Number_of_Mobiles

309.794

17.916

12

0.118

Subscription_Model

295.576

3.698

4

0.448

11

Camera

Intercept 324.768a

0

0

.
Occupation

327.293

2.526

4

0.64

Gender

329.194

4.426

4

0.351

Age

337.858

13.09

12

0.363

Service_Provider

340.273

15.505

8

0.05

Number_of_Mobiles

334.432

9.665

12

0.645

Subscription_Model

330.005

5.237

4

0.264

12

Receive SMS

Intercept 275.432a

0

0

.
Occupation

277.969

2.537

4

0.638

Gender

277.601

2.168

4

0.705

Age

280.962

5.529

12

0.938

Service_Provider

286.698

11.265

8

0.187

Number_of_Mobiles

307.081

31.648

12

0.002

Subscription_Model

278.011

2.579

4

0.631

13

Send SMS

Intercept 285.928a

0

0

.
Occupation 288.476b

2.548

5

0.769

Gender 293.966b

8.038

5

0.154

Age 305.533b

19.605

15

0.188

Service_Provider 294.885b

8.957

10

0.536

Number_of_Mobiles

305.355

19.427

15

0.195

Subscription_Model 287.612b

1.684

5

0.891

14

Make Calls

Intercept 242.497a

0

0

.
Occupation 246.231b

3.735

5

0.588

Gender 246.635b

4.138

5

0.53

Age 270.920b

28.424

15

0.019

Service_Provider 251.485b

8.988

10

0.533

Number_of_Mobiles

262.34

19.844

15

0.178

Subscription_Model 243.947b

1.451

5

0.919

15

Receive Calls

Intercept 223.691a

0

0

.
Occupation 225.886b

2.194

5

0.822

Gender 228.639b

4.948

5

0.422

Age 257.699b

34.008

15

0.003

Service_Provider 234.994b

11.303

10

0.334

Number_of_Mobiles

241.013

17.322

15

0.3

Subscription_Model 228.302b

4.611

5

0.465

16

Calendar/Organizer

Intercept 360.686a

0

0

.
Occupation 370.997b

10.311

5

0.067

Gender 367.332b

6.647

5

0.248

Age 382.896b

22.21

15

0.102

Service_Provider 371.317b

10.632

10

0.387

Number_of_Mobiles

375.201

14.516

15

0.487

Subscription_Model 364.506b

3.82

5

0.576

17

Games

Intercept 350.138a

0

0

.
Occupation

352.171

2.033

5

0.845

Gender

358.413

8.274

5

0.142

Age

375.759

25.621

15

0.042

Service_Provider

369.939

19.801

10

0.031

Number_of_Mobiles

362.488

12.35

15

0.652

Subscription_Model

355.894

5.756

5

0.331

Table6: Multinomial regression analysis

As illustrated and highlighted in above table, occupation seems to be the main and only factor affecting the use of “Document Readers (PDF, Word)”, “Application Stores” and “Video”, whereas for “Email Applications” occupation has equal impact with gender variable, However, age has a significant impact it. Age also is the main factor affecting the usage of “Google Map” mobile service. Its also clear that “Twitter” is impacted by occupation, age, and number of mobiles owned, although that age is considered the significant factor among those variables. Gender has a significant impact on the usage of “WhatsApp”, whereas, the usage of “Instagram” is affected by gender, age, service provider, number of mobiles owned, and subscription models, for “Bluetooth” it seems to be affected only by Gender, and for “Camera” it is impacted by the service provider. Receiving SMS is affected by the factor of Number of mobiles owned, but receiving and making calls dependent significantly on the age. Both the service provider and the age impact the usage of “Games”, although that age has the significant impact, however, none of the all analyzed variables seems to have impact on the usage of the following mobile services of “Send SMS”, “Calendar/Organizer” and “Youtube”. 

Conclusions

This study analyses the significant differences in the consumption of mobile phones between students and professionals in the state of Qatar. The results showed that the research is reliable and it captured the importance of information for analyzing the significant differences of mobile phones among students and professionals. Findings of the current study suggest that there is a significant difference for frequency of charging or paying bills (daily, weekly, monthly, every 2 months) between Students and professionals in Qatar, subscription model (pre-paid/post-paid), camera, Twitter, Instagram, WhatsApp, YouTube, games, and calendar/organizer between students and professionals. However, there is no significant differences of number of mobiles owned, service provider (Q-tel, Vodafone, and both Qtel&Vodafone), receive calls, make calls, send SMS, receive SMS, video, Bluetooth, E-mail application, Google maps, application store, and document reader (PDF, Word) between students and professionals, and its 95% confidant to say that both groups are almost exhibiting the same mobile phone usage patterns. Occupation has a significant impact on the usage of Document Readers, Applicant Stores, and videos, whereas both gender and age have significant effect on the usage of email applications, similarly age has significant impact on using Google Maps, Making Calls, Receiving Calls, and Gender has a major impact on using Twitter and Bluetooth, other factors such as number of mobiles owned has significant impact on Receiving Calls, and service providers affect significantly usage of instagram. The research showed that there is addictive usage among both students and professionals in the State of Qatar in using WhatsApp, and Email Application, the researchers strongly believes that other heavily used services such as Twitter, Instagram, and Camera usage will move towards addictive level in the near future. 

Current Study Limitation and Future research suggestions:

Below are list of recommendations for future researches in the same field:

  1. Further analysis need to be done, in order to specify the category that scored addictive usage such as Email Application, and WhatsApp, in order to have precise judgment on professional claims that they work beyond working hours due to mobile usage.
  2. Further analysis need to be done in order to determine, which category of studied occupation caused significant differences between them in using Camera, Twitter, Instagram, frequency of charging/paying bills, and subscription model.
    1. 3.Conduct paper analysis at secondary schools and universities level, since their contribution was limited compare to the professionals, despite researchers attempts to increase this group participation.
  3. Enhance the survey used in this research to collect more information on the following:
    1. Individual Income: Collect individual’s monthly income, in order to determine if the income has significant impact on the usage of mobile services in Qatar or its independent from this factor.
    2. Nationality: to determine Qatari Vs. None Qataris consumption of mobile services in the State of Qatar, and to shade light on whether it has impact on the level of usage or not.

References:

  1. Ahmed, I., Ramazan, M.,Qazi. T., and Jabeen. S. (2011), An Investigation of Mobile Consumption Patterns among Students and Professionals, Is There any Difference?. Retrieved on December 7, 2012, from http://www.eurojournals.com/EJEFAS_39_13.pdf
  2. Arbucle, J.L (2007), AMOS 16 user’s guide, SPSS Chicago, P.50
  3. International Telecommunication Union, Key statistical highlights: ITU data release June 2012. (2012). Retrieved on December 10, 2012, from http://www.itu.int/ITU-D/ict/statistics/material/pdf/2011%20Statistical%20highlights_June_2012.pdf
  4. One third of the world’s population is online, The world in 2011-ICT Facts and Figures.(2011). Retrieved on December 11, 2012, from www.itu.int/ITU-D/ict/material/FactsFigures2010.pdf
  5. Qatar’s ICT Landscape, (2011). Retrieved on December 16, 2012, from http://www.ictqatar.qa/en/documents/document/qatars-ict-landscape-report-2011
  6. Towards the end of double-digit mobile growth, The world in 2010-ICT Facts and Figures.(2010). Retrieved on December 11, 2012, from www.itu.int/ITU-D/ict/material/FactsFigures2010.pdf

Appendix A

Compare Independent Samples T-Test Outputs Using SPSS:

1.     Occupation and Receive Calls

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Receive Calls Student

54

3.17

.885

.120

Professional

139

3.42

.816

.069

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Receive Calls Equal variances assumed

.027

.871

-1.871

191

.063

-.251

.134

-.515

.014

Equal variances not assumed

-1.805

89.992

.074

-.251

.139

-.526

.025

2.     Occupation and Make Calls

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Make Calls Student

54

3.30

.903

.123

Professional

139

3.50

.871

.074

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

Df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Make Calls Equal variances assumed

.103

.748

-1.469

191

.144

-.207

.141

-.486

.071

Equal variances not assumed

-1.445

93.525

.152

-.207

.143

-.492

.078

 3.     Occupation and Send SMS

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Send SMS Student

54

2.46

.905

.123

Professional

139

2.64

1.000

.085

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Send SMS Equal variances assumed

.821

.366

-1.135

191

.258

-.177

.156

-.486

.131

Equal variances not assumed

-1.186

106.025

.238

-.177

.150

-.474

.119

 4.     Occupation and Receive SMS

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Receive SMS Student

54

2.93

.866

.118

Professional

139

2.94

.938

.080

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Receive SMS Equal variances assumed

.804

.371

-.112

191

.911

-.017

.147

-.307

.274

Equal variances not assumed

-.116

104.127

.908

-.017

.142

-.298

.265

5.     Occupation and Camera

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Camera Student

54

3.93

1.113

.152

Professional

139

3.22

1.196

.101

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

Df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Camera Equal variances assumed

.008

.927

3.772

191

.000

.710

.188

.339

1.081

Equal variances not assumed

3.894

103.233

.000

.710

.182

.348

1.072

 6.     Occupation and Video

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Video Student

54

2.67

.911

.124

Professional

139

2.45

1.072

.091

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Video Equal variances assumed

1.915

.168

1.293

191

.198

.213

.165

-.112

.539

Equal variances not assumed

1.388

112.751

.168

.213

.154

-.091

.518

 7.     Occupation and Bluetooth

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Bluetooth Student

54

1.89

1.003

.137

Professional

139

1.86

1.146

.097

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Bluetooth Equal variances assumed

.512

.475

.184

191

.854

.033

.178

-.318

.383

Equal variances not assumed

.196

109.518

.845

.033

.168

-.299

.365

 8.     Occupation and Twitter

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Twitter Student

54

3.80

1.630

.222

Professional

139

2.76

1.654

.140

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Twitter Equal variances assumed

.278

.598

3.940

191

.000

1.041

.264

.520

1.562

Equal variances not assumed

3.966

97.904

.000

1.041

.262

.520

1.562

 9.     Occupation and Instagram

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Instagram Student

54

3.30

1.880

.256

Professional

139

2.71

1.807

.153

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Instagram Equal variances assumed

.345

.558

1.993

191

.048

.584

.293

.006

1.162

Equal variances not assumed

1.959

93.261

.053

.584

.298

-.008

1.176

 10.   Occupation and WhatsApp

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

WhatsApp Student

54

4.24

1.063

.145

Professional

139

3.76

1.462

.124

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

T

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

WhatsApp Equal variances assumed

11.158

.001

2.187

191

.030

.478

.219

.047

.909

Equal variances not assumed

2.509

132.181

.013

.478

.191

.101

.855

 11.   Occupation and You Tube

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

You Tube Student

54

3.52

1.255

.171

Professional

139

3.02

1.294

.110

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

You Tube Equal variances assumed

.206

.650

2.415

191

.017

.497

.206

.091

.903

Equal variances not assumed

2.448

99.283

.016

.497

.203

.094

.900

 12.   Occupation and Email Applications

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Email Applications Student

54

3.65

1.456

.198

Professional

139

3.37

1.431

.121

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Email Applications Equal variances assumed

.047

.828

1.189

191

.236

.274

.231

-.181

.729

Equal variances not assumed

1.180

95.107

.241

.274

.232

-.187

.735

 13.   Occupation and Games Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Games

Student

54

2.83

1.514

.206

Professional

139

2.24

1.333

.113

 

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Games Equal variances assumed

2.530

.113

2.682

191

.008

.596

.222

.158

1.034

Equal variances not assumed

3.110

113.028

.013

.596

.235

.129

14.   Occupation and Calendar / Organizer

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Calendar / Organizer Student

54

3.43

1.126

.153

Professional

139

2.83

1.328

.113

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

T

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Calendar / Organizer Equal variances assumed

2.222

.138

2.893

191

.004

.591

.204

.881

.995

Equal variances not assumed

3.110

113.028

.002

.591

.190

.215

.968

 15.   Occupation and Google maps

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Google maps Student

54

2.04

.971

.132

Professional

139

2.02

1.113

.094

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Google maps Equal variances assumed

1.741

.189

.090

191

.929

.015

.172

-.325

.356

Equal variances not assumed

.095

109.959

.924

.015

.162

-.306

.337

16.   Occupation and Applications Store

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Applications Store Student

54

3.24

1.243

.169

Professional

139

2.85

1.388

.118

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Applications Store Equal variances assumed

1.972

.162

1.811

191

.072

.392

.216

-.035

.819

Equal variances not assumed

1.901

107.134

.060

.392

.206

-.017

.800

 17.   Occupation and Document Reader (PDF, WORD)

Group Statistics

Occupation

N

Mean

Std. Deviation

Std. Error Mean

Document Reader (PDF, WORD) Student

54

2.59

1.296

.176

Professional

139

2.42

1.335

.113

Independent Samples Test

Levene’s Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Document Reader (PDF, WORD) Equal variances assumed

.052

.820

.826

191

.410

.175

.212

-.243

.594

Equal variances not assumed

.837

99.189

.405

.175

.210

-.241

.591

 

Appendix B

Charts illustrating Level of Usage for 17 mobile services:

Screen Shot 2014-03-03 at 12.31.28 AM Screen Shot 2014-03-03 at 12.31.42 AM Screen Shot 2014-03-03 at 12.32.37 AM Screen Shot 2014-03-03 at 12.32.27 AM Screen Shot 2014-03-03 at 12.32.17 AM Screen Shot 2014-03-03 at 12.32.04 AM Screen Shot 2014-03-03 at 12.31.55 AM

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About aemadi

Qatari Information Technology professional. Who witnessed technology revolution back in 1995. Master in Business Administration, from Qatar University. Perfectionist and passionate about my country. Hopefully will leave thumbnail to this live.
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