SEM-based study on the characteristics of business activities
Received date: 2013-11-08
Request revised date: 2014-02-25
Online published: 2014-08-10
Copyright
The combination of information and communication technology (ICT) and rapid transit technology in today's information age has accelerated the spatio-temporal fragmentation of activities and has influenced travel behavior. Information and transportation networks are embedded in the “space of flows,” thus becoming a major component of simultaneous social interactions. “Mobility elites” connect physical and information flows with the new mobility paradigm. Changes in the behavioral activities of mobility elites under a novel background is worthy of further study because business people comprise a vital sector of the high-mobility population. In this empirical study, we analyzed the influence of ICT and transportation on business activities using a sample of sales team members of a multinational corporation in the medical device industry in Nanjing, China. We collected spatio-temporal data on 11 members of the selected sales team for 7 consecutive working days through global positioning system details, activity diaries, and in-depth interviews.We derived the following conclusions through structural equation modeling. First, the use of ICT and that of rapid transit technology in business are correlated (correlation coefficient = 0.439). The combined application of ICT and rapid transit technology promotes the integration of business activities in both cyber and physical spaces, results in a “new mobile paradigm” for business activities, and leads to the emergence of activities that serve as an external manifestation of the space of flows. Second, rapid transit technology significantly and positively influences business activities by affecting place type, flow state, and spatial distribution. It plays a more important role in business activities than ICT does (path coefficient = 0.709). The combination of high speed rail-based cross-city mobility and taxi-based inner-city mobility results in the seamless connection between places and, consequently, in the spatial fragmentation of business activities. Third, ICT directly (path coefficient = -0.060) and indirectly (path coefficient = 0.015) affects business, as its use leads to the temporal fragmentation of business activities. Business travel occurs between 9:00 to 18:00, whereas actual business operations occur between 18:00 and 22:00.
ZHAI Qing , ZHEN Feng , Wang Xia , CHEN Ying Xue . SEM-based study on the characteristics of business activities[J]. GEOGRAPHICAL RESEARCH, 2014 , 33(8) : 1477 -1488 . DOI: 10.11821/dlyj201408008
Tab. 1 Basic information about the respondents表1 受访者基本情况 |
编号 | 性别 | 年龄(岁) | 学历 | 职位 | 一周跨城流动次数 | 随身携带终端设备 | ||
---|---|---|---|---|---|---|---|---|
高铁 | 长途大巴 | 飞机 | ||||||
1 | 男 | 31~40 | 本科 | 工程师 | 6 | 2 | 0 | 手机、笔记本、密钥 |
2 | 男 | 21~30 | 本科 | 工程师 | 2 | 2 | 0 | |
3 | 男 | 21~30 | 本科 | 销售 | 0 | 0 | 2 | |
4 | 女 | 31~40 | 本科 | 市场推进 | 6 | 0 | 0 | |
5 | 男 | 21~30 | 本科 | 销售 | 10 | 0 | 0 | |
6 | 男 | 21~30 | 本科 | 工程师 | 2 | 3 | 0 | |
7 | 男 | 21~30 | 本科 | 工程师 | 1 | 2 | 2 | |
8 | 男 | 21~30 | 大专 | 工程师 | 0 | 3 | 3 | |
9 | 男 | 31~40 | 本科 | 销售 | 1 | 0 | 4 | |
10 | 女 | 21~30 | 本科 | 主管 | 3 | 1 | 0 | |
11 | 女 | 21~30 | 本科 | 销售 | 2 | 0 | 2 |
Tab.2 Description of variable assignment表2 变量赋值说明 |
变量 | 赋值 | 样本量(个) | 说 明 | 变量 | 赋值 | 样本量(个) | 说明 |
---|---|---|---|---|---|---|---|
信息技术使用 | 1 | 94 | 三种都使用 | 商务活动 | 1 | 77 | 客户办公 |
2 | 21 | 使用手机和笔记本 | 2 | 28 | 酒店办公 | ||
3 | 338 | 使用手机 | 3 | 16 | 总部办公 | ||
4 | 9 | 使用笔记本 | 4 | 23 | 公司办公 | ||
5 | 6 | 不使用信息设备 | 5 | 11 | 居家办公 | ||
快速交通技术 | 1 | 172 | 固定状态 | 6 | 39 | 移动办公 | |
2 | 1 | 步行 | 7 | 274 | 商务出行 | ||
3 | 1 | 公交车 | 流动状态 | 1 | 172 | 没有流动 | |
4 | 206 | 出租车 | 2 | 238 | 同城流动 | ||
5 | 12 | 自驾车 | 3 | 58 | 跨城流动 | ||
6 | 18 | 地铁 | 场所类型 | 1 | 124 | 工作地(公司、总部、客户所在地) | |
7 | 14 | 大巴 | 2 | 40 | 居住地(家、酒店) | ||
8 | 34 | 高铁、动车 | 3 | 298 | 移动中 | ||
9 | 10 | 飞机 | 4 | 6 | 其他 | ||
时间分布 | 1 | 70 | 18:00-22:00 | 空间分布 | 1 | 99 | 江苏省以外 |
2 | 348 | 9:00-18:00 | 2 | 205 | 江苏省内,南京市外 | ||
3 | 50 | 6:00-9:00 | 3 | 164 | 南京市内 |
Fig. 1 Comparison between the respondents' various working times图1 受访者不同形式的商务办公时间比较 |
Fig. 2 Research framework图2 研究框架 |
Tab. 3 Goodness of fit indices of criteria of SEM表3 结构方程模型拟合度检验 |
拟合指数 | 绝对拟合指数 | 相对拟合指数 | 信息指数 AIC | |||||||
---|---|---|---|---|---|---|---|---|---|---|
RMR | GFI | AGFI | RMSEA | NFI | RFI | IFI | TLI | CFI | ||
理想值 | <0.05 | >0.90 | >0.90 | <0.05 | >0.90 | >0.90 | >0.90 | >0.90 | >0.90 | 越接近饱和模型越好 |
假设模型 | 0.021 | 0.992 | 0.977 | 0.030 | 0.994 | 0.988 | 0.998 | 0.996 | 0.998 | 50.133 |
饱和模型 | 0.000 | 1.000 | - | - | 1.000 | - | 1.000 | - | 1.000 | 56.000 |
独立模型 | 0.047 | 0.696 | 0.629 | 0.205 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2430.359 |
Fig. 3 The direct effect between endogenous variables and exogenous variables图3 内生变量与外生变量之间的直接效应路径图 注:*表示在0.05以上的水平上显著,其余均在0.001以上的水平上显著。 |
Tab. 4 Total, direct and indirect effects between exogenous variables and endogenous variables表4 外生变量对内生变量的总体效应、直接效应和间接效应 |
外生变量效应 | 快速交通技术 | 信息技术使用 | ||||
---|---|---|---|---|---|---|
总体效应 | 直接效应 | 间接效应 | 总体效应 | 直接效应 | 间接效应 | |
场所类型 | 0.731** | 0.731** | 0 | 0 | 0 | 0 |
流动状态 | 0.956** | 0.837** | 0.119** | 0 | 0 | 0 |
空间类型 | -0.181** | -0.181** | 0 | 0 | 0 | 0 |
时间类型 | 0 | 0 | 0 | 0.156** | 0.156** | 0 |
商务活动 | 0.709** | 0 | 0.709** | -0.044* | -0.060* | 0.015** |
注:以上数值均为标准化后的值,*表示在0.05及以上水平显著,**表示在0.001及以上水平极显著,下同。 |
Tab. 5 Total, direct and indirect effects between endogenous variables表5 内生变量之间的总体效应、直接效应和间接效应 |
流动状态 | 商务活动 | |||||
---|---|---|---|---|---|---|
总体效应 | 直接效应 | 间接效应 | 总体效应 | 直接效应 | 间接效应 | |
场所类型 | 0.163** | 0.163** | 0 | 0.752** | 0.715** | 0.036** |
流动状态 | 0 | 0 | 0 | 0.222** | 0.222** | 0 |
空间类型 | 0 | 0 | 0 | 0.151** | 0.151** | 0 |
时间类型 | 0 | 0 | 0 | 0.099** | 0.099** | 0 |
The authors have declared that no competing interests exist.
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