ORIGINAL_ARTICLE
The effect of crude oil futures price on risk premium volatilities in the futures market
This paper explores the impact of crude oil futures prices on risk premium volatilities in the NYMEX futures market. For this purpose, the ARCH and GARCH methods are used to model risk premium volatilities and explore how crude oil futures prices influence the risk premium volatilities in futures contract with a maturity of one-month, two-month and three-month over 1990-2014. In addition, it examines the impact of various maturities for futures contracts. The results indicate positive and statistically significant relationship between risk premium volatility and crude oil futures prices, and this relationship varies across the maturity length with change in maturity length. The longer the futures maturities, the higher the impact of futures crude oil prices on risk premium volatility is anticipated.
https://pbr.put.ac.ir/article_58052_032bb947d9e7e0fcb202f067842a72df.pdf
2017-11-01
3
8
crude oil futures prices
Risk premium volatility
NYMEX futures market
ARCH and GARCH volatility modeling JEL classification: C32
Q74
G32
G13
Mirhossein
Mousavi
hmousavi@alzahra.ac.ir
1
associate professor, economics department, alzahra university
LEAD_AUTHOR
Mohammad
Mazraati
maryam.motiei@gmail.com
2
OFID, Vienna
AUTHOR
Alquist, Ron, Kilian, Lutz, 2010. What do we learn from the price of crude oil futures? Journal of Applied Econometrics 25, 539– 573.
1
Chernenko, S. V., Schwarz, K. B. andWright, J. H, : 2004, The Information Content of Forward and Futures Prices: Market expectations and the price of risk. Board of Governors of the Federal Reserve System, International Finance Discussion Papers, No. 808.
2
Chin M. D., M. LeBlanch and O. Coibion (2005), The Predictive Content of Energy Futures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline, NBER Working Paper 11033.
3
Chinn, M., LeBlanc, M. and Coibion, O, : 2005 “The Predictive Content of Energy Futures: An Update on Petroleum, Natural gas, Heating Oil and Gasoline” NBER Working Paper No. 11033, February.
4
De Roon, Frans, Nijman, Theo, Veld, Chris, 2000. Hedging pressure effects in futures markets. Journal of Finance 55, 1437–1456.
5
Engle, R. F., Lilien, D. M. and Russell, R. P.: “Estimating time varying risk premia in the term structure: the ARCH-M model”. Econometrica, 1987, vol. 55, No. 2, 391- 497.
6
Fan Ying , Yue-Jun Zhang, 2008, Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach, DOI: 10.1016/j.eneco.2008.04.002.
7
Fattouh, Bassam, Kilian, Lutz, Mahadeva, Lavan, 2013. The role of speculation in oil markets: what have we learned so far? Energy Journal 34, 7–33.
8
Haase Marco and Heinz Zimmermann, 2013, Scarcity, Risk Premiums, and the Pricing of Commodity Futures: The Case of Crude Oil Contracts, The Journal of Alternative Investments Summer 2013, 16 (1) 43-71; DOI: https://doi.org/10.3905/jai.2013.16.1.043
9
Hamilton James D., Wu Jing Cynthia, 2014, Risk premia in crude oil futures prices, Journal of International Money and Finance, Volume 42, April 2014, Pages 9-37
10
Irwin, Scott H., Sanders, Dwight R., 2012. Testing the masters hypothesis in commodity futures markets. Energy Economics 34, 256–269.
11
Jalali Naiini1 Ahmadreza ؛ Vahid Ghorbani Pashakolae؛ Mohamad Sayadi, 2013, Risk Spillover Effect between Oil Spot and Futures Price Returns, Iran Energy Economics Research Journal, 3(9): 31-52.
12
Keynes, John M., 1930. A Treatise on Money, vol. 2. Macmillan, London.
13
Kilian, Lutz, Murphy, Daniel P., 2013. The role of inventories and speculative trading in the global market for crude oil (forthcoming). Journal of Applied Econometrics.
14
Melolinna Marko, 2011, What explains risk premia in crude oil futures?, Bank of Finland Research, discussion Papers 2 • 2011.
15
Moosa A. Nabeel E. Al-Loughani, 1994, Unbiasedness and time varying risk premia in the crude oil futures market, Energy Economics, 1994, vol. 16, issue 2, 99-105.
16
Pindyck R.S.,2001, The dynamics of commodity spot and futures markets: A primer. The Energy Journal, 3 (1-22), 2001.
17
ORIGINAL_ARTICLE
Relationship between Financial Leverage and Firm Growth in the Oil and Gas Industry: Evidence from OPEC
Recent theories of firm dynamics emphasize on the role of financial variables as determinants of firm growth. Most of the technical literature shows that there is a positive relationship between financial leverage and firm growth. The purpose of this paper is to examine whether such relationship exists among oil and gas companies within the Organization of the Petroleum Exporting Countries (OPEC). Data were collected from the selected members of the OPEC. The collected data was then analyzed using the Arellano and Bond (1991) GMM method and Sargan test. The results showed a significant and positive relationship between financial leverage and firm growth which is in line with the technical literature. This research contributes to the body of knowledge by examining a specific and important sector within several different countries. It shows the current theory is not affected by industry or country.
https://pbr.put.ac.ir/article_58117_b1090a74623ebec14efb1c52a18333bf.pdf
2017-11-01
9
21
10.22050/pbr.2017.58117
Financial Leverage
Firm growth
GMM
Oil and gas industry
OPEC
Seyed Mohammad
Javadi
javadi@put.ac.ir
1
Accounting Department, Tehran Faculty of Petroleum, Petroleum University of Technology
LEAD_AUTHOR
Abbas
Alimoradi
alimoradi@put.ac.ir
2
Accounting Department, Tehran Faculty of Petroleum, Petroleum University of Technology
AUTHOR
Mohammad Reza
Ashtiani
m.ashtiani.1361@gmail.com
3
MA in Oil & Gas Economics, Petroleum Faculty of Tehran, Petroleum University of Technology, Iran
AUTHOR
Abor, J., 2005. The effect of capital structure on profitability: an empirical analysis of listed firms in Ghana. The journal of risk finance, 6(5), pp.438–445.
1
Aivazian, V. a., Ge, Y. & Qiu, J., 2005. The impact of leverage on firm investment: Canadian evidence. Journal of Corporate Finance, 11(1–2), pp.277–291.
2
Arellano, M. & Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), pp.277–297.
3
Awan, H.M. et al., 2010. How growth opportunities are related to corporate leverage decisions? Investment Management and Financial Innovations, 7(1), pp.90–97.
4
Baltagi, B., 2008. Econometric analysis of panel data, John Wiley & Sons.
5
Bei, Z. & Wijewardana, W.P., 2012. Financial leverage, firm growth and financial strength in the listed companies in Sri Lanka. Procedia - Social and Behavioral Sciences, 40, pp.709–715. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1877042812007203.
6
Bistrova, J., Lace, N. & Peleckien.e, V., 2011. The influence of capital structure on baltic corporate performance. Journal of Business Economics and Management, 12(4), pp.655–669.
7
ÇOBAN, S., 2014. THE INTERACTION BETWEEN FIRM GROWTH AND PROFITABILITY: EVIDENCE FROM TURKISH (LISTED) MANUFACTURING FIRMS. Bilgi Ekonomisi ve Y{ö}netimi Dergisi, 9(1).
8
Davidson, R. & MacKinnon, J.G., 2004. Econometric theory and methods, Oxford University Press New York.
9
Donaldson, G., 1961. Corporate debt capacity, Harvard University, Boston, available at: http://img.kb.dk/tidsskriftdk/pdf/leo/leo_0028-PDF/leo_0028_81960.pdf
10
Graham, J.R., 2000. How Big Are the Tax Benefits of Debt? The Journal of Finance, 55(5), pp.1901–1941. Available at: http://dx.doi.org/10.1111/0022-1082.00277.
11
Harris, M. & Raviv, A., 1991. The theory of capital structure. the Journal of Finance, 46(1), pp.297–355.
12
Hovakimian, A., Opler, T. & Titman, S., 2001. The debt-equity choice. Journal of Financial and Quantitative analysis, 36(1), pp.1–24.
13
Hurme, S., 2010. Firm Leverage and Its Effects on Future, Master's thesis, Aolto University.
14
Huynh, K.P. & Petrunia, R.J., 2010. Age effects, leverage and firm growth. Journal of Economic Dynamics and Control, 34(5), pp.1003–1013. Available at: http://dx.doi.org/10.1016/j.jedc.2010.01.007.
15
Jensen, M.C. & Meckling, W.H., 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of financial economics, 3(4), pp.305–360.
16
I. Choi, 2001. “Unit root tests for panel data, ” Journal of International money and Financ., vol. 20, no. 2, pp. 249–272.
17
Lang, L., Ofek, E. & Stulz, R.M., 1996. Leverage, investment, and firm growth. Journal of Financial Economics, 40(1), pp.3–29.
18
Mazhar, A. & Nasr, M., 2010. Determinants of capital structure decisions case of Pakistani government owned and private firms. International Review of Business Research Papers, 6(1), pp.40–46.
19
McConnell, J.J. & Servaes, H., 1995. Equity ownership and the two faces of debt. Journal of financial economics, 39(1), pp.131–157.
20
Modigliani, F. & Miller, M.H., 1963. Corporate income taxes and the cost of capital: a correction. The American economic review, 53(3), pp.433–443.
21
Poblete, L. & Elin Grimsholm, 2010. Internal and External factors hampering SME growth: A qualitative case study of SMEs in Thailand.
22
Rahaman, M. M., 2011. Access to financing and firm growth. Journal of Banking and Finance, 35(3), pp.709–723. Available at: http://www.sciencedirect.com/science/article/pii/S0378426610003377.
23
Stulz, R., 1988. Managerial control of voting rights: Financing policies and the market for corporate control. Journal of financial Economics, 20, pp.25–54.
24
Wu, Y., 2013. Leverage and firm growth : The European evidences. Master's thesis, Aolto University.
25
Yat Hung, C., Ping Chuen Albert, C. & Chi Man Eddie, H., 2002. Capital structure and profitability of the property and construction sectors in Hong Kong. Journal of Property Investment & Finance, 20(6),
26
ORIGINAL_ARTICLE
Technological Change and its Relationship with Total Factor Productivity in Iran's Petroleum Refineries
Nowadays, using appropriate technologies in order to increase productivity of production factors can be resulted in optimal factors employment and production enhancement in factories. Technological change is considered as one of the main sources of productivity growth. The purpose of this paper is to analyze the various aspects of technological change and their relationship with total factor productivity in Iran’s petroleum refineries. In order to achieve this goal, we used the econometric method to estimate the cost function. This method seems useful to estimate the structure of factors demand, considering changes in factors prices and technology status. We estimated a translog cost function as well as equations system of cost share, using Seemingly Unrelated Regressions (SUR) approach from 1982 to 2012. The results show that the average rate of technological change was -0.482 percent over the study period. It means that over time, the cost growth rate of production units was decreased mainly due to technological change. Furthermore, the results indicate that technological change was biased towards the use of more labor and material, while it saved more capital and energy. Also, based on the estimation results, we decomposed total factor productivity growth rate into the contributions of technological change and economies of scale. Decomposition results show that the share of technological change in the productivity growth is greater than that of scale economies.
https://pbr.put.ac.ir/article_57912_f02aabb3a9e5968706ebe321bf66c1e3.pdf
2017-11-01
22
28
10.22050/pbr.2017.57912
Productivity
technological change
translog cost function
petroleum refineries
Nader
Dashti
n_dashti@put.ac.ir
1
Assistant Professor, Energy Economics & Management Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran
LEAD_AUTHOR
Mahdi
Rostami
rostami92@yahoo.com
2
Assistant Professor, Energy Economics & Management Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran
AUTHOR
Reza
Rashidi
rezarashidigermi1990@gmail.com
3
M.A. Student in Oil & Gas Economics, Energy Economics & Management Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran
AUTHOR
Atkinson, Anthony, and J. E. Stiglitz. (1969). A New View of Tecnological Change, Economic Journal, 79, 573-578.
1
Baltagi, B. H. (2005). Econometrics Analysis of Panel Data. John Wiley and Sons Ltd, 2005.
2
Baltagi, B. H. and J.M.Griffin. (1988). A General Index of Technical Change. Journal of Political Economy, 96:20_41.
3
Bhattacharyya, S.C. (2011). Energy Economics, Concepts, Issues, Markets and Governance. Springer-Verlag London.
4
Binswanger, H. P. (1974). A Microeconomic Approach to Induced Innovation. Economic Journal, 84, 940- 958.
5
Chambers, R. G. (1988). Applied Production Analysis: A Dual Approach. Cambridge University Press.
6
Chen, K. and Edouard Wemy, (2015). Investment-specific technological changes: The source of long-run TFP fluctuations, European Economic Review, 80, 230- 252.
7
Datta, A., & Christoffersen, S. (2004). Production Costs, Scale Economies and Technical Change in U.S. Textile and Apparel Industries. School of Business Administration,Philadelphia university.
8
Drandakis, E. M. and E. S. Phelps. (1966). A Model of Induced Invention, Growth and Distribution, Economic Journal. 76, 823-840.
9
Diewert, W. E. (1971). An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function. The Journal of Political Economy, 79(3), 481-507.
10
Grebel, Thomas, (2009). Technological change: A microeconomic approach to the creation of knowledge, Structural change and Economic Dynamics, 20, 301-312.
11
Gujarati, Damodar N. (2004). Basic Econometrics (4th ed.). McGraw-Hill.
12
Hart, Rob, (2013). Directed technological change and factor shares, Economics Letters, 119, 77-80.
13
Hayami, Y., & Godo, Y. (2005). Development Economics. Oxford University Press.
14
Iranian statistical center. Iranian report on industrial workshops, various issues.
15
Intriligator, M. D. (1965). Embodied Technical Change and Productivity in the United States, 1929-1957. Review of Economics and Statistics, 47, 65-70.
16
Jorgenson, D. W. (1966). The Embodiment Hypothesis. Journal of Political Economy, 74, 1-17.
17
Kant, S., & Nautiyal, J. C. (1997). Production Structure, Factor Substitution, Technical Change, and Total Factor Productivity. Canadian Journal of Forest Research, 27, 701-710.
18
Krysiak, Frank C., (2011). Environmental regulation, technological diversity, and the dynamics of technological change, Journal of Economic Dynamics and Control, 35, 528-544.
19
Mattalia, Claudio, (2013). Embodied technological change and technological revolution: Which sectors matter? Journal of Macroeconomics, 37, 249-264.
20
McCarthy, M. D. (1965). Embodied and Disembodied Technical Progress in the Constant Elasticity of Substitution Production Function. Review of Economics and Statistics, 47, 71-75.
21
Napasintuwong, O., & Emerson, R. D. (2002). Induced Innovations and Foreign Workers in U.S. Agriculture. Selected paper prepared for presentation of the American Agricultural Economics Association Annual Meeting, Califonia.
22
Napasintuwong, O., & Emerson, R. D. (2003). Farm Mechanization and the Farm Labor Market: A Socioeconomic Model of Induced Innovation. Selected paper prepared for presentation of the Southern Agricultural Economics Association Annual Meeting. Mobile, A Labama.
23
Nordhaus, William D. (1969). An Economic Theory of Technological Change, American Economic Review, 59, 18-28.Peretto, Pietro F., (1999). Industrial development, technological change, and long-run growth, Journal of Development Economics, 59, 389-417.
24
Rasmussen, S. (2000). Technological Change and Economies of Scale in Danish Agriculture. The Royal Eterinary and Agricultural University KVL, Copenhagen.
25
Romer, P. M. (1990). Endogenous Technological change. Journal of Politiocal Economy, 98, 71-102.
26
Roshef, Ariell, (2013). Is technological change biased towards the unskilled in services? An empirical investigation, Review of Economic Dynamics, 16, 312-331.
27
Salter Wilfred, E. J. (1960). Productivity and Technical Change, Cambridge University Press.
28
Schafer, Andreas, (2014). Technological change, population dynamics, and natural resource depletion, Mathematical Social Sciences, 71, 122-136.
29
Solow, R. M. (1957). Technical Change and the Aggregate Production Function. Review of Economics and Statistics, 39, 312-320.
30
Solow, R. M. (1962). Technical Progress, Capital Formation, and Economic Growth. American Economic Review, 52, 76-86.
31
Stevenson, R. (1980). Measuring Technological Bias. American Economic Review, 70, 162-173.
32
ORIGINAL_ARTICLE
Effects of International Barriers and Limitations on Natural Gas Production in Iran
The enforced international limitations and sanctions against Iran have affected all stages of natural gas extraction from gas reservoirs. In this study, the effects of various situations on natural gas extraction from the Iranian operating oil reservoirs have been examined. Thus, this study aimed to study the role and effects of imposed international sanctions on Iran’s gas extraction and production. Outcome of the study provides appropriate solutions to recognize such situation and cope with the resulting circumstances. Regarding methodology of the research, quantitative data were collected and analyzed by using the statistical panel model. Results show that gas extraction from natural gas reservoirs has been decreased significantly in the period of international sanctions and limitations were imposed. To achieve the previous desired gas extraction level according to the initial developing plans and the existing potentials as well as to be able to cope with hardship of international circumstances, it is necessary to improve implementation system of the respective projects, attain technological knowledge and take serious steps towards resilient economy to enhance the domestic abilities through knowledge-based advancement especially in producing strategic equipment and goods required by such a large scale projects while the existing internal rules and regulations should be reformed and be more flexible.
https://pbr.put.ac.ir/article_58053_c379eaf29d58b9eff999b70c00a157a6.pdf
2017-11-01
29
39
10.22050/pbr.2017.58053
Natural gas
Gas Reservoirs and Production
International Sanctions
Ghasem
Bolu
gh.boulu@atu.ac.ir
1
Assistant professor, Accounting and Management Department, Allameh Tabataba’i University
AUTHOR
Touraj
Jahan-Ara
nagmezoghi@gmail.com
2
PhD. Student in Oil & Gas Economics and Finance, Allameh Tabataba’i University
LEAD_AUTHOR
Energy Information Administration (EIA) Beta report, 19th June 2015, https://www.eia.gov/beta/international/analysis.cfm?iso=IRN, last accessed: 15 July 2017.
1
Global Gas Flaring Reduction (2014), “Flared Gas Utilization Strategy; Opportunities for Small-Scale Uses of Gas,” The World Bank. PP: 1-129.
2
Broad, M.J., and Javadi, S.M. (2009), "Modelling a successful performance measurement system", Journal of Business and Economic Review. Institute of Management Sciences, 1, (1), pp. 29-39.
3
Derakhshanno, Masoud (2014), “National Interests and Utilization Policies of Oil and Gas Resources,” Majles and Pajouhesh, Vol. 34, 9th year, pp. 13-65.
4
Bagheri, Ali (2011), “The Role of OPEC in the Future Market of Various Energies,” Iranian Economic Research Quarterly, Vol. 46, pp. 1-18, spring.
5
Hausman, J. (2013), “Non-neutrality of the Proposed Resource Super Profits Tax,” Australian Economic Review, 44:3, pp. 239–44.
6
Nguyen, C.C., Bhatti, M.I. (2012). Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam. Journal of Inte`rnational Financial Markets, Institutions & Money, 22, 758–773.
7
Hufbauer, G. C., and Schott Jeffrey J. (2012). "Will the World Trade Organization Enjoy a Bright Future?" Policy Briefs PB12-11, Peterson Institute for International Economics.
8
Hufbauer G., Schott J., Elliott K. and Oegg B. (2007), “Economic Sanctions Reconsidered, 3rd edition, Peterson Institute for International Economics.
9
Dorri, Behrouz and Hamzei, Ehsan (2010), Determining a Strategy in response to Risk Management, Industrial Management Journal, in Persian, issue 4 , pages 77-94.
10
Mirmoghadam, M. and Ghazinoory, S. (2017), “An institutional analysis of technological learning in Iran's oil and gas industry: Case study of south Pars gas field development”, Technological Forecasting and Social Change, Volume 122, September 2017, Pages 262-27.
11
Derakhshanno, Masoud (2011), “Energy Security and Future Developments of Oil and Gas Markets,” Rahbord Quarterly, Center for Strategic Research, Vol. 64, pp. 159-188.
12
ORIGINAL_ARTICLE
The Impact of ERP Implementation on Financial Processes: A Case Study
In this research we try to investigate impact of Enterprise Resource Planning (ERP) implementation on the effectiveness of financial processes in PETROPARS Ltd as a case study in petroleum industry. In this regard, three indicators of Ahituv and Neumann model containing time, content, and format have been utilized to compare traditional systems used in PETROPARS Ltd before implementation of ERP/SAP system. For this purpose, four hypothesis related to financial processes were developed and data collected from 101 employees related to financial processes in PETROPARS Ltd. There were 10 research questions related to the three indicators of Ahituv and Neumann model. Research findings show that ERP/SAP implementation has a positive and significant impact on financial processes effectiveness compared to the traditional systems previously used (before ERP/SAP system) in PETROPARS Ltd. This research contributes to the body of knowledge for investigation and documentation of actual results of ERP implementation in the petroleum industry of Iran. A phenomenon that has not been previously touched.
https://pbr.put.ac.ir/article_58051_eb7bb986236e6125be2097276b0b5a8f.pdf
2017-11-01
40
48
10.22050/pbr.2017.58051
ERP system
Financial Processes Effectiveness
Ahituv and Neumann Model
PETROPARS Ltd
Ali Mohammad
Ghanbari
amgh364@yahoo.com
1
assistan professor, accounting department, petroleum faculty of tehran, petroleum university of technology
LEAD_AUTHOR
leila
Soleimani
soleimanileila24@yaoo.com
2
MA student in Finance, Petroleum University of Technology
AUTHOR
References
1
Energy Information Administration (EIA) Beta report, 19th June 2015, https://www.eia.gov/beta/international/analysis.cfm?iso=IRN, last accessed: 15 July 2017.
2
Global Gas Flaring Reduction (2014), “Flared Gas Utilization Strategy; Opportunities for Small-Scale Uses of Gas,” The World Bank. PP: 1-129.
3
Broad, M.J., and Javadi, S.M. (2009), "Modelling a successful performance measurement system", Journal of Business and Economic Review. Institute of Management Sciences, 1, (1), pp. 29-39.
4
Derakhshanno, Masoud (2014), “National Interests and Utilization Policies of Oil and Gas Resources,” Majles and Pajouhesh, Vol. 34, 9th year, pp. 13-65.
5
Bagheri, Ali (2011), “The Role of OPEC in the Future Market of Various Energies,” Iranian Economic Research Quarterly, Vol. 46, pp. 1-18, spring.
6
Hausman, J. (2013), “Non-neutrality of the Proposed Resource Super Profits Tax,” Australian Economic Review, 44:3, pp. 239–44.
7
Nguyen, C.C., Bhatti, M.I. (2012). Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam. Journal of Inte`rnational Financial Markets, Institutions & Money, 22, 758–773.
8
Hufbauer, G. C., and Schott Jeffrey J. (2012). "Will the World Trade Organization Enjoy a Bright Future?" Policy Briefs PB12-11, Peterson Institute for International Economics.
9
Hufbauer G., Schott J., Elliott K. and Oegg B. (2007), “Economic Sanctions Reconsidered, 3rd edition, Peterson Institute for International Economics.
10
Dorri, Behrouz and Hamzei, Ehsan (2010), Determining a Strategy in response to Risk Management, Industrial Management Journal, in Persian, issue 4 , pages 77-94.
11
Mirmoghadam, M. and Ghazinoory, S. (2017), “An institutional analysis of technological learning in Iran's oil and gas industry: Case study of south Pars gas field development”, Technological Forecasting and Social Change, Volume 122, September 2017, Pages 262-27.
12
Derakhshanno, Masoud (2011), “Energy Security and Future Developments of Oil and Gas Markets,” Rahbord Quarterly, Center for Strategic Research, Vol. 64, pp. 159-188.
13
ORIGINAL_ARTICLE
Identification and Prioritization of the Organizational Capital Attributes in the Petroleum Industry in Iran using Grey Systems Theory
The organizational capital is one of the important components of the intellectual capital. the organizational capital is an infrastructure in term of organizational successes so that it plays a vital role in achieving the goals of organization. Managing and directing “Organizational Capital” entails identification and measurement of its attributes. Although the literature on the intellectual capital is rich, the review shows that few researches have studied the organizational capital models and the related attributes. Hence, at first, this study aims to collect the organizational capital attributes through reviewing the literature and classify them as a comprehensive model. Then, a Multiple Attribute Decision Making (MADM) approach in uncertanity situation has been utilized in order to prioritize and rank the classified attributes by gathering the opinions of experts. In this study, the Grey systems theory has been used for the first time as a method to deal with uncertainty inherent in the in the organizational capital measurement. Whereas the presented comprehensive model can be applied in different situations and industries, it seems that this model may have different attribute weights with regard to the nature of organizations’ activity and internal and external conditions of the specified industry. Finally, the proposed methodology has been utilized in the petroleum industry in Iran and prioritization procedure and ranking results have been illustrated step by step.
https://pbr.put.ac.ir/article_57913_675a55785c3084e0ea0196a89e9e9e94.pdf
2017-11-01
49
57
Organizational Knowledge and the Learning Intellectual Capital
organizational capital
Grey Systems Theory
Iran petroleum Industry
Azadeh
Dabbaghi
dabbaghi@ut.ac.ir
1
Institute for International Energy Studies (IIES), Ministry of Petroleum, Iran
LEAD_AUTHOR
Afjeh, S.A.A; Ghaderpoor, H (2010). Investigating the Effect of Intellectual Capital Management on the Financial Performance of Organization, Public Management Quarterly, 1(3).
1
Attafar, A; Alinaghian, N (2008). A Review on Models Measuring Intellectual Capital, Modiriate Farda journal, 6 (20)
2
Badpar, F (2011). Investigating the Relationship Between Intellectual Capital Management and Knowledge Management and its Effects on the Occupational Development of Organizations, Allameh Tabatabai University, Public Administration, Human Resources.
3
Bontis, N. (1998), Intellectual capital: an exploratory study that develops measures and Models, Management Decision, Vol. 36 No.2, pp. 63-76.
4
Bozbura F. T. (2004), Measurement and application of intellectual capital in Turkey, The Learning Organization: An International Journal 11 (4–5) 357–367.
5
Bozbura T., Beskese A. (2007), Prioritization of organizational capital measurement indicators using fuzzy AHP, International Journal of Approximate Reasoning, 44; 124–147
6
Brooking, A. (1996), Intellectual Capital, Core Assets for the Third Millennium International Thomson Business Press, London.
7
CIC (2003), Modelo Intellectus: Medicio´n y Gestio´ndel Capital Intelectual, Centro de Investigacio´nsobre la Sociedad del Conocimiento (CIC), Madrid.
8
Cohen, S. and Kaimenakis, N. (2007) Intellectual capital and corporate performance in knowledge-intensive SMEs, The Learning Organization, 14, 241-262.
9
Dabbaghi, A; Malek, A.M; Shafiei, S (2010). Introducing GOCAI Gray Organizational Culture Assessment instrument and application in Structural Engineering and Productivity department of National Iranian Oil Company, Human Resource Management in the Oil Industry, No. 13.
10
Dabbaghi A., Malek, A.M., Aulizadeh A.R. (2009), “Evaluating the Quality of corporate mission Statement”, 5th international strategic management conference, Tehran.
11
Deng, J.L. (1989), “Introduction to Grey System Theory”, Journal of Grey System, 1(1), pp. 1–24.
12
Habibi, M; Jafari Farsani, J; Rashidi, M.M (2010). The Relationship between Intellectual Capital and Organizational Learning Capabilities in the Institute of International Studies in Energy, Human Resource Management in the Oil Industry; 4 (11): 59-75.
13
Lev, B. (2001) Intangibles: Management, Measurement And Reporting, Brookings Institution, Washington, DC.
14
Li Q.X., Liu S.F., (2008), “The foundation of the Grey matrix and the Grey input-output analysis”, Applied Mathematical Modelling 32, pp: 267–291.
15
Liu S.; Lin Y. (2006), “Grey Information Theory and Practical Applications”, Springer-Verlag London Limited.
16
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