Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support

Okpala Izunna Udebuana; Ikerionwu Charles

doi:doi:10.11648/j.ijssam.20190402.12

| Peer-Reviewed

Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support

Okpala Izunna Udebuana, Ikerionwu Charles

Published in International Journal of Systems Science and Applied Mathematics (Volume 4, Issue 2)

Received: 21 August 2019 Accepted: 6 September 2019 Published: 24 September 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Strategic decisions positively drive organizational performance and could have a measurable impact on any enterprise. Proper management and resource allocation are relevant to the growth of any organization, and there is an accelerated progression towards a complete overhaul of manual systems leading to the increased proliferation of digital systems. Businesses with less or no computerization create a bridge between users and data, in turn, causes poor decision making, loss of data on transit, time wastage in data extraction, poor data management, improper use of data and erroneous application of organizational data for decision making. This study utilizes information modeling method aimed at studying a decision-making framework and how growth hacking plays a critical role in the implementation of a decision support system for organizational growth. Supporting decision making in a traditional platform consumes time, taking note of the data collection phase, analysis and the choice of alternatives phases but a decision support system digitizes the whole process of data input or extraction, data processing, and the output mechanisms. The paper models the decision-making steps and also suggests that decision-making will take less time in contrast to the use of traditional methods using this growth hacking model. The end product of the implementation of the suggestions from the output stage of this model is growth.

Published in	International Journal of Systems Science and Applied Mathematics (Volume 4, Issue 2)
DOI	10.11648/j.ijssam.20190402.12
Page(s)	24-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Decision-making, Growth-Hacking, Information Modeling, Performance, Decision Support System

References

[1]	Luna, D., Almerares, A., Mayan, J. C., Gonzalez Bernaldo de Quiros, F., & Otero, C. (2014). Health informatics in developing countries: going beyond pilot practices to sustainable implementations: a review of the current challenges. Healthcare informatics research, 20 (1), 3-10.
[2]	Williamson, P. J., Hoenderop, S., & Hoenderop, J. (2018). An alternative benchmark for the validity of China’s GDP growth statistics. Journal of Chinese Economic and Business Studies, 16 (2), 171-191.
[3]	Reese M. (2016) Positives and Negatives of Hacking Retrieved December 02, 2018, from https://prezi.com/qvjezimmrqx6/positives-and-negatives-of-hacking/.
[4]	Nolan 2017 What is Growth Hacking in 2018? What's Next for Digital Marketers? Retrieved June 13, 2018, from https://clemmons.io/what-is-growth-hacking-in-2017/.
[5]	Turban, E., Sharda, R., & Delen, D. (2010). Decision support and business intelligence systems (required).
[6]	Kopáčková, H., & Škrobáčková, M. (2006). Decision support systems or business intelligence: what can help in decision making? Scientific Papers of the University of Pardubice. Series D, Faculty of Economics and Administration, 10.
[7]	Agbonifoh, B. A., Agbadudu, A. B., & Iyayi, F. I. O. (2005). Management, a Nigerian Perspective. Lagos: Malthouse Press Limited.
[8]	Maxime S. (2017). Mark Zuckerberg on Building a Growth Team at Facebook. Retrieved January 15, 2019, from https://growthhackers.com/articles/mark-zuckerberg-on-building-a-growth-team-at-facebook.
[9]	Taylor, G., Burmeister, R., Xu, Z., Singh, B., Patel, A., & Goldstein, T. (2016, June). Training neural networks without gradients: A scalable admm approach. In International Conference on Machine Learning (pp. 2722-2731).
[10]	Holiday, R. (2013). Trust me, i'm lying: confessions of a media manipulator. Penguin.
[11]	Gandhi, J., Chen, A., Dagur, G., Suh, Y., Smith, N., Cali, B., & Khan, S. A. (2016). Genitourinary syndrome of menopause: an overview of clinical manifestations, pathophysiology, etiology, evaluation, and management. American journal of obstetrics and gynecology, 215 (6), 704-711.
[12]	Sarker, A., Ginn, R., Nikfarjam, A., O’Connor, K., Smith, K., Jayaraman, S., & Gonzalez, G. (2015). Utilizing social media data for pharmacovigilance: a review. Journal of biomedical informatics, 54, 202-212.
[13]	Kutcher, E., Nottebohm, O., & Sprague, K. (2014). Grow fast or die slow. McKinsey & Company (http://www.mckinsey.com/Insights/High_Tech_Telecoms_Internet/Grow _fast_or_die_slow).
[14]	Mark Paskin (2006). Lecture note- Introduction to Probability Retrieved from http://ai.stanford.edu/~paskin/gm-short-course/lec1.pdf.
[15]	Marek J. D., Roger R. F. (2005). Decision Support Systems. Retrieved January 16, 2018, from http://www.pitt.edu/~druzdzel/psfiles/dss.pdf.
[16]	Levy, J. S. (2002). Daniel Kahneman: Judgment, decision, and rationality. PS: Political Science & Politics, 35 (2), 271-273.
[17]	Bates, M. J. (2012). Understanding Information Retrieval Systems. Taylor & Francis Group, Boca Raton: USA.
[18]	Gaol, F. L., Kadry, S., Taylor, M., & Li, P. S. (Eds.). (2014). Recent Trends in Social and Behaviour Sciences: Proceedings of the International Congress on Interdisciplinary Behaviour and Social Sciences 2013. CRC Press.
[19]	Simon, H. A. (1960). The new science of management decision.
[20]	Soelberg, P. O. (1967). A study of decision-making: Job choice (Doctoral dissertation, Alfred P. Sloan School of Management, MIT).
[21]	Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018). UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Science, 27 (1), 14-25.
[22]	Kureichik, V., & Safronenkova, I. (2017, September). Ontology-Based Decision Support System for the Choice of Problem-Solving Procedure of Commutation Circuit Partitioning. In Conference on Creativity in Intelligent Technologies and Data Science (pp. 467-478). Springer, Cham.
[23]	Portela, M. C., Pronovost, P. J., Woodcock, T., Carter, P., & Dixon-Woods, M. (2015). How to study improvement interventions: a brief overview of possible study types. BMJ Qual Saf, 24 (5), 325-336.
[24]	Khanolkar D. (2016). What is your Biggest Management information challenge? Some common Business Intelligence challenges faced by companies. Retrieved December 10, 2019, from https://www.linkedin.com/pulse/what-your-biggest-management-information-challenge-some-khanolkar.

Cite This Article

Plain Text BibTeX RIS

APA Style

Okpala Izunna Udebuana, Ikerionwu Charles. (2019). Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. International Journal of Systems Science and Applied Mathematics, 4(2), 24-30. https://doi.org/10.11648/j.ijssam.20190402.12

Copy | Download

ACS Style

Okpala Izunna Udebuana; Ikerionwu Charles. Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. Int. J. Syst. Sci. Appl. Math. 2019, 4(2), 24-30. doi: 10.11648/j.ijssam.20190402.12

Copy | Download

AMA Style

Okpala Izunna Udebuana, Ikerionwu Charles. Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. Int J Syst Sci Appl Math. 2019;4(2):24-30. doi: 10.11648/j.ijssam.20190402.12

Copy | Download

@article{10.11648/j.ijssam.20190402.12,
  author = {Okpala Izunna Udebuana and Ikerionwu Charles},
  title = {Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {4},
  number = {2},
  pages = {24-30},
  doi = {10.11648/j.ijssam.20190402.12},
  url = {https://doi.org/10.11648/j.ijssam.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20190402.12},
  abstract = {Strategic decisions positively drive organizational performance and could have a measurable impact on any enterprise. Proper management and resource allocation are relevant to the growth of any organization, and there is an accelerated progression towards a complete overhaul of manual systems leading to the increased proliferation of digital systems. Businesses with less or no computerization create a bridge between users and data, in turn, causes poor decision making, loss of data on transit, time wastage in data extraction, poor data management, improper use of data and erroneous application of organizational data for decision making. This study utilizes information modeling method aimed at studying a decision-making framework and how growth hacking plays a critical role in the implementation of a decision support system for organizational growth. Supporting decision making in a traditional platform consumes time, taking note of the data collection phase, analysis and the choice of alternatives phases but a decision support system digitizes the whole process of data input or extraction, data processing, and the output mechanisms. The paper models the decision-making steps and also suggests that decision-making will take less time in contrast to the use of traditional methods using this growth hacking model. The end product of the implementation of the suggestions from the output stage of this model is growth.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support
AU  - Okpala Izunna Udebuana
AU  - Ikerionwu Charles
Y1  - 2019/09/24
PY  - 2019
N1  - https://doi.org/10.11648/j.ijssam.20190402.12
DO  - 10.11648/j.ijssam.20190402.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20190402.12
AB  - Strategic decisions positively drive organizational performance and could have a measurable impact on any enterprise. Proper management and resource allocation are relevant to the growth of any organization, and there is an accelerated progression towards a complete overhaul of manual systems leading to the increased proliferation of digital systems. Businesses with less or no computerization create a bridge between users and data, in turn, causes poor decision making, loss of data on transit, time wastage in data extraction, poor data management, improper use of data and erroneous application of organizational data for decision making. This study utilizes information modeling method aimed at studying a decision-making framework and how growth hacking plays a critical role in the implementation of a decision support system for organizational growth. Supporting decision making in a traditional platform consumes time, taking note of the data collection phase, analysis and the choice of alternatives phases but a decision support system digitizes the whole process of data input or extraction, data processing, and the output mechanisms. The paper models the decision-making steps and also suggests that decision-making will take less time in contrast to the use of traditional methods using this growth hacking model. The end product of the implementation of the suggestions from the output stage of this model is growth.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Okpala Izunna Udebuana

Department of Information Technology, Federal University of Technology, Owerri, Nigeria
Ikerionwu Charles

Department of Information Technology, Federal University of Technology, Owerri, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Okpala Izunna Udebuana, Ikerionwu Charles. (2019). Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. International Journal of Systems Science and Applied Mathematics, 4(2), 24-30. https://doi.org/10.11648/j.ijssam.20190402.12

Copy | Download

ACS Style

Okpala Izunna Udebuana; Ikerionwu Charles. Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. Int. J. Syst. Sci. Appl. Math. 2019, 4(2), 24-30. doi: 10.11648/j.ijssam.20190402.12

Copy | Download

AMA Style

Okpala Izunna Udebuana, Ikerionwu Charles. Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support. Int J Syst Sci Appl Math. 2019;4(2):24-30. doi: 10.11648/j.ijssam.20190402.12

Copy | Download

@article{10.11648/j.ijssam.20190402.12,
  author = {Okpala Izunna Udebuana and Ikerionwu Charles},
  title = {Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {4},
  number = {2},
  pages = {24-30},
  doi = {10.11648/j.ijssam.20190402.12},
  url = {https://doi.org/10.11648/j.ijssam.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20190402.12},
  abstract = {Strategic decisions positively drive organizational performance and could have a measurable impact on any enterprise. Proper management and resource allocation are relevant to the growth of any organization, and there is an accelerated progression towards a complete overhaul of manual systems leading to the increased proliferation of digital systems. Businesses with less or no computerization create a bridge between users and data, in turn, causes poor decision making, loss of data on transit, time wastage in data extraction, poor data management, improper use of data and erroneous application of organizational data for decision making. This study utilizes information modeling method aimed at studying a decision-making framework and how growth hacking plays a critical role in the implementation of a decision support system for organizational growth. Supporting decision making in a traditional platform consumes time, taking note of the data collection phase, analysis and the choice of alternatives phases but a decision support system digitizes the whole process of data input or extraction, data processing, and the output mechanisms. The paper models the decision-making steps and also suggests that decision-making will take less time in contrast to the use of traditional methods using this growth hacking model. The end product of the implementation of the suggestions from the output stage of this model is growth.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Decision-Making Framework Using a Growth Hacking Model for Computerized Decision Support
AU  - Okpala Izunna Udebuana
AU  - Ikerionwu Charles
Y1  - 2019/09/24
PY  - 2019
N1  - https://doi.org/10.11648/j.ijssam.20190402.12
DO  - 10.11648/j.ijssam.20190402.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20190402.12
AB  - Strategic decisions positively drive organizational performance and could have a measurable impact on any enterprise. Proper management and resource allocation are relevant to the growth of any organization, and there is an accelerated progression towards a complete overhaul of manual systems leading to the increased proliferation of digital systems. Businesses with less or no computerization create a bridge between users and data, in turn, causes poor decision making, loss of data on transit, time wastage in data extraction, poor data management, improper use of data and erroneous application of organizational data for decision making. This study utilizes information modeling method aimed at studying a decision-making framework and how growth hacking plays a critical role in the implementation of a decision support system for organizational growth. Supporting decision making in a traditional platform consumes time, taking note of the data collection phase, analysis and the choice of alternatives phases but a decision support system digitizes the whole process of data input or extraction, data processing, and the output mechanisms. The paper models the decision-making steps and also suggests that decision-making will take less time in contrast to the use of traditional methods using this growth hacking model. The end product of the implementation of the suggestions from the output stage of this model is growth.
VL  - 4
IS  - 2
ER  -

Copy | Download