Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise
Abstract
:1. Introduction
- Inevitable investment expenses and escalating operating expenditures.
- Unrealized returns via emerging immature technology.
- Inconsistency with strategic digital direction.
- Unmanageable external market turbulence control.
2. Technology and Behavior Model Direction Literature
2.1. Technology Acceptance Model
2.2. Theory of Planned Behaviour
3. Methodology
- ➢
- z is the Z-Score (According to the query, the Z-Score of 90% is 1.8
- ➢
- ε is the margin of error (ensure the accuracy of sample quantity is set to the minimum value of 0.05)
- ➢
- n is the population size
- ➢
- p is the population fraction
4. Results and Findings
5. Conclusions and Limitation
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- SMECorp. 2020. Available online: https://www.smecorp.gov.my/images/SMEAR/SMEAR2018_2019/final/english/SME%20AR%20%20English%2020All%20Chapter%20Final%2024Jan2020.pdf (accessed on 18 February 2020).
- Guo, Z.; Li, J.; Ramesh, R. Optimal Management of Virtual Infrastructure Under Flexible Cloud Service Agreement. Inf. Syst. Res. 2019, 30, 1424–1446. [Google Scholar] [CrossRef]
- Rao, J.J.; Kumar, V. Technology Adoption in the SME Sector for Promoting Agile Manufacturing Practices. In Smart Intelligent Computing and Applications; Smart Innovation, Systems and Technologies; Satapathy, S., Bhateja, V., Das, S., Eds.; Springer: Singapore, 2019; p. 105. [Google Scholar]
- Jung, H.; Hwang, J.T.; Kim, B.K. Does R&D investment increase SME survival during a recession. Technol. Forecast. Soc. Change 2018, 137, 190–198. [Google Scholar]
- Zhang, D.; Nault, B.R.; Wei, X. The strategic value of information technology in setting productive capacity. Inf. Syst. Res. 2019, 30, 1124–1144. [Google Scholar] [CrossRef]
- Benitez, J.; Ray, G.; Henseler, J. Impact of information technology infrastructure flexibility on mergers and acquisitions. MIS Q. 2018, 42, 25–43. [Google Scholar] [CrossRef]
- Valacich, J.S.; Wang, X.; Jessup, L.M. Did I Buy the Wrong Gadget? How the Evaluability of Technology Features Influences Technology Feature Preferences and Subsequent Product Choice. MIS Q. 2018, 42, 633–644. [Google Scholar] [CrossRef]
- Banares, J.A.; Altmann, J. Economics behind ICT infrastructure management. Electron. Mark. 2018, 28, 7–9. [Google Scholar] [CrossRef] [Green Version]
- Urbach, N.; Ahlemann, F. Infrastructure as Commodity: IT Infrastructure Services are Traded on Free Markets and Purchased as Required. In IT Management in the Digital Age; Springer: Berlin/Heidelberg, Germany, 2016; pp. 75–84. [Google Scholar]
- Furstenau, D.; Baiyere, A.; Kliewer, N. A dynamic model of embeddedness in digital infrastructure. Inf. Syst. Res. 2019, 30, 1319–1342. [Google Scholar] [CrossRef]
- Mesgari, M.; Okoli, C. Critical review of organization-technology sensemaking: Towards technology materiality. discovery and action. Eur. J. Inf. Syst. 2019, 28, 205–232. [Google Scholar] [CrossRef]
- Jahantigh, F.F. A conceptual framework for business intelligence critical success factors. Int. J. Bus. Inf. Syst. 2019, 30, 109–123. [Google Scholar] [CrossRef]
- Narayanan, V.K. Radical cost innovation. Strategy Leadersh. 2019, 47, 53–54. [Google Scholar] [CrossRef] [Green Version]
- Chauhan, S.; Jaiswal, M.; Rai, S.; Motiwalla, L.; Pipino, L. Determinants of adoption for open-source office applications: A plural investigation. Inf. Syst. Manag. 2018, 35, 80–97. [Google Scholar] [CrossRef]
- Clinton, V. Savings without sacrifice: A case report on open-source textbook adoption. Open Learn. J. Open Distance e-Learn. 2018, 33, 177–189. [Google Scholar] [CrossRef]
- Garin, A.M.; Garcia, J.A.M.; Medel, J.M.; Lizarraga, J.M. Environmental monitoring system based on an Open Source Platform and Internet of Things for building energy retrofit. Autom. Constr. 2018, 87, 201–214. [Google Scholar] [CrossRef]
- Olson, D.L. Open source ERP business model framework. Robot. Comput. Integr. Manuf. 2018, 50, 30–36. [Google Scholar] [CrossRef]
- Alkhanak, E.N.; Lee, S.P.; Rezaei, R.; Parizi, R.M. Cost optimization approaches for scientific workflow scheduling in the cloud and grid computing: A review, classifications, and open issues. J. Syst. Softw. 2016, 113, 1–26. [Google Scholar] [CrossRef]
- Ooi, K.B.; Lew, S.; Tan, G.W.H.; Loh, X.M.; Hew, J.J. The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technol. Soc. 2020, 63, 101430. [Google Scholar] [CrossRef]
- Wei-Han Tan, G.; Ooi, K.B.; Yuan, Y.P.; Lim, W.L. Can the COVID-19 pandemic influence experience response in mobile learning? Telemat. Inform. 2021, 64, 101676. [Google Scholar] [CrossRef]
- Yousif, A.; Alqhtani, S.M.; Bashir, M.B.; Ali, A.; Hamza, R.; Hassan, A.; Tawfeeg, T.M. Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing. Sensors 2022, 22, 850. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–339. [Google Scholar] [CrossRef] [Green Version]
- Ajzen, I. The theory of planned behaviour: Frequently asked questions. Hum. Behav. Emerg. Technol. 2020, 2, 314–324. [Google Scholar] [CrossRef]
- Anderson, D.P. BOINC: A Platform for Volunteer Computing, Computer Science; Cornell University Press: Ithaca, NY, USA, 2018; pp. 1–37. [Google Scholar]
- Anderson, D.P. BOINC: A platform for volunteer computing. J. Grid Comput. 2020, 18, 99–122. [Google Scholar] [CrossRef]
- Venkatesh, V.; Brown, S.A.; Sullivan, Y.W. Guidelines for Conducting Mixed-methods Research: An Extension and Illustration. J. Assoc. Inf. Syst. 2016, 17, 435–495. [Google Scholar] [CrossRef] [Green Version]
- Zhang, K.; Alasmari, T. Mobile learning technology acceptance in Saudi Arabian high education: An extended framework and a mixed-method study. Educ. Inf. Technol. 2019, 24, 2127–2144. [Google Scholar]
- Lin, T.T.C. Multiscreen Social TV System: A mixed-method understanding of users; attitude and adoption intention. Int. J. Hum. -Comput. Interact. 2018, 35, 99–108. [Google Scholar] [CrossRef]
- Creswell, J.W. Research Designs: Qualitative, Quantitative and Mixed Methods Approach, 5th ed.; Sage Publication: Thousand Oaks, CA, USA, 2018. [Google Scholar]
- JosephNg, P.S.; Loh, Y.F.; Eaw, H.C. Grid Computing for “MSE” during Volatile Economy. In Proceedings of the International Conference on Control, Automation and Systems, IEEE Explore, Busan, Republic of Korea, 13–16 October 2022; pp. 709–714. [Google Scholar]
- Soon, J.P.; Moy, K.C.; Mahmood, A.K.; Wan, W.S.; Yuen, P.K.; Hui, S.S.; Theam, L.J. EaaS: Available yet Hidden Infrastructure inside “MSE”. In Proceedings of the 5th International Conference on Network, Communication and Computing, ACM International Conference Proceeding Series, Kyoto, Japan, 17–21 December 2016; pp. 17–20. [Google Scholar]
- Soon, J.; Wan, W.; Yuen, P.; Heng, L.; Theam, L.; Wei, L. BareBone cloud IaaS: Revitalization disruptive technology. In Proceedings of the 2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), Penang, Malaysia, 7–8 April 2014; pp. 49–54. [Google Scholar] [CrossRef]
- JosephNg, P.S.; Kang, C.M.; Choo, P.Y.; Wong, S.W.; Phan, K.Y.; Lim, E.H. Beyond cloud infrastructure services in medium size manufacturing. In Proceedings of the International Symposium on Mathematical Sciences & Computing Research, Ipoh, Malaysia, 19–20 May 2015; pp. 150–155. [Google Scholar]
- Joseph, N.P.; Choo, P.; Wong, S.; Phan, K.; Lim, E. Malaysia SME ICT During Economic Turbulence. In Proceedings of the International Conference on Information & Computer Network, Singapore, 29–31 December 2012; pp. 67–71. [Google Scholar]
- Soon, N.P.; Yin, C.; Wan, W.; Nazmudeen, M. Energizing ICT Infrastructure for Malaysia SME during Economic Turbulence. In Proceedings of the Student Conference on Research and Development, Cyberjaya, Malaysia, 19–20 December 2011; IEEE Explore: Berlin, Germany, 2011; pp. 322–328. [Google Scholar]
- JosephNg, P.S.; Eaw, H.C. Making financial sense from EaaS for “MSE” during economic uncertainty. Adv. Intell. Syst. Comput. 2021, 1, 976–989. [Google Scholar]
- JosephNg, P. Economic Turbulence and EaaS Grid Computing. Preprints 2021, 2021090329. [Google Scholar] [CrossRef]
- JosephNg, P.S. EaaS Infrastructure Disruptor for “MSE”. Int. J. Bus. Inf. Syst. 2019, 30, 373–385. [Google Scholar] [CrossRef]
- JosephNg, P.S. EaaS Optimization: Available yet hidden information technology infrastructure inside Medium Size Enterprise. J. Technol. Forecast. Soc. Change 2018, 132, 165–173. [Google Scholar] [CrossRef]
- Soon, J.P.; Moy, K.C. Beyond barebone cloud infrastructure services: Stumbling competitiveness during economic turbulence. J. Sci. Technol. 2016, 24, 101–121. [Google Scholar]
- Soon, J.N.P.; Wan, W.S.; Yuean, P.K.; Heng, L.J. Barebone Cloud IaaS: Revitalisation Disruptive Technology. Int. J. Bus. Inf. Syst. 2015, 18, 107–126. [Google Scholar] [CrossRef]
- Joseph, N.P.S.; Mahmood, A.K.; Choo, P.Y.; Wong, S.W.; Phan, K.Y.; Lim, E.H. IaaS Cloud Optimization during Economic Turbulence for Malaysia Small and Medium Enterprise. Int. J. Bus. Inf. Syst. 2014, 16, 196–208. [Google Scholar]
- Joseph, N.P.S.; Mahmood, A.K.; Choo, P.Y.; Wong, S.W.; Phan, K.Y.; Lim, E.H. Battles in volatile information and communication technology landscape: The Malaysia small and medium enterprise case. Int. J. Bus. Inf. Syst. 2013, 13, 217–234. [Google Scholar] [CrossRef]
- Joseph, N.P.S.; Kang, C.M.; Mahmood, A.K.; Choo, P.Y.; Wong, S.W.; Phan, K.Y.; Lim, E.H. Exostructure Services for Infrastructure Resources Optimization. J. Telecommun. Electron. Comput. Eng. 2016, 8, 65–69. [Google Scholar]
- Joseph, N.P.S.; Choo, P.; Wong, S.; Phan, K.; Lim, E. Hibernating ICT Infrastructure During Rainy Days. J. Emerg. Trends Comput. Inf. Sci. 2012, 3, 112–116. [Google Scholar]
- Yuriev, A.; Dahmen, M.; Paillé, P.; Boiral, O.; Guillaumie, L. Pro-environmental behaviours through the lens of the theory of planned behaviour: A scoping review. Resour. Conserv. Recycl. 2020, 155, 104660. [Google Scholar] [CrossRef]
- Kamal, S.A.; Shafiq, M.; Kakria, P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol. Soc. 2020, 60, 101212. [Google Scholar] [CrossRef]
- Basco, R.; Hair, J.F.; Ringle, C.M.; Sarstedt, M. Advancing family business research through modelling nonlinear relationships: Comparing PLS-SEM and multiple regression. J. Fam. Bus. Strategy 2021, 13, 1–16. [Google Scholar] [CrossRef]
- Khan, Z.H. Exploring Strategies that IT Leaders Use to Adopt Cloud Computing. Ph.D. Thesis, Walden University, Minneapolis, MN, USA, 2016. [Google Scholar]
- Bergmann, M.; Brück, C.; Knauer, T.; Schwering, A. Digitization of the budgeting process: Determinants of the use of business analytics and its effect on satisfaction with the budgeting process. J. Manag. Control 2020, 31, 25–54. [Google Scholar] [CrossRef] [Green Version]
- Anwar, N.; Masrek, M.N.; Sani, M.K.J.; Mohamad, A.N. The proof of concept on the determinants of strategic utilization of information systems. Int. Inf. Inst. 2016, 19, 27–55. [Google Scholar]
- Asad, M.; Sharif, M.N.M.; Alekam, J.M. Moderating effect of entrepreneurial networking on the relationship between access to finance and performance of micro and small enterprises. Paradig. Res. J. 2016, 10, 1–13. [Google Scholar] [CrossRef]
- Veiga, A.D. A cybersecurity culture research philosophy and approach to developing a valid and reliable measuring instrument. In Proceedings of the SAI Computing Conference 2016, London, UK, 13–15 July 2016. [Google Scholar]
- Kante, M.; Chepken, C.; Oboko, R. Effects of Farmers’ Peer Influence on the Use of ICT-based Farm, Input Information in Developing Countries: A Case in Sikasso, Mali. J. Digit. Media Interact. 2018, 1, 99–116. [Google Scholar]
- Flowers, E.; Freeman, P.; Flowers, E.P.; Freeman, P.; Gladwell, V.F. A cross-sectional study examining predictors of visit frequency to local green space and the impact this has on physical. BMC 2016, 16, 420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tsai, H.; LaRose, R. Broadband Internet adoption and utilization in the inner city: A comparison of competing theories. Comput. Hum. Behav. 2015, 51, 344–355. [Google Scholar] [CrossRef]
- da Silveira, B.; Vasconcellos, E.; Guedes, L.V.; Guedes, L.F.A.; Costa, R.M. Technology Road Mapping: A methodological proposition to refine Delphi results. Technol. Forecast. Soc. Chang. 2018, 126, 194–206. [Google Scholar]
- Wang, I.K.; Seidle, R. The degree of technological innovation: A demand heterogeneity perspective. Technol. Forecast. Soc. Change 2017, 125, 166–177. [Google Scholar] [CrossRef]
- Udo, E.N.; Akwukwuma, V.V.N. Software adaptability metrics model using ordinary logistic regression. J. Softw. 2019, 14, 116–128. [Google Scholar]
- Ho, K.L.P.; Nguyen, C.N.; Adhikari, R.; Miles, M.P.; Bonney, L. Exploring market orientation, innovation and financial performance in agricultural value chains in emerging economies. J. Innov. Knowl. 2018, 3, 154–163. [Google Scholar] [CrossRef]
Research Dimension | Phenomena Explanatory Sequential Dimension | |||
Research Design |
|
|
|
|
Data Collection |
|
|
|
|
Research Methods |
|
|
|
|
Data Analysis | Spearman Correlation Quantitative Exploration | Qualitative Reasoning Explanation | Delphi Inductive Conclusion | Qualitative Reasoning Explanation |
Latent Construct | Loadings | Standard Deviation | RhoA (ρA) | Composite Reliability | Average Variance Extracted (AVE) |
---|---|---|---|---|---|
Resource | 0.933 | 0.384 | 0.929 | 0.954 | 0.875 |
0.944 | |||||
0.928 | |||||
Maintenance | 0.893 | 0.389 | 0.917 | 0.943 | 0.846 |
0.923 | |||||
0.943 | |||||
Budget | 0.928 | 0.397 | 0.907 | 0.941 | 0.841 |
0.927 | |||||
0.895 | |||||
Turbulence | 0.923 | 0.395 | 0.910 | 0.942 | 0.844 |
0.922 | |||||
0.910 | |||||
Virtualization Usefulness | 0.928 | 0.377 | 0.921 | 0.948 | 0.857 |
0.923 | |||||
0.927 | |||||
Virtualization Ease of Use | 0.903 | 0.397 | 0.908 | 0.941 | 0.841 |
0.941 | |||||
0.906 | |||||
Virtualization Adoption Intention | 0.892 | 0.385 | 0.914 | 0.944 | 0.849 |
0.933 | |||||
0.937 |
Latent Construct | VAI | VU | VEU | R | M | B | T |
---|---|---|---|---|---|---|---|
VAI | x | ||||||
VU | 0.642 | x | |||||
VEU | 0.654 | 0.678 | x | ||||
R | 0.601 | 0.665 | 0.541 | x | |||
M | 0.642 | 0.650 | 0.570 | 0.622 | x | ||
B | 0.652 | 0.673 | 0.649 | 0.638 | 0.662 | x | |
T | 0.683 | 0.622 | 0.623 | 0.617 | 0.513 | 0.592 | x |
% | ||
---|---|---|
Eco Uncertainty—Q12a (Dependent Variable) | Least_Critical | 0.5 |
Slightly_Less_Critical | 12.2 | |
Neutral | 25.6 | |
Slighly_Critical | 27.0 | |
Most_Critical | 34.7 | |
Budgeted Cost—Q6a (Independent Variable) | Least_Critical | 1.8 |
Slightly_Less_Critical | 2.3 | |
Neutral | 17.1 | |
Slighly_Critical | 33.8 | |
Most_Critical | 45.0 | |
Maintenance—Q10b (Independent Variable) | Least_Critical | 1.3 |
Slightly_Less_Critical | 1.4 | |
Neutral | 8.1 | |
Slighly_Critical | 45.5 | |
Most_Critical | 43.7 | |
Prod Knowledge—Q8c (Independent Variable) | Least_Critical | 33.6 |
Slightly_Less_Critical | 28.3 | |
Neutral | 22.1 | |
Slighly_Critical | 10.2 | |
Most_Critical | 1.8 | |
Complex Infrastructure—Q4c (Independent Variable) | Least_Critical | 0.9 |
Slightly_Less_Critical | 5.0 | |
Neutral | 9.0 | |
Slighly_Critical | 25.7 | |
Most_Critical | 59.5 |
Budgeted Cost | Complex Infrastructure | Maintenance | Prod Knowledge | |||
---|---|---|---|---|---|---|
Spearman rho | Budgeted Cost (Q6a) | Correlation Coefficient | 1.000 | 0.243 | 0.838 | 0.218 |
Sig (2-tailed) | −0.001 | 0.001 | −0.002 | |||
Complex Infrastructure (Q10b) | Correlation Coefficient | 0.243 | 1.000 | 0.221 | 0.0209 | |
Sig (2-tailed) | −0.001 | 0.001 | 0.001 | |||
Maintenance (Q8c) | Correlation Coefficient | 0.838 | 0.221 | 1.000 | −0.243 | |
Sig (2-tailed) | 0.001 | 0.001 | 0.001 | |||
Prod Knowledge (Q4c) | Correlation Coefficient | 0.218 | 0.209 | 0.243 | 1.000 | |
Sig (2-tailed) | −0.002 | 0.001 | 0.001 |
Managerial Survey Findings Summary | Top Management Interview Findings Summary | Expert Focus Group Findings Summary |
---|---|---|
A1. Continuous spending on IT infrastructure investment currently. | B1. Current business economic turbulence has limited impact on IT investment decision due to it medium and long-term deliverables. | C1. Current economic turbulence is a seasonal factor that may be beyond the work scope of the budgeting process. |
A2. Current investment for IT infrastructure is based on the pressure from having what competitor have. | B2. Sufficient technologies to fulfil operational requirements. | C2. IT infrastructure is a medium and long-term planning where contribution may be experienced during or after the economic turbulence. |
A3. Technology features focuses on day to day electronic business. | B3. Existing IT infrastructures investments is to provide the platform to differentiate the market player. | C3. Technology edge service is a major differentiation in technology product competition. |
A4. Managements felt that their enterprise has over-invested on ICT. | B4. Slow migration to virtual due to security and capacity concerns on shared services. | C4. IaaS high baseline charges forcing impractical utility model for lower range users. |
A5. A technology feature is highly dynamic and evolving. | B5. Perceived IT infrastructures are nowadays considered as a Utility Tool for day-to-day operational support. | C5. Current market saturation is forcing competitive pricing war to attract customers. |
A6. Lack of existing internal IT expertise to implement technology solution. | B6. Focus primarily on core operation like client service and manufacturing flow. | C6. Technology resource is now available from outsourcing. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
JosephNg, P.S. Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise. Appl. Syst. Innov. 2023, 6, 11. https://doi.org/10.3390/asi6010011
JosephNg PS. Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise. Applied System Innovation. 2023; 6(1):11. https://doi.org/10.3390/asi6010011
Chicago/Turabian StyleJosephNg, Poh Soon. 2023. "Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise" Applied System Innovation 6, no. 1: 11. https://doi.org/10.3390/asi6010011
APA StyleJosephNg, P. S. (2023). Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise. Applied System Innovation, 6(1), 11. https://doi.org/10.3390/asi6010011