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Realizing that data driven decisions have been rapidly growing in the last few years as an essential segment of business intelligence, researchers have vividly found interest in devling more deeply into this field. Data driven decisions and digital asset management have been addressed by savvy researchers in a well-integrated theoretical framework from psychological, managerial and operational magnitudes, evaluating the effect of these dimensions on decisions and deliberately dissecting the process of collecting, storing, processing, analyzing and utilizing data. Data driven decisions have markedly enhanced the quality of decisions by buttressing the decision making mechanisms and elevating their efficiency, substituting the intuition based approach that suffers from exacerbating structural and operational problems.
Empirically examining data driven decisions and quantitatively assessing the impact of such tool have become inescapable. The recent evidence from both business and information technology management literature and business oriented non-academic papers, have been increasingly found interest in tackling the digital asset management in a quantifiable approach. More specifically, scrutinizing the data driven decisions and the new digital asset management tools is actually a manifestation of collecting, handling and storing and processing data in a proficient manner. Furthermore, the variable “not having data trained staff” is consistent with what have been written and discussed in the literature as a potential cause of several aggravating difficulties in the market economy; this is largely due to the shortage of possessing data oriented tools by decisions makers and employees (Streifer and Goens, 2004).
Efficiently utilizing data and the latest digital asset management necessitates the use of these tools by companies and firms planning to expand in the contemporary market. This is in line with what Leibowitz (2013) maintains regarding the transformative role of statistical records in regards to the decision making process. This chapter attempts to discuss the empirical findings of the paper, citing and integrating the results of the literature, making sure that our findings are being built on solid theoretical and empirical grounds.
Quantitative data have been introduced in any essential stage of decision making processes, proving the effectiveness and reliability of such methods. The literature is full of these studies, such as that ofLiebowitz’s (2013), who argues that matching historical records with our current status quo gives humans a psychological comfortableness in all types of decision making situations. In fact, well-constructed models are analogous to the way our brains function. Therefore, using data driven decision making approaches is like emulating the way our human brain functions.
Thus, this section aims at empirically unfolding the effect of data driven decisions and the impact of digital asset management on company’s performance by meticulously extracting the results from our regression models and statistical tests, organizing the concepts and ideas from the literature that are related to our significant variables and drawing relevant policy implications. It is worth noting that this empirical approach adopted in this model is the Ordinary Least Square (OLS) model through the STATA software. STATA has been primarily used to run regressions, hypothesizing the two models at hand and providing several statistical tests, such as the T test, the robustness test and the test of variance.
These statistical tools have been introduced to validate the robustness of the empirical approach. The models address the significance of data driven decisions and their impact on firms’ performance and subsequent managerial and non-managerial decisions.
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