Review Jurnal Internasional (3)

 Jurnal 3

BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH

 

Academy of Management Journal

2016, Vol. 59, No. 5, 1493–1507.

http://dx.doi.org/10.5465/amj.2016.4005

 

 

1.      Background (Problem) 

The recent advent of remote sensing, mobile technologies, novel transaction systems, and high performance computing offers opportunities to un- derstand trends, behaviors, and actions in a manner that has not been previously possible. Researchers can thus leverage “big data” that are generated from a plurality of sources including mobile transactions, wearable technologies, social media, ambient net- works, and business transactions. An earlier Academy of Management Journal (AMJ) editorial explored the potential implications for data science in management research and highlighted questions for management scholarship as well as the attendant challenges of data sharing and privacy (George, Haas, & Pentland, 2014). This nascent field is evolving rapidly and at a speed that leaves scholars and practitioners alike attempting to make sense of the emergent opportunities that big data hold. With the promise of big data come questions about the analytical value and thus relevance of these data for theory development—including concerns over the context-specific relevance, its reliability and its validity. To address this challenge, data science is emerging as an interdisciplinary field that combines statistics, data mining, machine learning, and analytics to un- derstand and explain how we can generate analytical insights and prediction models from structured and unstructured big data. Data science emphasizes the systematic study of the organization, properties, and analysis of data and their role in inference, including our confidence inthe inference (Dhar, 2013). Whereas both big data and data science terms are often used interchangeably, “big data” refer to large and varied data that can be collected and managed, whereas “data science” develops models that capture, visual- ize, and analyze the underlying patterns inthe data. In this editorial, we address both the collection and we are grateful for the insightful comments of Kevin Boudreau, Avigdor Gal, John Hollenbeck, Mark Kennedy, and Michel Wedel on earlier versions. Gerry George gratefully acknowledges the financial and research sup- port of the Lee Foundation. handling of big data and the analytical tools provided by data science for management scholars.

At the current time, practitioners suggest that data science applications tackle the three core elements of big data: volume, velocity, and variety (McAfee & Brynjolfsson, 2012; Zikopoulos & Eaton, 2011). “Vol- ume” represents the sheer size of the dataset due to the aggregation of a large number of variables and an even larger set of observations for each variable. “Velocity” reflects the speed at which these data are collected and analyzed, whether in real time or near real time from sensors, sales transactions, social media posts, and sentiment data for breaking news and social trends. “Variety” in big data comes from the plurality of structured and unstructured data sources such as text, videos, networks, and graphics among others. The combinations of volume, velocity, and variety reveal the complex task of generating knowledge from big data, which often runs into millions of observations, and deriving theoretical contributions from such data. In this editorial, we provide a primer or a “starter kit” for potential data science applications in management research. We do so with a caveat that emerging fields outdate and improve upon methodologies while often supplanting them with new applications. Neverthe- less, this primer can guide management scholars who wish to use data science techniques to reach better answers to existing questions or explore completely new research questions.

 

2.      Research Objectives

Big data and data science have potential as new tools for developing management theory, but, given the differences from existing data collection and analytical techniques to which scholars are socialized in doctoral training, it will take more effort and skill in adapting new practices. The current model of management re- search is post hoc analysis, wherein scholars analyze data collected after the temporal occurrence of the event—a manuscript is drafted months or years after the original data are collected. Therefore, velocity, or the real-time applications important for management practice, is not a critical concern for management scholars in the current research paradigm. However, data volume and data variety hold potential for schol- arly research. Particularly, these two elements of data science can be transposed as data scope and data granularity for management research.

 

3.      Research Methodology

When it comes to reporting, one of the key challenges of big data and data science is to be complete. The va- riety of big data makes it important to clearly describe the different data sources that are used. Also, steps taken in preprocessing and merging of the data should be carefully discussed. For example, when working with web traffic data, the researcher should indicate how long a visitor needs to stay on a webpage for the visit to be counted, whether a re-visit to a webpage within a certain time frame is counted as an additional visit or not, and so on. Similarly, when a program is used to rate the degree of positive sentiment in a news item, the researcher needs to be clear as to which words are searched for. Loughran and McDonald (2011) demonstrated the importance of context. For exam- ple, the words cost and liability may express negative sentiment in some settings, whereas they are more neutrally used in financial texts. Also, it should be clear to the reader whether weights are applied, and which other decisions have been made in preparing the data for analysis (e.g., Haas et al., 2015).

With regard to data analysis, statistical signifi- cance becomes less meaningful when working with big data. Even variables that have a small effect on the dependent variable will be significant if the sample size is large enough. Moreover, spurious correlations are likely when considering a large number of vari- ables. One should therefore, in addition to statistical significance, focus on the effect size of a variable and on its out-of-sample performance. Also, it is impor- tant to note that traditional statistical concepts apply to situations wherein a sample of the population is analyzed, whereas big data may capture the entire population. Bayesian statistical inference may pro- vide a solution (Wedel & Kannan, 2016) as it assumes the data to be fixed and the parameters to be random—unlike the frequentist approach, which assumes that the data can be resampled. That being said, it may also be the case that, despite having big data, researchers may ultimately aggregate observa- tions so that sample size decreases dramatically and statistical significance remains an important issue. This might occur, for example, because one’s theory is not about explaining millisecond-to-millisecond changes in phenomena such as heart rate, but, rather, about explaining differences between individuals in their overall health and well-being.

When applying methods for variable selection, it is important to describe the method used and, partic- ularly, how the model was tuned. Since different approaches may give different results, it is highly advised to try multiple approaches to show robust- ness of the findings. In theory testing, it is clear which variables to use. However, these variables may often be operationalized in different ways. Also, the num- ber and way of including control variables may be open for discussion. Simonsohn and colleagues (2015) advised the estimation of all theoretically jus- tified model specifications to demonstrate robustness of the results across specifications. Finally, researchers may consider visualizing pat- terns in the data to give the audience a better feel for the strength of the effects, to allow easy comparison of effects, and to show that the effects are not an artifact of the complicated model that was used to obtain them. With the rise of data science, many new visualization tools have become available (e.g., Bime, Qlik Sense, and Tableau) that allow for easy application of multi- ple selections and visualization for large datasets.

 

4.      Conclusion

In this editorial, we discussed the applications of data science in management research. We see op- portunities for scholars to develop better answers to existing theories and extend to new questions by embracing the data scope and granularity that big data provide. Our starter kit explains the key challenges of data collection, storage, processing and analysis, and reporting and visualization—which represent departures from existing methods and paradigms. We repeat our caveat that the field is evolving rapidly with business practices and com- puting technologies. Even so, our editorial provides the novice with the basic elements required to ex- periment with data science techniques. A few de- cades ago, the emergence of commercial databases, such as Compustat and SDC, and the availability of advanced analytics packages, such as STATA and UCINET, revolutionized management research by enabling scholars to shift from case studies and simple two-by-two frameworks to complex models that leverage rich archival data. The advent of data science can be the next phase in this evolution, which offers opportunities not only for refining established perspectives and enhancing the accu- racy of known empirical results, but also for embarking into novel research domains, raising new types of research questions, adopting more refined units of analysis, and shedding new light on the mechanisms that drive observed effects. Whereas data science applications are becoming pervasive in marketing and organizational behavior research, strategy scholars have yet to harness these powerful tools and techniques. Data science applications in management will take significant effort to craft, re- fine, and perfect. With a new generation of re- searchers evincing interest in these emergent areas, and the doctoral research training being provided, management scholarship is getting ready for its next generational leap forward.

Nama : Ulfha Lestari

NIM : 19.01.051.029

Dosen Pengampu : Nawassyarif, S.Kom.,M.Pd.

Program Studi Ilmu Komunikasi

Fakultas Ilmu Komunikasi

Universitas Teknologi Sumbawa

www.uts.ac.id