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| The Implementation Path of Data Empowerment:A Case Study Based on Resource Orchestration |
| Hu Haibo,WangYiqin,Lu Haitao,Liu Chen |
| (School of Business Administration，Jiangxi University of Finance and Economics，Nanchang 330032，China) |
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Abstract Nowadays, data has become a key factor in corporate performance improvement and value creation. Meanwhile, the concept of data empowerment emerges, and it emphasizes the empowering value acquired by enterprises based on the use of data information and data technology. However currently only one-third of enterprises have activated data value, and not all companies trying to use data are able to discover the empowering value of data. Wherefore the empowering value of data currently needs to be further explored. The key to implement data empowerment is the implementation path instead of data itself. On this basis, this study focuses on how enterprises use data empowerment to obtain data empowering value. According to the existing studies, it is found that data is usually taken as a collection of information and its application is explored from a technical point of view. Consequently enterprises gain little despite of heavy investment. Therefor it is necessary to study data empowerment from a comprehensive perspective of resources, capabilities, and environment. Meanwhile, the existing studies have also pointed out that, only when an enterprise's resources and capabilities are effectively orchestrated and managed can the intrinsic value be truly obtained. The resource orchestration model can help enterprises open the black box of the process from resources to value. Therefore, this study utilizes the resource orchestration model to help enterprises deeply analyze the implement path of data empowerment. It will specifically explain how enterprises use the resource orchestration model to obtain the enabling value of data in the context of data so as to achieve data empowerment.#br#Taking Jiangxi Jucai Human Resources Group Co., Ltd. asthe exaple, this study adopted an exploratory single case study method and collected first-hand data through semi-structured interviews and field visits, and second-hand data through corporate official websites, public accounts, brochures, industry research reports and related news reports. Next, following the coding requirements of the normative coding steps for qualitative data in case studies, this study conducted three rounds of data analysis to systematically conceptualize the phenomenon. Eventually, a theoretical framework with logical relationships was formed.#br#Based on the resource orchestration model, enterprises can implement data empowerment through three processes: data structuring, data bundling with other enterprise resources, data leveraging and value creation. According to different stages of business development, the forms of data empowerment include supporting data empowerment, driving data empowerment and enabling data empowerment, and they have different implementation paths. Specifically, in the business formation stage, data structuring is formed through the acquisition of traditional information. Data bundling is implemented through the reorganization of existing resources based on personal experience analysis. Ultimately, data can support business formation, thereby realizing supporting data empowerment. In the business development stage, data structuring is formed through the intelligent acquisition of data information and the synchronization of data technology. Data bundling is implemented through the development of heterogeneous resources based on data information analysis. Therefore data can drive business expansion and implement driving data empowerment. In the business innovation stage, data structuring is formed through data information sharing, digital technology upgrade and technical team iteration. Data bundling is implemented through enterprise organization update based on data value mining. Ultimately, data can enable business innovation and implement enabling data empowerment.#br#Starting from the perspective of resources, this study clarifies the implementation path of data empowerment and provides a new research perspective for the study of data empowerment. Compared with the existing research which regards data empowerment as the intermediate process of enterprise value creation, this study takes data empowerment as one of the realization forms of enterprise value creation; in addition, this research enriches the application of the resource orchestration model in the big data context. Different from the existing research that mainly focuses on the arrangement of a single resource of data, this study explores the co-arrangement process of data and other resources to give full play to the multiplier effect of data and help enterprises maximize value, thus providing a new research direction for the study of resource orchestration models in the context of data. This study also has instructive significance for the utilization of enterprise data. First, in the process of implementing data empowerment, enterprises should pay attention to the interaction and collaboration of data and other resources. Secondly, enterprises should not put too much emphasis on new business, product and resources, while ignoring the utilization of existing resources and original service, which may also contain enormous power. Finally, in the process of implementing data empowerment, enterprises not only need to focus on creating internal value, but also need to help other participants in the enterprise ecosystem to create external value, so as to achieve value co-creation and win-win cooperation.#br#
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Received: 21 July 2021
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| [1] MCAFEE A，BRYNJOLFSSON E，DAVENPORT T，et al.Big data: the management revolution[J].Harvard business review，2012，90（10）：60-68.[2] 刘启雷,张媛,雷雨嫣,等.数字化赋能企业创新的过程、逻辑及机制研究[J].科学学研究,2022,40(1):150-159.[3] 刘意，谢康，邓弘林.数据驱动的产品研发转型：组织惯例适应性变革视角的案例研究[J].管理世界，2020，36（3）：164-183.[4] 张振刚，肖丹，许明伦.数据赋能对制造业企业绩效的影响：战略柔性的中介作用[J].科技管理研究，2021，41（10）：126-131.[5] 孙新波，苏钟海.数据赋能驱动制造业企业实现敏捷制造案例研究[J].管理科学，2018，31（5）：117-130.[6] LEONG C M，PAN S L，RACTHAM P，et al.ICT-enabled community empowerment in crisis response: social media in Thailand Flooding 2011[J].Journal of the Association for Information Systems，2015，16（3）：174-212.[7] 胡海波，卢海涛.企业商业生态系统演化中价值共创研究——数字化赋能视角[J].经济管理，2018，40（8）：55-71.[8] LENKA S，PARIDA V，WINCENT J.Digitalization capabilities as enablers of value co-creation in servitizing firms[J].Psychology & Marketing，2017，34（1）：92-100.[9] 周文辉，邓伟，陈凌子.基于滴滴出行的平台企业数据赋能促进价值共创过程研究[J].管理学报，2018，15（8）：1110-1119.[10] 赵大伟，景爱萍.数据赋能视角下在线出行服务动态价值共创过程研究[J].商业研究，2019,62（4）：22-30.[11] GUPTA M，GEORGE G.Toward the development of a big data analytics capability[J].Information & Management，2016，53（8）：1049-1064.[12] 杜占河，魏泽龙，谷盟.大数据环境特征对IT外包项目绩效的影响——基于资源编排理论视角[J].科技进步与对策，2017，34（4）：23-30.[13] 刘祎，王玮.工业大数据资源转化为竞争优势的内在机理——基于资源编排理论的案例研究[J].华东经济管理，2019，33（12）：163-170.[14] ZHANG D，PAN S L，YU J，et al.Orchestrating big data analytics capability for sustainability: a study of air pollution management in China[J].Information & Management， 2019：103231.[15] AMIT R，HAN X.Value creation through novel resource configurations in a digitally enabled world[J].Strategic Entrepreneurship Journal，2017，11（3）：228-242.[16] PENROSE E T.The theory of the growth of the firm[M].New York：John Wiley & Sons，1959.[17] SRIVASTAVA A，BARTOL K M，LOCKE E A.Empowering leadership in management teams: effects on knowledge sharing, efficacy, and performance[J].Academy of Management Journal，2006，49（6）：1239-1251.[17] 孙新波，苏钟海，钱雨，等.数据赋能研究现状及未来展望[J].研究与发展管理，2020，32（2）：55-66.[19] 赵振，彭毫.“互联网+”跨界经营——基于价值创造的理论构建[J].科研管理，2018，39（9）：121-133.[20] VIAL G.Understanding digital transformation: a review and a research agenda[J].The Journal of Strategic Information Systems，2019，28（2）：118-144.[21] WAMBA S F，AKTER S，EDWARDS A，et al.How 'big data' can make big impact:findings from a systematic review and a longitudinal case study[J].International Journal of Production Economics，2015，165（7）：234-246.[22] SIRMON D G，HITT M A，IRELAND R D，et al.Resource orchestration to create competitive advantage: breadth，depth，and life cycle effects[J].Journal of Management，2011，37（5）：1390-1412.[23] 杨善林，周开乐.大数据中的管理问题：基于大数据的资源观[J].管理科学学报，2015，18（5）：1-8.[24] 张青，华志兵.资源编排理论及其研究进展述评[J].经济管理，2020，42（9）：193-208.[25] 米莉，苗馨.资源行动演化下动态能力对战略绩效的影响——以亿利集团为例[J].管理案例研究与评论，2021，14（1）：91-110.[26] YIN R K.Case study research: design and methods（5th）[M].London：Sage Publications， 2014.[27] 李亮，刘洋，冯永春.管理案例研究：方法与应用[M].北京：北京大学出版社，2020.[28] 潘善琳，崔丽丽.SPS案例研究方法[M].北京：北京大学出版社，2016.[29] KOTLARSKY J，SCARBROUGH H，OSHRI I.Coordinating expertise across knowledge boundaries in offshore-outsourcing projects[J].MIS Quarterly，2014，38（2）：607-628.[30] CHANIAS S，MYERS M D，HESS T.Digital transformation strategy making in pre-digital organizations:the case of a financial services provider[J].The Journal of Strategic Information Systems，2019，28（1）：17-33. |
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