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经济代写|供应链管理代写supply chain management代考|A QUALITATIVE APPROACH

The interviews with US-based IT managers involved asking them about their hiring processes and were designed to understand how they approached the challenge of meeting their big data initiatives in lieu of a shortage of qualified big data specialists. We know there are data scientists employed. Shan, Wang, Chen, and Song (2015) conducted interviews with data scientists who work for businesses such as LinkedIn ${ }^{\mathrm{TM}}$, Yahoo! $\mathrm{TM}$, Uber ${ }^{\mathrm{TM}}$, and others. Is there a standard, most successful manner to fill these positions? Has the lack of available qualified big data specialists forced IT Managers to find innovative ways to get the value of big data for their respective businesses? What exactly has been done to fill these roles? The research was limited to the United States. What processes are US-based business leaders taking to meet the big data demands of their businesses? What quality criteria are business leaders in the United States considering in big data specialist candidates?

IT Managers are the people in organizations most commonly tasked with filling technology-centric roles. Because of this, they were the best suited, considering their efforts in finding resources to fill those roles. In the event they could not find a person to fill the role, they would be responsible for attempting other methods to meet the big data demands, such as outsourcing to consultants, sending existing employees to be trained, identifying multiple people due to not finding one with all of the skills, etc. While “IT Manager” is the name being used throughout the research, that title may be either a specific or a general term for the person responsible for filling IT-related positions. In some businesses, this may be the Vice President of IT, Director of IT, Information Systems Lead, etc. The choice of this was me asking myself, “Self, who would best know the big data resource hiring process?”

经济代写|供应链管理代写supply chain management代考|DEPENDABILITY AND TRUSTWORTHINESS

Trustworthiness in qualitative research, according to Graneheim and Lundman (2004), consists of credible, dependable, and transferable findings. Credibility addresses the focus of the research study and pertains to confidence in how appropriate the data and analysis processes were in addressing the study’s purpose (Graneheim \& Lundman, 2004; Thomas \& Magilvy, 2011). Dependability refers to the stability and consistency of the data and whether changes made in the data collection or analysis of it exist due to some factors or phenomena (Graneheim \& Lundman, 2004). Transferability is explained by Thomas and Magilvy (2011) as research findings being able to be carried over to another group or environment. It is argued that it is up to the reader to determine if they believe the results are transferrable or not (Graneheim \& Lundman, 2004).

Triangulation in the form of comparing the different data and/or analysis methods was one method used to maximize the trustworthiness of the data analysis. Member checking was applied for this purpose as well. Member checking, according to Thomas and Magilvy (2011), requires ensuring with the subjects that the interpretations made by the researcher are accurate representations of the participant’s experiences. Each step in the data collection, coding, and analysis was explained in detail to increase the dependability of the study results. Each technique was communicated to justify the reason the researcher believed it was required and to increase the study’s credibility. This type of reflexivity allows the readers to understand how and why the researcher interpreted the data and see the “big picture” of the study (Thomas \& Magilvy, 2011, p. 154).

经济代写|供应链管理代写supply chain management代考|CUS522

经济代写|供应链管理代写供应链管理代考|定性方法


对美国IT经理的面试包括询问他们的招聘流程,旨在了解他们如何应对大数据计划的挑战,以取代合格大数据专家的短缺。我们知道有数据科学家被雇佣。Shan、Wang、Chen和Song(2015)对供职于LinkedIn ${ }^{\mathrm{TM}}$、Yahoo!$\mathrm{TM}$, Uber ${ }^{\mathrm{TM}}$等等。有没有一个标准的、最成功的方式来填补这些职位?缺乏合格的大数据专家是否迫使IT经理寻找创新的方式,为各自的业务获取大数据的价值?到底做了什么来填补这些角色?这项研究仅限于美国。美国的商业领袖们在满足其业务的大数据需求方面采取了哪些步骤?美国的商业领袖在挑选大数据专家时,会考虑什么样的素质标准?


IT经理是组织中最常被赋予以技术为中心的角色的人。正因为如此,考虑到他们在寻找填补这些角色的资源方面所做的努力,他们是最合适的人选。如果他们找不到合适的人来填补这个职位,他们将负责尝试其他方法来满足大数据的需求,比如外包给顾问,把现有员工送去培训,因为找不到一个具备所有技能的人而识别多个人,等等。虽然“IT经理”是在整个研究过程中使用的名称,但该头衔可能是负责填补IT相关职位的人的一个具体或笼统的术语。在一些企业中,这可能是IT副总裁、IT总监、信息系统主管等。我的选择是问我自己:“Self,谁最了解大数据资源的招聘过程?”

经济代写|供应链管理代写供应链管理代考|可靠性和可信赖性


根据Graneheim和Lundman(2004),质性研究中的可信度由可信的、可靠的和可转移的发现组成。可信度解决了研究研究的重点,并涉及到数据和分析过程在解决研究目的方面的适当程度的信心(Graneheim &伦德曼,2004年;托马斯&Magilvy, 2011)。可靠性是指数据的稳定性和一致性,以及数据收集或分析过程中是否由于某些因素或现象而发生变化(Graneheim &伦德曼,2004)。可移动性被Thomas和Magilvy(2011)解释为研究结果能够转移到另一个群体或环境中。有人认为,这取决于读者来决定他们是否相信结果是可转移的(Graneheim &Lundman, 2004)。


以比较不同数据和/或分析方法的形式进行三角测量是一种最大化数据分析可信度的方法。成员检查也适用于此目的。根据Thomas和Magilvy(2011)的观点,成员检查需要向被试确保研究者所做的解释是被试经验的准确表述。详细解释了数据收集、编码和分析的每一个步骤,以增加研究结果的可靠性。每种技术的交流都是为了证明研究人员认为需要它的原因,并增加研究的可信度。这种反身性可以让读者理解研究者如何以及为什么解释数据,并看到研究的“大局”(Thomas &Magilvy, 2011, p. 154)。

经济代写|供应链管理代写supply chain management代考

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