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Big-Data Readiness of Four-Year Publ...

Molina, Hector M.

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Big-Data Readiness of Four-Year Public and Private North Carolina Higher Education Institutions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big-Data Readiness of Four-Year Public and Private North Carolina Higher Education Institutions./
Author:	Molina, Hector M.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	128 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-06, Section: A.
Contained By:	Dissertations Abstracts International80-06A.
Subject:	Higher Education Administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13423255
ISBN:	9780438752641

Big-Data Readiness of Four-Year Public and Private North Carolina Higher Education Institutions.
Molina, Hector M.

Big-Data Readiness of Four-Year Public and Private North Carolina Higher Education Institutions. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 128 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: A.

Thesis (D.B.A.)--Wilmington University (Delaware), 2019.

This item must not be added to any third party search indexes.

This study seeks to understand the big-data analytics readiness of four-year public and private higher education institutions (HEIs) in North Carolina. The higher education landscape has been experiencing unprecedented challenges including declines in enrollment, graduation rates, and student retention rates. Coupled with cuts in funding at the state level, executive leaders in HEIs in North Carolina and nationally have found it difficult to effectively address these challenges due to the highly competitive and dynamic education environment. However, like many industries, the higher higher-education sector is rapidly changing as a result of technological advancements. The use of big-data analytics has been identified as a potential solution to challenges experienced in the sector. Yet, the adoption of big-data analytics is still in its early stages. This quantitative cross-sectional study utilizes the DELTTA model framework, which is comprised of six elements: data, enterprise, leadership, targets, technology, and analysts. Each element aids in assessing whether organizations are ready to effectively utilize big-data analytics. A structured questionnaire, the Big Data Readiness Assessment, was used to assess big-data analytics adoption and readiness. It was hypothesized that readiness would differ among the six elements, and that there would be differences based on institutional control and size of the HEIs in North Carolina. No significant differences in readiness were found among the six elements for the set of HEIs in the study. However, sentiment analysis of the data aided in identifying the areas where respondents felt that their institutions were leading or lagging in terms of big-data readiness. The findings also revealed that big-data analytics readiness was perceived to be higher in public HEIs than their private counterparts, and the perceived readiness was more advanced in medium and large HEIs compared to small HEIs.

ISBN: 9780438752641Subjects--Topical Terms:

3432472
Higher Education Administration.

Big-Data Readiness of Four-Year Public and Private North Carolina Higher Education Institutions.
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500 $a Publisher info.: Dissertation/Thesis.
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520 $a This study seeks to understand the big-data analytics readiness of four-year public and private higher education institutions (HEIs) in North Carolina. The higher education landscape has been experiencing unprecedented challenges including declines in enrollment, graduation rates, and student retention rates. Coupled with cuts in funding at the state level, executive leaders in HEIs in North Carolina and nationally have found it difficult to effectively address these challenges due to the highly competitive and dynamic education environment. However, like many industries, the higher higher-education sector is rapidly changing as a result of technological advancements. The use of big-data analytics has been identified as a potential solution to challenges experienced in the sector. Yet, the adoption of big-data analytics is still in its early stages. This quantitative cross-sectional study utilizes the DELTTA model framework, which is comprised of six elements: data, enterprise, leadership, targets, technology, and analysts. Each element aids in assessing whether organizations are ready to effectively utilize big-data analytics. A structured questionnaire, the Big Data Readiness Assessment, was used to assess big-data analytics adoption and readiness. It was hypothesized that readiness would differ among the six elements, and that there would be differences based on institutional control and size of the HEIs in North Carolina. No significant differences in readiness were found among the six elements for the set of HEIs in the study. However, sentiment analysis of the data aided in identifying the areas where respondents felt that their institutions were leading or lagging in terms of big-data readiness. The findings also revealed that big-data analytics readiness was perceived to be higher in public HEIs than their private counterparts, and the perceived readiness was more advanced in medium and large HEIs compared to small HEIs.
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