成都城乡居民手机使用行为研究
发布时间：2018-08-31 10:02
【摘要】：随着手机在人们日常生活中的普及和手机功能集成化的日益增加,手机使用行为越来越引起人们的关注。本研究在界定手机使用行为概念的基础上,采用方差分析、线性回归等研究技术对成都市城乡居民的手机使用行为及其影响因素和手机使用行为对手机依赖度的影响进行了深入探讨。 首先,笔者对成都市城乡居民的手机使用概况进行了简要的描述统计。手机使用概况分为手机持有情况和手机使用情况。手机持有情况分为手机使用年数、手机使用部数、使用手机原因、手机获得来源、手机购买价格和手机品牌选择等6个方面;手机使用情况分为每天打电话次数、每天发短信条数和每月手机资费等3个方面。 其次,笔者从手机功能知晓度、手机功能使用频率及其重要性评价3个维度对手机功能使用行为进行了总体分析,分析发现这3个维度在总体上呈正相关关系,即调查对象越清楚自己手机有哪些功能(主观上的手机功能多寡),那么他使用这些功能的频率就会越高,既而他也认为这些功能越重要。随后笔者将手机使用行为归纳为五个方面:通讯联系、网络应用、休闲娱乐、工具扩展和个人助理。接着,笔者采用多元方差分析详细探讨了人口属性分别对这五个方面的手机使用行为的不同影响。结果发现,年龄和文化程度对手机使用行为的5个方面都有显著影响,年龄与手机使用行为呈负相关,文化程度与手机使用行为呈正相关。 再次,笔者对城乡手机调查问卷中手机依赖量表进行了分析,在确定其信效度达标的基础上,计算了调查对象的量表得分,并把结果化分为轻度依赖、中度依赖、高度依赖三个等级。接着笔者探讨了性别、年龄、职业、在业状况和文化程度等人口属性特征在手机依赖度上是否存在显著差异。结果发现,性别在手机依赖度上无显著差异。 最后,笔者构建了手机依赖理论模型,把手机依赖分为工作依赖和生活依赖,接着再分为通讯联系依赖、网络应用依赖、休闲娱乐依赖、工具扩展依赖、个人助理依赖。其中除了休闲娱乐依赖和个人助理依赖属于生活依赖外,其他的三项既属于工作依赖也属于生活依赖。随后,笔者利用多元线性回归模型探讨了手机使用行为(通讯联系、网络应用、休闲娱乐、工具扩展、个人助理)对手机依赖度的影响。结果发现,个人助理(P=0.0770.05)对手机依赖度不存在显著影响。由于手机依赖量表得分的总体分布是显著的非正态性,因此在做多元线性回归之前,对其进行了数据转换。
[Abstract]:With the popularity of mobile phone in our daily life and the increasing integration of mobile phone functions, mobile phone use behavior has attracted more and more attention. On the basis of defining the concept of mobile phone use behavior, this study adopts ANOVA. Linear regression and other research techniques are used to study the mobile phone use behavior of urban and rural residents in Chengdu and its influencing factors and the influence of mobile phone use behavior on mobile phone dependence. First of all, the author briefly describes the mobile phone usage of urban and rural residents in Chengdu. Mobile phone use profile is divided into mobile phone holdings and mobile phone use. Mobile phone holdings are divided into six aspects: the number of years of mobile phone use, the number of mobile phone users, the reasons for using the mobile phone, the source of mobile phone acquisition, the price of mobile phone purchase and the choice of mobile phone brand; the use of mobile phones is divided into six aspects: the number of phone calls per day. Daily SMS number and monthly mobile phone charges and other three aspects. Secondly, the author makes a general analysis of the mobile phone function using behavior from the three dimensions of mobile phone function awareness degree, mobile phone function usage frequency and its importance evaluation, and finds that these three dimensions are positively correlated as a whole. That is, the more clearly the subject knows what functions his phone has, the more frequently he uses them, and the more important he thinks they are. Then the author concludes the mobile phone use behavior into five aspects: communication, network application, leisure and entertainment, tool extension and personal assistant. Then, the author uses multivariate variance analysis to discuss the different effects of population attributes on the mobile phone use behavior in these five aspects in detail. The results showed that age and education had significant effects on five aspects of mobile phone use behavior, age had negative correlation with mobile phone use behavior, and education level had positive correlation with mobile phone use behavior. Thirdly, the author analyzes the mobile phone dependence scale in the questionnaire of mobile phone in urban and rural areas. On the basis of determining the reliability and validity of the questionnaire, the author calculates the score of the questionnaire, and divides the results into mild dependence and moderate dependence. Height depends on three levels. Then the author discusses whether there are significant differences in the dependence of mobile phone on the characteristics of population attributes such as sex, age, occupation, working status and education level. The results showed that there was no significant difference in the dependence of mobile phone. Finally, the author constructs the mobile phone dependence theory model, divides the mobile phone dependence into the work dependence and the life dependence, then divides into the communication connection dependence, the network application dependence, the leisure entertainment dependence, the tool extension dependence, the personal assistant dependence. In addition to leisure and entertainment dependence and personal assistant dependence belong to life dependence, the other three belong to both work and life dependence. Then, the author uses the multiple linear regression model to study the influence of mobile phone use behavior (communication link, network application, leisure entertainment, tool extension, personal assistant) on the dependence of mobile phone. The results showed that personal assistant (PX 0.077 0.05) had no significant effect on the dependence of mobile phone. Since the total distribution of mobile phone dependency scale scores is significantly non-normal, data conversion is performed before multivariate linear regression.
【学位授予单位】：四川省社会科学院
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：C912
本文编号：2214654
[Abstract]:With the popularity of mobile phone in our daily life and the increasing integration of mobile phone functions, mobile phone use behavior has attracted more and more attention. On the basis of defining the concept of mobile phone use behavior, this study adopts ANOVA. Linear regression and other research techniques are used to study the mobile phone use behavior of urban and rural residents in Chengdu and its influencing factors and the influence of mobile phone use behavior on mobile phone dependence. First of all, the author briefly describes the mobile phone usage of urban and rural residents in Chengdu. Mobile phone use profile is divided into mobile phone holdings and mobile phone use. Mobile phone holdings are divided into six aspects: the number of years of mobile phone use, the number of mobile phone users, the reasons for using the mobile phone, the source of mobile phone acquisition, the price of mobile phone purchase and the choice of mobile phone brand; the use of mobile phones is divided into six aspects: the number of phone calls per day. Daily SMS number and monthly mobile phone charges and other three aspects. Secondly, the author makes a general analysis of the mobile phone function using behavior from the three dimensions of mobile phone function awareness degree, mobile phone function usage frequency and its importance evaluation, and finds that these three dimensions are positively correlated as a whole. That is, the more clearly the subject knows what functions his phone has, the more frequently he uses them, and the more important he thinks they are. Then the author concludes the mobile phone use behavior into five aspects: communication, network application, leisure and entertainment, tool extension and personal assistant. Then, the author uses multivariate variance analysis to discuss the different effects of population attributes on the mobile phone use behavior in these five aspects in detail. The results showed that age and education had significant effects on five aspects of mobile phone use behavior, age had negative correlation with mobile phone use behavior, and education level had positive correlation with mobile phone use behavior. Thirdly, the author analyzes the mobile phone dependence scale in the questionnaire of mobile phone in urban and rural areas. On the basis of determining the reliability and validity of the questionnaire, the author calculates the score of the questionnaire, and divides the results into mild dependence and moderate dependence. Height depends on three levels. Then the author discusses whether there are significant differences in the dependence of mobile phone on the characteristics of population attributes such as sex, age, occupation, working status and education level. The results showed that there was no significant difference in the dependence of mobile phone. Finally, the author constructs the mobile phone dependence theory model, divides the mobile phone dependence into the work dependence and the life dependence, then divides into the communication connection dependence, the network application dependence, the leisure entertainment dependence, the tool extension dependence, the personal assistant dependence. In addition to leisure and entertainment dependence and personal assistant dependence belong to life dependence, the other three belong to both work and life dependence. Then, the author uses the multiple linear regression model to study the influence of mobile phone use behavior (communication link, network application, leisure entertainment, tool extension, personal assistant) on the dependence of mobile phone. The results showed that personal assistant (PX 0.077 0.05) had no significant effect on the dependence of mobile phone. Since the total distribution of mobile phone dependency scale scores is significantly non-normal, data conversion is performed before multivariate linear regression.
【学位授予单位】：四川省社会科学院
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：C912
【引证文献】
相关硕士学位论文 前2条
1 王凤仙;上海大学生智能手机的使用行为研究[D];上海交通大学;2013年
2 胡洋;基于方法—目的链的高端手机消费者需求研究[D];北京邮电大学;2013年
，本文编号：2214654
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