Exploring regional disparities of Big-Data applications in ...€¦ · ranging from industry,...
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Exploring regional disparities of Big-Data applications in China Introduction Conclusions Author2 Prof. Dr. Christoph Mingtao Shi Professor for International Management and Entrepreneurship, bbw Hochschule Berlin [email protected] [email protected] Author1 Martin Lechner MA Student (China Studies) Freie Universität Berlin [email protected] [email protected] The ongoing project intends to answer a number of research questions, which have rarely been explored in the literature: In which functional areas is Big-Data deployed in China? How do the applications differ from province to province and why do these regional disparities exist? Which managerial implications can be derived for German firms? The poster illustrates how MAXQDA has contributed to effectively investigating the defined research questions. Methodologically, over 100 Chinese governmental documents from central and local administrative hierarchies as well as qualitative interviews and observations have been adopted as primary data to delineate essential issues related to Big-Data in China. Word-cloud has been especially valuable while searching for repeatedly mentioned words and phrases in a large base of primary data, in order to identify relevant key codes. Most inspiringly, a list of connected texts and transcripts is displayed instantaneously with just one click, revealing the sources of the respective words. In this word-cloud, a connection to the initiative 互联网+ (“Internet+”) and other areas such as 进出口 (import and export), 药品 (medicines) or 文化 (culture) is being revealed. Even uncommon expressions such as 三证合一 (“to merge three certificates into one”) or 一照一码 (“one-pass-one-number”) are recognized and interpreted correctly by MAXQDA. As the next research step, combining the codes stemming from the word-cloud, the lexical search and the code system, MAXQDA is able to create a compact code-matrix, illustrating the “landscape” of Big-Data usage in China elegantly. The province Guangxi and Guizhou for example both stress poverty abatement, while Guizhou also emphasizes innovation, agriculture, industry and additional aspects. An interesting resultant question would be why so few regions intend to apply Big-Data for poverty reduction? How can Big-Data be used to do so? MAXQDA assisted greatly in obtaining an overview of the spatial distribution of a huge span of Big-Data application fields in China, ranging from industry, agriculture, culture, healthcare to poverty reduction. Apparently, regions and provinces are not striving for homogenous but heterogeneous deployment opportunities, presumably because of heterogeneous economic conditions, user expectations or policy preferences. Further research steps are required to better answer the why question sufficiently. Using the research results, German firms may design market products related to Big-Data, select potential Chinese collaborators and identify indigenous competitors more effectively, in order to strategize and to economize. Analysis Figure 4 存储 (to save), 共享 (to share) are the most frequently mentioned words Figure 5 An overview of Big-Data diversity is achieved by juxtaposing the application fields (left column) and the regions or provinces (top row) Figure 6 Results are aggregated and summarized visually in a map of China, using MAXQDA’s emoticodes* * only 34 objects are being highlighted here . An absence of a symbol in a certain province shows that the application is not emphasized there, which does not necessarily imply that there is no related application at all. M ap Legend: Signpost (demonstration or testing sites) Tractor (agriculture or farming) Light bulb (innovation) Hospital (related to healthcare) Three screens (big data industry) Factory (industry) Globe (environment) Hand holding coins (poverty abatement, only in Guizhou and Guangxi) Classic features such as Document- Browser and List of Documents can easily accommodate a range of data types including document texts, interview transcripts and observation notes, enabling the researcher to read, sort and codify the primary data in the most efficient manner. Systemizing, integrating and transcending the codes is an essential preparation for building the categories with theoretical implications. The coding system facilitates the relevant analysis by installing a coding environment, in which codes and sub-codes can be added, deleted, layered and changed with great ease. While exploring reasons for regional disparities of Big-Data applications, the Multimedia-Browser demonstrates a great effectiveness for the researcher to extract vital codes from the audio or video data directly without time-consuming transcription process. Data compilation & Coding Figure 1 “Agriculture”, “Artificial Intelligence” and “Machine Learning” as the applicational emphasis of Big-Data in the region of Shanghai Figure 3 “Umwelt” (environment) as a main code encompasses sub- codes like 污染 (pollution) and 保护 环境 (environmental protection) Figure 2 Audio recording of a narrative interview and its coding process province outline of china derived from: www.d-maps.com Coding via Word-cloud
Transcript of Exploring regional disparities of Big-Data applications in ...€¦ · ranging from industry,...
Exploring regional disparities of Big-Data applications in China
Introduction
Conclusions
Author2Prof. Dr. Christoph Mingtao ShiProfessor for International Management and Entrepreneurship, bbw Hochschule [email protected]@campus.tu-berlin.de
Author1Martin Lechner
MA Student (China Studies)Freie Universität Berlin
[email protected]@outlook.de
The ongoing project intends to answer a number of research questions, which have rarely been explored in the literature: In which functional areas isBig-Data deployed in China? How do the applications differ from province to province and why do these regional disparities exist? Which managerialimplications can be derived for German firms? The poster illustrates how MAXQDA has contributed to effectively investigating the defined researchquestions. Methodologically, over 100 Chinese governmental documents from central and local administrative hierarchies as well as qualitativeinterviews and observations have been adopted as primary data to delineate essential issues related to Big-Data in China.
Word-cloud has been especially valuable while searching forrepeatedly mentioned words and phrases in a large base of primarydata, in order to identify relevant key codes. Most inspiringly, a listof connected texts and transcripts is displayed instantaneously withjust one click, revealing the sources of the respective words. In thisword-cloud, a connection to the initiative 互联网+ (“Internet+”)and other areas such as 进出口 (import and export), 药品(medicines) or 文化 (culture) is being revealed. Even uncommonexpressions such as 三证合一 (“to merge three certificates intoone”) or 一照一码 (“one-pass-one-number”) are recognized andinterpreted correctly by MAXQDA.
As the next research step, combining the codes stemming from theword-cloud, the lexical search and the code system, MAXQDA isable to create a compact code-matrix, illustrating the “landscape”of Big-Data usage in China elegantly.The province Guangxi and Guizhou for example both stress povertyabatement, while Guizhou also emphasizes innovation, agriculture,industry and additional aspects. An interesting resultant questionwould be why so few regions intend to apply Big-Data for povertyreduction? How can Big-Data be used to do so?
MAXQDA assisted greatly in obtaining an overview of the spatialdistribution of a huge span of Big-Data application fields in China,ranging from industry, agriculture, culture, healthcare to povertyreduction. Apparently, regions and provinces are not striving forhomogenous but heterogeneous deployment opportunities,presumably because of heterogeneous economic conditions, userexpectations or policy preferences. Further research steps arerequired to better answer the why question sufficiently.Using the research results, German firms may design marketproducts related to Big-Data, select potential Chinese collaboratorsand identify indigenous competitors more effectively, in order tostrategize and to economize.
Analysis
Figure 4 存储 (to save), 共享 (to share) are the most frequently mentioned words
Figure 5 An overview of Big-Data diversity is achieved by juxtaposing the application fields (left column) and the regions or provinces (top row)
Figure 6 Results are aggregated and summarized visually in a map of China, using MAXQDA’s emoticodes*
* only 34 objects are being highlighted here. An absence of a symbol in a certain province shows that the application is not emphasized there, which does not necessarily imply that there is no related application at all. Map Legend:
Signpost (demonstration or testing sites) Tractor (agriculture or farming) Light bulb (innovation)
Hospital (related to healthcare) Three screens (big data industry) Factory (industry)
Globe (environment) Hand holding coins (poverty abatement, only in Guizhou and Guangxi)
Classic features such as Document-Browser and List of Documents caneasily accommodate a range of datatypes including document texts,interview transcripts and observationnotes, enabling the researcher to read,sort and codify the primary data in themost efficient manner.
Systemizing, integrating andtranscending the codes is anessential preparation for buildingthe categories with theoreticalimplications. The coding systemfacilitates the relevant analysis byinstalling a coding environment, inwhich codes and sub-codes can beadded, deleted, layered andchanged with great ease.
While exploring reasonsfor regional disparities ofBig-Data applications, theMultimedia-Browserdemonstrates a greateffectiveness for theresearcher to extract vitalcodes from the audio orvideo data directlywithout time-consumingtranscription process.
Data compilation & Coding
Figure 1 “Agriculture”, “Artificial Intelligence” and “Machine Learning” as the applicational emphasis of Big-Data in the region of Shanghai
Figure 3 “Umwelt” (environment) as a main code encompasses sub-codes like 污染(pollution) and 保护环境 (environmental protection)
Figure 2 Audio recording of a narrative interview and its coding process
province outline of china derived from: www.d-maps.com
Coding via Word-cloud
