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An optimization model for investment in technology and government regulation

Abstract

Companies struggle every day to estimate the adequate level of investment in new technologies, and governments lack the tools to determine the impact of their regulations on industry including telecommunications networks. Despite these facts, few studies discuss ways to assess appropriate levels of investment for technological initiatives and government regulations. To fill this gap, this study provides an optimization model for the investment of technology and government regulation, based on efficiencies. Results obtained from surveying northern European companies support the importance of estimating investment in technology and government regulation levels. The survey identified the four most relevant factors for practitioners: quality, cost, technology adoption, and government regulations. Based on the survey’s results, the model evaluates the level of investment for technology adoption and government regulations using cost and quality as target variables. Additional data from a German carrier served to test the model. Results show that technology investment delivers more benefits in cost and quality by increasing technology adoption. However, the model also suggests that diminishing returns make efficiencies stall at a certain level of technology adoption, and shows an investment threshold dependent on the type of benefit, cost, or quality the company seeks to maximize. Regarding government regulation, the model shows a counterintuitive behavior at higher levels of investment for the cost coefficients and at all levels of investment for the quality coefficient. This suggests that government regulation effects could be shifting from fixed-order cost to other types of costs.

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Notes

  1. 1.

    Input values for efficiency coefficients and exponential parameters were taken from previous modeling experiences.

References

  1. 1.

    Adelman, D., Aydin, A., & Parker, R. P. (2015). Driving technology innovation down a competitive supply chain. In INFORMS.

  2. 2.

    Adjerid, I., Acquisti, A., Telang, R., Padman, R., & Adler-Milstein, J. (2015). The impact of privacy regulation and technology incentives: The case of health information exchanges. Management Science, 62(4), 1042–1063.

  3. 3.

    Alzawawi, M. (2014). Drivers and obstacles for creating sustainable supply chain management and operations. Retrieved March 20, 2016 from http://www.asee.org/documents/zones/zone1/2014/Student/PDFs/109.pdf.

  4. 4.

    Atkin, D., Chaudhry, A., Chaudry, S., Khandelwal, A. K., & Verhoogen, E. (2017). Organizational barriers to technology adoption: Evidence from soccer-ball producers in Pakistan. The Quarterly Journal of Economics, 132, 1101–1164.

  5. 5.

    Azadeh, A., Keramati, A., & Songhori, M. (2009). An integrated Delphi/VAHP/DEA framework for evaluation of information technology/information system (IT/IS) investments. International Journal of Advanced Manufacturing Technology, 45, 1233–1251.

  6. 6.

    Billington, P. (1987). The classic economic production quantity model with setup cost as a function of capital expenditure. Decision Sciences, 18, 25–42.

  7. 7.

    Bojanc, R., Jerman-Blazic, B., & Tekavcic, M. (2012). Managing the investment in information security technology by use of a quantitative modeling. Information Processing and Management, 48, 1031–1052.

  8. 8.

    Chen, Y., Liang, L., Yang, F., & Zhu, J. (2006). Evaluation of information technology investment: A data envelopment analysis approach. Computers & Operations Research, 33, 1368–1379.

  9. 9.

    Chou, T.-Y., Chou, S., & Tzeng, G.-H. (2006). Evaluating IT/IS investments: A fuzzy multi-criteria decision model approach. European Journal of Operational Research, 173, 1026–1046.

  10. 10.

    Chuu, S.-J. (2014). An investment evaluation of supply chain RFID technologies: A group decision-making model with multiple information source. Knowledge-Based Systems, 66, 210–220.

  11. 11.

    Devaraj, S., & Kohli, R. (2003). Performance impacts of information technology: Is actual usage the missing link? Management Science, 49(3), 273–289.

  12. 12.

    Dewan, S., Shi, C., & Gurbaxani, V. (2007). Investigating the risk-return relationship of information technology investment: Firm-level empirical analysis. Management Science, 53(12), 273–289.

  13. 13.

    Doerr, K., Gates, W., & Mutty, J. (2006). A hybrid approach to the valuation of RFID/MEMS technology applied to ordnance inventory. International Journal of Production Economics, 103, 1829–1842.

  14. 14.

    Gunasekaran, A., Love, P. E. D., Rahimi, F., & Miele, R. (2001). A model for investment justification in information technology projects. International Journal of Information Management, 21, 349–364.

  15. 15.

    Hwang, H.-G., Ku, C.-Y., Yen, D., & Cheng, C.-C. (2004). Critical factors influencing the adoption of data warehouse technology: A study of the banking industry in Taiwan. Decision Support Systems, 37(1), 1–21. https://doi.org/10.1016/S0167-9236(02)00191-4.

  16. 16.

    Kauffman, R., Liu, J., & Ma, D. (2015). Technology investment decision-making under uncertainty. Information Technology and Management, 16, 153–172.

  17. 17.

    Lee, I., & Lee, B.-C. (2010). An investment evaluation of supply chain RFID technologies: A normative modeling approach. International Journal of Production Economics, 125, 313–323.

  18. 18.

    Lu, M.-T., Lin, S.-W., & Tzeng, G.-H. (2013). Improving RFID adoption in Taiwan’s healthcare industry based on a DEATEL technique with a hybrid MCDM model. Decision Support Systems, 56, 259–269.

  19. 19.

    Luftman, J., & Kempaiah, R. (2008). Key issues for IT executives 2007. MIS Quarterly Executive, 7, 99–112.

  20. 20.

    Marchet, G., Perotti, S., & Mangiaracina, R. (2012). Modeling the impacts of ICT adoption for inter-modal transportation. International Journal of Physical Distribution and Logistics Management, 42, 110–127.

  21. 21.

    Menon, N., & Lee, B. (2000). Cost control and production performance enhancement by IT investment and regulation changes: Evidence from the healthcare industry. Decision Support Systems, 30, 153–169.

  22. 22.

    Newell, R., Jaffe, A., & Stavins, R. (1999). The induced innovation hypothesis and energy-saving technological change. The Quarterly Journal of Economics, 114, 941–975.

  23. 23.

    Rouhani, S., Ghazanfari, M., & Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS. Expert Systems with Applications, 39, 3764–3771.

  24. 24.

    You, C. J., Lee, C. K. M., Chen, S. L., & Jiao, R. (2012). A real option theoretic fuzzy evaluation model for enterprise resource planning investment. Journal of Engineering and Technology Management, 29, 47–61.

  25. 25.

    Zandi, F., & Tavana, M. (2011). A fuzzy goal programming model for strategic information technology investment assessment. Benchmarking: An International Journal, 18, 172–196.

  26. 26.

    Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on E-business. Management Science, 52(10), 1557–1576.

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Acknowledgements

The authors wish to thank the Mexican National Council of Science and Technology (CONACYT) for financing this work.

Author information

Correspondence to Oliverio Cruz-Mejia.

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Appendix

Appendix

Technology adoption cost-oriented model

The derivative of \(TC_{c}\) with respect to technology (T) is given by:

$$\frac{{\partial ({\text{TC}}_{\text{c}} )}}{{\partial {\text{T}}}} = \frac{{{\text{O}}\left( {\frac{{\partial {\text{R}}}}{{\partial {\text{T}}}}} \right){\text{D}}}}{\text{Q}} + \frac{{\partial {\text{J}}}}{{\partial {\text{T}}}}{\text{CD}} + 1$$
(10)

From Eqs. 3 and 8:

$$\frac{{\partial {\text{R}}}}{{\partial {\text{T}}}} =\upbeta_{1} \left( {{\text{M}} - {\text{N}}} \right){\text{e}}^{{\upbeta_{1} {\text{T}}}}$$
(11)

From Eqs. 10 and 7, setting Eq. 10 = 0:

$$\frac{{\partial {\text{R}}}}{{\partial {\text{T}}}} = - \left( {\frac{{1 + {\text{CD}}\left( {\frac{{\partial {\text{J}}}}{{\partial {\text{T}}}}} \right)}}{\text{OD}}} \right){\text{Q}}$$
(12)

Given that:

$$\frac{{\partial {\text{J}}}}{{\partial {\text{T}}}} =\upbeta_{5} \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}}}$$

Then from Eqs. 11 and 12:

$$\upbeta_{1} \left( {{\text{M}} - {\text{N}}} \right){\text{e}}^{{\upbeta_{1} {\text{T}}}} = - \left( {\frac{{1 + {\text{CD}}\left( {\upbeta_{5} \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}}} } \right)}}{\text{OD}}} \right){\text{Q}}$$
(13)

Substituting Eq. 2 in Eq. 13’s Q and solving for \(R^{*}\):

$${\text{R}}^{ *} = \left[ {\frac{{ -\upbeta_{1} \left( {{\text{M}} - {\text{N}}} \right){\text{e}}^{{\upbeta_{1} {\text{T}}}} }}{{1 + {\text{CD}}\left( {\upbeta_{5} \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}}} } \right)}}} \right]^{2} \frac{\text{ODHI}}{2}$$
(14)

From Eqs. 3 and 14:

$${\text{T}}^{ *} = \frac{{\ln \left( {\frac{{{\text{R}}^{ *} - {\text{N}} + {\text{M}}}}{{{\text{M}} - {\text{N}}}}} \right)}}{{\upbeta_{1} }}$$
(15)

Technology adoption quality-oriented model

Following the same process, from Eqs. 16 and 17:

$$\frac{{\partial {\text{TC}}_{\text{Q}} }}{{\partial {\text{T}}}} = 1 + \frac{{{\text{HQ}}\left( {\frac{{\partial {\text{I}}}}{{\partial {\text{T}}}}} \right)}}{2}$$
(19)

Setting Eq. 20 = 0

$$\frac{{\partial {\text{I}}}}{{\partial {\text{T}}}} = - \frac{2}{\text{HQ}}$$
(20)

From Eqs. 4 and 18:

$$\frac{{\partial {\text{I}}}}{{\partial {\text{T}}}} =\upbeta_{3} \left( {{\text{L}} - {\text{U}}} \right){\text{e}}^{{\upbeta_{3} {\text{T}}}}$$
(21)

Then:

$$\upbeta_{3} \left( {{\text{L}} - {\text{U}}} \right){\text{e}}^{{\upbeta_{3} {\text{T}}}} = - \frac{2}{\text{HQ}}$$
(22)

Substituting Eq. 2 in Q:

$${\text{I}}^{ *} = \frac{{{\text{ORDH}}\left[ {\upbeta_{3} \left( {{\text{L}} - {\text{U}}} \right){\text{e}}^{{\upbeta_{3} {\text{T}}}} } \right]^{2} }}{2}$$
(23)

\(J^{*}\) is obtained from Eqs. 5 and 15:

$${\text{J}}^{ *} = \left( {{\text{E}} - {\text{A}}} \right) + \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}^{ *} }}$$
(24)

Summarizing, the following optimal equations were obtained:

$${\text{R}}^{ *} = \left[ {\frac{{ -\upbeta_{1} \left( {{\text{M}} - {\text{N}}} \right){\text{e}}^{{\upbeta_{1} {\text{T}}}} }}{{1 + {\text{CD}}\left( {\upbeta_{5} \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}}} } \right)}}} \right]^{2} \frac{\text{ODHI}}{2}$$
(14)
$${\text{T}}^{ *} = \frac{{\ln \left( {\frac{{{\text{R}}^{ *} - {\text{N}} + {\text{M}}}}{{\left( {{\text{M}} - {\text{N}}} \right)}}} \right)}}{{\upbeta_{1} }}$$
(15)
$${\text{I}}^{ *} = \frac{{{\text{ORDH}}\left[ {\upbeta_{3} \left( {{\text{L}} - {\text{U}}} \right){\text{e}}^{{\upbeta_{3} {\text{T}}}} } \right]^{2} }}{2}$$
(23)
$${\text{J}}^{ *} = \left( {{\text{E}} - {\text{A}}} \right) + \left( {{\text{A}} - {\text{E}}} \right){\text{e}}^{{\upbeta_{5} {\text{T}}^{ *} }}$$
(24)

Government regulation optimization

The derivative of \(TC_{c}\) with respect to government regulations (G) is given by:

$$\frac{{\partial ({\text{TC}}_{\text{c}} )}}{{\partial {\text{G}}}} = \frac{{{\text{O}}\left( {\frac{{\partial {\text{R}}}}{{\partial {\text{G}}}}} \right){\text{D}}}}{\text{Q}} + \frac{{\partial {\text{J}}}}{{\partial {\text{G}}}}{\text{CD}} + 1$$
(32)

From Eqs. 26 and 31:

$$\frac{{\partial {\text{R}}}}{{\partial {\text{G}}}} = - \frac{{\left( {{\text{N}} - {\text{M}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{2} {\text{G}}}}}} }}{{\upbeta_{2} {\text{G}}^{2} }}$$
(33)

From Eqs. 33 and 30, setting Eq. 33 = 0:

$$\frac{{\partial {\text{R}}}}{{\partial {\text{G}}}} = - \left( {\frac{{1 + {\text{CD}}\left( {\frac{{\partial {\text{J}}}}{{\partial {\text{G}}}}} \right)}}{\text{OD}}} \right){\text{Q}}$$
(34)

Then, from Eqs. 33 and 34:

$$\frac{{\left( {{\text{N}} - {\text{M}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{2} {\text{G}}}}}} }}{{\upbeta_{2} {\text{G}}^{2} }} = \left( {\frac{{1 + {\text{CD}}\left( {\frac{{\partial {\text{J}}}}{{\partial {\text{G}}}}} \right)}}{\text{OD}}} \right){\text{Q}}$$
(35)

Substituting Eq. 2 in Eq. 35’s Q and solving for \(R^{*}\):

$${\text{R}}^{ *} = \frac{{\left[ {\left( {{\text{N}} - {\text{M}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{2} {\text{G}}}}}} } \right]^{2} {\text{ODHI}}}}{{2\left[ {\upbeta_{2} {\text{G}}\left( {1 - {\text{CD}}\left( {\frac{{\left( {{\text{E}} - {\text{A}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{6} {\text{G}}}}}} }}{{\upbeta_{6} {\text{G}}^{2} }}} \right)} \right)} \right]^{2} }}$$
(36)

From Eq. 26:

$${\text{G}}^{ *} = \frac{1}{{\upbeta_{2} \ln \left( {\frac{{{\text{R}}^{ *} - {\text{N}} + {\text{M}}}}{{\left( {{\text{N}} - {\text{M}}} \right)}}} \right)}}$$
(37)

Government regulation quality-oriented model

Following the same process, from Eqs. 39 and 40:

$$\frac{{\partial {\text{TC}}_{\text{Q}} }}{{\partial {\text{G}}}} = 1 + \frac{{{\text{HQ}}\left( {\frac{{\partial {\text{I}}}}{{\partial {\text{G}}}}} \right)}}{2}$$
(41)

Setting Eq. 41 = 0:

$$\frac{{\partial {\text{I}}}}{{\partial {\text{G}}}} = - \frac{2}{\text{HQ}}$$
(42)

From Eqs. 27 and 41:

$$\frac{{\partial {\text{I}}}}{{\partial {\text{G}}}} = - \frac{{\left( {{\text{U}} - {\text{L}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{4} {\text{G}}}}}} }}{{\upbeta_{4} {\text{G}}^{2} }}$$
(43)

Then:

$$- \frac{{\left( {{\text{U}} - {\text{L}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{4} {\text{G}}}}}} }}{{\upbeta_{4} {\text{G}}^{2} }} = - \frac{2}{\text{HQ}}$$
(44)

Substituting Eq. 2 in Eq. 44’s Q:

$${\text{I}}^{ *} = \frac{{{\text{ORDH}}\left[ {\left( {{\text{U}} - {\text{L}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{4} {\text{G}}}}}} } \right]^{2} }}{{2\left[ {\upbeta_{4} {\text{G}}^{2} } \right]^{2} }}$$
(45)

\(J^{*}\) is obtained from Eqs. 28 and 38:

$${\text{J}}^{ *} = \left( {{\text{E}} - {\text{A}}} \right) + \left( {{\text{E}} - {\text{A}}} \right){\text{e}}^{{\frac{1}{{\upbeta_{6} {\text{G}}^{ *} }}}}$$
(46)

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Monsreal-Barrera, M.M., Cruz-Mejia, O., Ozkul, S. et al. An optimization model for investment in technology and government regulation. Wireless Netw (2019). https://doi.org/10.1007/s11276-019-01958-z

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Keywords

  • Telecommunications industry
  • Technology adoption
  • Government regulation
  • Investment
  • Optimization