Incorporating big data analytics into a particular context brings various challenges that rest on the model or framework through which individuals or organisations adopt big data to achieve their objectives. Although these models have recently triggered scholars’ attention in various domains, in-depth knowledge of using each of these models in big data research is still blurred. This study enriches our knowledge on emerging models and theories that shape big data analytics adoption (BDAD) research through a bibliometric analysis of 229 studies (143 journal articles and 86 conference papers) published in indexed sources between 2013 and 2019. As a result, twenty models on BDAD have emerged (e.g., “Dynamic Capabilities”, “Resource-Based View”, “Technology Acceptance Model”, “Diffusion of Innovation”, etc.). The analysis reveals that BDAD research to demonstrate attributes suggestive of a topic at an initial stage of development as it is broadly dispersed across different domains employs a wide range of models, some of which overlap. Most of the applied models are generic in nature focusing on variance-based relationships and snapshot prediction with little consensus. There is a conspicuous dearth of process models, firm-level analysis and cultural orientation in contemporary BDAD research. Insights of this bibliometric study could guide rigorous big data research and practice in various contexts. The study concludes with research implications and limitations that offer promising prospects for forthcoming research.

Influencing models and determinants in big data analytics research: A bibliometric analysis

The proliferation, ubiquity, and growth of data, big data, and digital infrastructure raise a number of questions for library and information studies (LIS) practitioners, researchers, and educators. While some uncritically accept and embrace the idea that big data will fundamentally alter every sector of society including economics, politics, health care, and knowledge production, others are more critical of the data turn. Data can be contradictory in that it can be used for surveillance, impinge on privacy, be used for secondary purposes (often without consent), and can be totalizing in that we continually create data exhaust, it can be hacked, searched, aggregated, and preserved for years. Conversely, data can be used for the public good, to promote progressive social change, and to empower people. The overarching argument presented in this paper is that critical library and information studies must include critical data studies. To develop this argument, this paper explores the ontological nature of data and their contradictory implications and effects in terms of broader society, the academy, and in LIS research, education, and practice. Next, the philosophical foundations and the work being done in the budding area of critical data studies are presented (most notably work by Rob Kitchin). Finally, the intersections between critical data studies and LIS are discussed in terms of research methodologies, philosophical underpinnings, and application of critical social theory, values, and ethics using Dalton and Thatcher’s seven data criticisms.

/pdf/a-case-for-critical-data-studies-in-library-and-information-2ukid3qom1.pdf

A Case for Critical Data Studies in Library and Information Studies

Big data is a catch term for the massive amount of data collected continually through individual interaction with ubiquitous technology and the multitude of processes and procedures running in the background to support and record today's society. Some have asked if the size of the data collected, and our continual ability to parse and mine large datasets for meaningful information, change the social contract. This question is addressed by presenting a review of social contract theory and by incorporating aspects of Anthony Giddens's structuration theory and John Rawls's concept of justice.The Social Contract and Big DataIt has been asked, in academic as well as business circles, if the influx of big data and its use change the social contract (Chesterman, 20ii; Peters, 20i2). One reason this question is pertinent today, according to Chesterman (20ii), is that the idea of a social contract was once based specifically on the relationship between the governed and the governor. Today, however, commercial interests and the data collection made possible via individual use of technology influence government decision-making. Definitions of big data most frequently include the 3Vs-volume, variety, and velocity. These are the challenges that researchers as well as organizations face when managing large data sets. The term big data was traced to a paper that appeared in i997 written by NASA scientists, Michael Cox and David Ellsworth (Press, 20i4). In it they discuss the problem of creating computer visualizations of large data sets, using then current graphics systems:Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources [Cox & Ellsworth, i997, p. 235].For purposes of this article the definition provided in the Merriam-Webster Online Dictionary will be used: "an accumulation of data that is too large and complex for processing by traditional database management tools" ("Big data," n.d.). According to Chesterman (20ii), trust is a casualty of this increased ability to know more than ever before, about each of us. Because of this, he asserts that the social contract is no longer sufficient, and needs to be redefined. From a business perspective, Forbes journalist Brad Peters is quoted as claiming that big data "changes the social contract" (Davis, 20i2, p. 28). Peters goes on to state that, because of the mass personalization that is a driving force behind much commerce today, trust is ever more important (Davis, 20i2). The consumer or user of an online service must trust the company or government agency enough to continue to use the service. The individual must believe that the advertising being directed at him/her is relevant, that the data collected during their digital transactions, and the algorithms created to make sense of it, are being used justly. Trust is one of the concepts that undergirds any type of contract (Malhotra, 2009).In today's world of ubiquitous technology and big data, trust is a necessary and foundational principle for the protection of personal information. Since we cannot be exactly sure how (or by whom) the data traces we leave behind may be used, it is especially important that we can trust that the process by which the policies are designed is transparent. Whether data is being used for our consumerist benefit or for national security it is important that we can trust the intentions of the user. A new type of social contract need not be based on the assumption that data collection and its consequent use by the government and commercial entities leads to unwarranted laying lives bare. Rather, we need to consider how values such as trust, privacy, and justice can (re)inform the social contract in order to support these principles. …

The Social Contract and Big Data/Response to Unsworth

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The Social Contract and Big Data/Response to Unsworth