Impact of Information Technology on Business Performance: Integrated Structural Equation Modeling and Artificial Neural Network Approach

Document Type : Article

Authors

1 Dumlupinar University, Simav Vocational School, 43500, Simav- Kütahya, Turkey

2 Sakarya University, Faculty of Management, Serdivan-Sakarya, Turkey

Abstract

In today's globalizing world, which is also called the information age, information and information technologies are becoming increasingly important for businesses, and they have become an indispensable part of economic and social life. Nowadays, it is impossible to think about a business that is far from information technology, and also it is important not only to find information but also to find the fastest and highly reliable information. The important thing is to use information technologies effectively and efficiently. Therefore, it is expected that the effective usage of information technology will have significant positive effects on business performance.  The aim of this study is to examine and analyze the impact of the intensive usage of information technologies on business performance in the supply chain process. A sequential, multi-method approach integrating both structural equation modeling (SEM) and neural network analysis was employed in this research. The information technology usage performance network was formed by using the SEM model, and the ANN model was used to predict a relationship between information technology usage levels and business performance by using these network outputs. Furthermore, the validity and reliability tests of the relevant model data were performed.

Keywords

Main Subjects


References
1. Papazoglou, M. and Tsalgatidou, A. \Business-tobusiness
electronic commerce issues and solutions",
Decision Support Systems, 29(4), pp. 301-304 (2000).
2. Webster, F. \The information society: conceptions
and critique", Encyclopedia of Library and Information
Science, 58(21), pp. 74-112 (1996).
3. Brynjolfsson, E. and Hitt, L.M. \Beyond computation:
information technology, organizational transformation
and business performance", The Journal of Economic
Perspectives, 14(4), pp. 23-48 (2000).
4. Byrd, T.A. and Davidson N.W. \Examining possible
antecedents of IT impact on the supply chain and its
e ect on rm performance", Information and Management,
41, pp. 243-255 (2003).
5. Boran, S. and Diren, D.D. \Analysis of out of control
signals in multivariate processes with multilayer neural
network", Acta Physica Polonica A, 132(3), pp. 1054-
1057 (2017).
6. Bolvar-Ramos, M.T., Garca-Morales, V.J., and
Martn-Rojas, R. \The e ects of information technology
on absorptive capacity and organisational performance",
Technology Analysis & Strategic Management,
25(8), pp. 905-922 (2013).
7. Chen, Y., Wang, Y., Nevo S., Benitez, J., and Kou, G.
\Improving strategic
exibility with information technologies:
insights for rm performance in an emerging
economy", Journal of Information Technology, 32(1),
pp. 10-25 (2017).
8. Chae, H.C., Koh, C.E., and Prybutok, V.R. \Information
technology capability and rm performance:
contradictory ndings and their possible causes", MIS
Quarterly, 38(1), pp. 305-326 (2014).
9. Chakravarty, A., Grewal, R., and Sambamurthy, V.
\Information technology competencies, organizational
agility, and rm performance: enabling and facilitating
roles", Information Systems Research, 24(4), pp. 976-
997 (2013).
10. Garrison, G., Wake eld, R.L., and Kim, S. \The
e ects of IT capabilities and delivery model on cloud
computing success and rm performance for cloud
supported processes and operations", International
Journal of Information Management, 35(4), pp. 377-
393 (2015).
11. Kasemsap, K. \The role of information system within
enterprise architecture and their impact on business
performance", Technology, Innovation, and Enterprise
Transformation, pp. 262-284 (2015).
12. Keramati, A., Afshari-Mofrad, M., Behmanesh, I.,
and Gholami, R. \The impact of information technology
maturity on rm performance considering the
moderating role of relational maturity: an empirical
research", International Journal of Business Information
Sys., 23(1), pp. 23-43 (2016).
13. Li, M. and Ye, L.R. \Information technology and rm
performance: Linking with environmental, strategic
and managerial contexts", Information & Management,
35(1), pp. 43-51 (1999).
14. Liu, Z., Prajogo, D., and Oke, A. \Supply chain
technologies: linking adoption, utilization, and performance",
Journal of Supply Chain Management, 52(4),
pp. 22-41 (2016).
15. Peng, J., Quan, J., Zhang, G., and Dubinsky, A.J.
\Mediation e ect of business process and supply chain
management capabilities on the impact of IT on rm
performance: Evidence from Chinese rms", International
Journal of Information Management, 36(1), pp.
89-96 (2016).
16. Rivard, S., Raymond, L., and Verreault, D. \Resourcebased
view and competitive strategy: An integrated
model of the contribution of information technology to
rm performance", The Journal of Strategic Information
Systems, 15(1), pp. 29-50 (2006).
17. Sahin, H. and Topal, B. \The e ect of the use of information
technologies in businesses on cost and nancial
performance", International Journal of Engineering
Innovations and Research (IJEIR), 5(6), pp. 394-402
(2016).
18. Weill, P. \The relationship between investment in
information technology and rm performance: A study
of the valve manufacturing sector", Information Systems
Research, 3(4), pp. 307-333 (1992).
19. Wu, F., Yeniyurt, S., Kim, D., and Cavusgil, S.T.
\The impact of information technology on supply chain
capabilities and rm performance: A resource-based
view", Industrial Marketing Management, 35(4), pp.
493-504 (2006).
20. Thoni, A. and Tjoa, A.M. \Information technology
for sustainable supply chain management: a literature
survey", Enterprise Information Systems, 11(6), pp.
828-858 (2017).
21. Jain, V., Wadhwa, S., and Deshmukh, S.G. \Revisiting
information systems to support a dynamic supply
chain: Issues and perspectives", Production Planning
& Control, 20(1), pp. 17-29 (2009).
22. Perego, A., Perotti, S., and Mangiaracina, R. \ICT
for logistics and freight transportation: A literature
review and research agenda", International Journal of
Physical Distribution & Logistics Management, 41(5),
pp. 457-483 (2011).
23. Zhang, X., Pieter van Donk, D., and van der Vaart,
T. \Does ICT in
uence supply chain management and
performance? A review of survey-based research",
International Journal of Operations & Production
Management, 31(11), pp. 1215-1247 (2011).
H. Sahin and B. Topal/Scientia Iranica, Transactions B: Mechanical Engineering 25 (2018) 1272{1280 1279
24. Rahimi, Y., Tavakkoli-Moghaddam, R., Shojaie, S.,
and Cheraghi, I. \Design of an innovative construction
model for supply chain management by measuring
agility and cost of quality: An empirical study",
Scientia Iranica, 24(5), pp. 2515-2526 (2017).
25. Bayram, N., Introduction to Structural Equation Modeling,
Ezgi Bookstore, Bursa (2010).
26. Cokluk, O., Sekercioglu G., and Buyukozturk, S.,
Multivariate Statistics for Social Sciences, Pegem Publications,
Ankara (2010).
27. Meydan, C.H. and Sesen, H., Structural Equation Modeling,
AMOS Applications, Detay Publishing, Ankara
(2011).
28. Sharma, S.K., Joshi, A., and Sharma, H. \A multianalytical
approach to predict the Facebook usage in
higher education", Computers in Human Behavior, 55,
pp. 340-353 (2016).
29. Wong, T.C., Law, K.M., Yau, H.K., and Ngan, S.C.
\Analyzing supply chain operation models with the
PC-algorithm and the neural network", Expert Systems
with Applications, 38(6), pp. 7526-7534 (2011).
30. Chong, A.Y.L., Liu, M.J., Luo, J., and Keng-Boon, O.
\Predicting RFID adoption in healthcare supply chain
from the perspectives of users", International Journal
of Production Economics, 159, pp. 66-75 (2015).
31. Leong, L.Y., Hew, T.S., Lee, V.H., and Ooi, K.B.
\An SEM-arti cial-neural-network analysis of the relationships
between SERVPERF, customer satisfaction
and loyalty among low-cost and full-service airline",
Expert Systems with Applications, 42(19), pp. 6620-
6634 (2015).
32. Tan, G.W.H., Ooi, K.B., Leong, L.Y., and Lin,
B. \Predicting the drivers of behavioral intention to
use mobile learning: A hybrid SEM-neural networks
approach", Computers in Human Behavior, 36, pp.
198-213 (2014).
33. Chong, A.Y.L. and Bai, R. \Predicting open IOS
adoption in SMEs: An integrated SEM-neural network
approach", Expert Systems with Applications, 41(1),
pp. 221-229 (2014).
34. Chan, F.T. and Chong, A.Y. \A SEM-neural network
approach for understanding determinants of interorganizational
system standard adoption and performances",
Decision Support Systems, 54(1), pp. 621-
630 (2012).
35. Leong, L.Y., Hew, T.S., Tan, G.W.H., and Ooi,
K.B. \Predicting the determinants of the NFC-enabled
mobile credit card acceptance: A neural networks
approach", Expert Systems with Applications, 40(14),
pp. 5604-5620 (2013).
36. Chong, A.Y.L. \A two-staged SEM-neural network
approach for understanding and predicting the determinants
of m-commerce adoption", Expert Systems
with Applications, 40, pp. 1240-1247 (2013).
37. Yadav, R., Sharma, S.K., and Tarhini, A. \A multianalytical
approach to understand and predict the
mobile commerce adoption", Journal of Enterprise
Information Management, 29(2), pp. 222-237 (2016).
38. Scott, J.E. and Walczak, S. \Cognitive engagement
with a multimedia ERP training tool: Assessing
computer self-ecacy and technology acceptance", Information
& Management, 46(4), pp. 221-232 (2009).
39. Hew, T.S., Leong, L.Y., Ooi, K.B., and Chong, A.Y.L.
\Predicting drivers of mobile entertainment adoption:
a two-stage SEM-arti cial-neural-network analysis",
Journal of Computer Information Systems, 56(4), pp.
352-370 (2016).
40. Sharma, S.K., Gaur, A., Saddikuti, V., and Rastogi,
A. \Structural equation model (SEM)-neural network
(NN) model for predicting quality determinants of elearning
management systems", Behaviour & Information
Technology, 36(10), pp. 1053-1066 (2017).
41. Bejou, D., Wray, B., and Ingram, T.N. \Determinants
of relationship quality: An arti cial neural network
analysis", Journal of Business Research, 36, pp. 137-
143 (1996).
42. Ozcanli, Y., Kosovali Cavus, F., and Beken, M.
\Comparison of mechanical properties and arti cial
neural networks modeling of PP/PET blends", Acta
Physica Polonica A, 130(1), pp. 444-446 (2016).
43. Davraz, M., Kilincarslan, S., and Ceylan, H. \Predicting
the Poisson ratio of lightweight concretes using
arti cial neural network", Acta Physica Polonica A,
128, pp. 184-186 (2015).
44. Tekin, H.O., Manici, T., Altunsoy, E.E., Yilancioglu,
K., and Yilmaz, B. \An arti cial neural network-based
estimation of Bremsstarahlung photon
ux calculated
by MCNPX", Acta Physica Polonica A, 132(3), pp.
967-969 (2017).
45. Erkaymaz, O.,  Ozer, M., and Yumusak, N. \Impact
of small-world topology on the performance of a feedforward
arti cial neural network based on 2 di erent
real-life problems", Turkish Journal of Electrical Engineering
& Computer Sciences, 22(3), pp. 708-718
(2014).
46. Gunturkun, R. \Estimation of medicine amount used
anesthesia by an arti cial neural network", Journal of
Medical Systems, 34(5), pp. 941-946 (2010).
47. Sharma, S.K., Govindaluri, S.M., and Al Balushi,
S.M. \Predicting determinants of internet banking
adoption: A two-staged regression-neural network
approach", Management Research Review, 38(7), pp.
750-766 (2015).
48. Boutalbi, E., Ait Gougam, L., and Mekideche-Chafa,
F. \Comparison of second order algorithms for function
approximation with neural networks", Acta Physica
Polonica A, 128, pp. 184-186 (2015).

Volume 25, Issue 3
Transactions on Mechanical Engineering (B)
May and June 2018
Pages 1272-1280
  • Receive Date: 19 February 2018
  • Revise Date: 08 May 2018
  • Accept Date: 28 May 2018