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论文编号：	13780
作者编号：	2120202981
上传时间：	2023/3/10 16:21:07
中文题目：	任务场景中数据分析师信息搜寻行为研究——以某企业为例
英文题目：	Research on Information Seeking Behavior of Data Analysts in Task Scenarios: Take an Enterprise as an Example
指导老师：	李月琳
中文关键字：	信息搜寻；任务特征；信息源选择；数据分析师
英文关键字：	Information Seeking; Task Characteristics; Information Source Selection; Data Analyst
中文摘要：	数据分析师可以帮助企业从海量的数据中获取有用的信息，从而帮助企业更好的运营。数据分析师相较于其他职业具有一定的独特性。在任务场景中，数据分析师会产生问题导向的信息需求，并进行相应的信息搜寻活动，通过研究他们的搜寻行为可以帮助数据分析师提高信息搜寻的效率，并拓展针对职业人群的信息搜寻行为研究。 本文通过对国内外任务以及信息搜寻方面相关文献的概述，分析总结出目前研究的不足，以此明确本文的研究问题，具体为：1、工作任务如何影响信息搜寻行为？2、数据分析师在任务场景中如何进行信息源选择？在此基础上，对任务、信息搜寻等基础概念和理论进行详细的定义和阐述，以此作为概念基础和理论铺垫。在明确了本文的研究方法，即案例研究法后，对案例中的研究对象进行了数据收集，并对原始资料展开了三级编码，即开放编码、主轴编码、选择编码三个过程，形成并提出了任务特征、信息需求类型、信息源类型以及信息源选择标准的若干范畴和具体类属，依据于此，分别建立了关系图。将关系图进行联系整合，提出了任务场景下数据分析师信息搜寻行为模型，并对模型进行了饱和度检验。 基于任务场景下数据分析师信息搜寻行为模型，本研究进行了影响因素的深度分析，具体包括任务的模糊性、复杂性、交叉性和独特性会对数据分析师的信息搜寻行为产生重要的间接影响；任务职级会通过影响数据分析师在任务不同阶段的参与程度，来对其信息搜寻行为产生间接影响；信息源的相关性、实时性以及获取便捷性是影响数据分析师在搜寻活动中进行信息源选择的最关键因素；任务阶段和任务职级都会影响数据分析师的信息需求和信息源选择偏好，进而形成不同的信息搜寻策略。在此基础上，总结出了个人和企业两个层面的对策和建议来帮助数据分析师提高信息搜寻效率。
英文摘要：	Data analysts can help enterprises obtain useful information from massive data, so as to help enterprises operate better. Compared with other professions, data analyst has certain uniqueness. In task scenarios, data analysts will have problem-oriented information needs and information seeking behavior. Studying their search behavior can help data analysts improve the efficiency of information search and expand the research on information search behavior for professional groups. This paper analyzes and summarizes the shortcomings of the current research by summarizing the relevant literature on task and information search at home and abroad, so as to clarify the research questions of this paper, which are as follows: 1. How does work task affect information seeking behavior? 2. How do data analysts select information sources in task scenarios? The basic concepts and theories such as task and information search are defined and elaborated in detail, which serve as the conceptual basis and theoretical foundation. After clarifying the research method of this paper, which is the case study method, data was collected for the research objects in the case. Three levels of coding, namely open coding, spindle coding and selective coding, were developed for the original data, and several categories and specific categories of task characteristics, information demand types, information source types and information source selection criteria were proposed. Based on these, the relationship diagrams were established respectively. The information seeking behavior model of data analysts in task scenarios is proposed by combining the relationship graphs, and the saturation test of the model is conducted. Based on the information search behavior model of data analysts in task scenarios, this study conducted in-depth analysis of the influencing factors, including the ambiguity, complexity, crossover and uniqueness of tasks will have important indirect effects on the information seeking behavior of data analysts. Task rank has an indirect impact on the information seeking behavior of data analysts by influencing their participation in different stages of the task. The most critical factors affecting the information source selection of data analysts are the relevance, real-time and easy access of information sources. Task stage and task rank will affect the information demand and information source selection preference of data analysts, and then form different information seeking strategies. On this basis, the author summarizes countermeasures and suggestions at both individual and enterprise levels to help data analysts improve the efficiency of information seeking.
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