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This paper aims to compare the application of most common forecasting techniques within financial data context such as regression analysis and artificial neural network, according to the most popular forecasting efficiency indices. Findings depicted that robust regression has an advantages over the least square regression and ANN, in financial data analysis because of outliers derived from business cycles. To this aim, relationship between earning per share, book value of equity per share and share price as price model and earnings per share, annual change of earning per share and return of stock as return model scrutinized implementing robust and least square regressions as well as ANN. Based on results, it can be concluded that the robust regression can provide better and more reliable analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression outperforms OLS and ANN, and can be recommended reaching more precise analysis in financial data context.

This paper aims to discuss the effectiveness of Six Sigma through balanced scorecard in banking industry. Six Sigma is a powerful analytical technique which yields a dramatic reduction in defects, errors, or mistakes in production and service processes. So it can be useful to find defectives in financial institutions and banks while it can accompany with BSC to approach all organization aspects. Reaching this aim, this paper tries to survey the effectiveness of Six Sigma through balanced scorecard aspects in one of Iranian pioneer banks in deploying aforementioned methodologies. Respondents have been extracted among bank experts with various backgrounds who experienced both techniques in practice for at least four continues years. Findings depict that customer aspect is the most appropriate aspect for implementing Six Sigma, while Learning; Financial and Internal process aspects are less suitable respectively. It is fruitful to declare that all BSC aspects are prone to use the advantages of Six Sigma in practice according to respondent’s points of view.

Till now, various models of the motion of nanobots have been submitted; the pioneer models, in spite of being exact in mathematics, had their own kind of problems. The most recent challenge is to describe a model whose attitude can be practical so that these motions can be estimated. Considering the massive uses of nanobots, the kinds of motions and velocities of these very small robots need to be studied in a more accurate way. In this essay, we tried to develop a three-dimensional model. The three dimensional model is based on 7 spheres and 6 arms and describes a kind of movement requiring 2 spheres at each arm. The velocity of each kind has also been evaluated. Furthermore, two kinds of three-dimensional movements have been issued and compared as well. That will result in the simplicity of the equations. By applying Oseen’s approximation in the Stokes’ equation, the velocity in various media has been calculated and modulated.

The present study examines the factors affecting the value relevance of accounting information for investors in the Tehran Stock Exchange over the period of six years. In this study, the effect of four factors; being profitable or loss generating, company size, earnings stability and company growth on the value relevance of accounting information have been studied. For this purpose Ohlson model and the cumulative regression analysis is used in order to examine the hypotheses and as the basis of data analysis T-test by Regression coefficient analysis is deployed. Findings confirm that these factors influence on the value relevance of accounting information for investors in Tehran Stock Exchange. Based on this study it can be concluded that accounting information of companies with profitability, small companies, companies with stable high earnings and companies with high growth rate are more value relevant in the Tehran’s stock market. These results are more useful for Iranian investors in their investing decision makings.

PurposeThe purpose of this paper is to develop a quantitative methodology for benchmarking process which is simple, effective and efficient as a rejoinder to benchmarking detractors who debate benchmarking is just a catch‐up process.Design/methodology/approachThe methodology developed for benchmarking here consists of three phases; define, analyze and results. Define phase concentrates on what to benchmark, whereas analyze and results concentrate on how to benchmark. Analyze phase is developed based on two popular mathematical programming techniques which are called technique for order preference by similarity to ideal solution (TOPSIS) and goal programming.FindingsThe developed benchmarking methodology is deployed in the case of business schools and results show its efficiency and effectiveness as well as its applicability to various business environments in implementation.Research limitations/implicationsThe main limitation here is necessity of collecting data about all the peers involved in benchmarking which indirectly restricts the number of peers in the benchmarking process.Practical implicationsBased on the TOPSIS that addresses the benchmark (what to benchmark) and the GP model that addresses the way to reach the benchmark, this methodology may be implemented as a solution procedure for business benchmarking process.Originality/valueThe novelty in this approach is that TOPSIS and GP are being used as a benchmarking techniques in a simple methodology which choose a non‐real benchmark that is more than all the peers involved. In that sense, this research work may be the first, where quantitative methodology for benchmarking is developed and rejoined to the benchmarking old criticize that debates benchmarking is just a catch‐up play.

Corporate social responsibility (CSR) is about the social expectations and responsibilities of business to its environment, which is highlighted recently in practice and theory. In today’s business era, there is an increasing emphasis on how to improve CSR among firms operations in order to be more institutionalized which will lead to more survival chance in addition to the need of being technically and economically efficient. A lot of studies are conducted to define the determinants of CSR among the firms, which is also the aim of the presented study. This paper to do so firstly defines the CSR determinants and then analyzes their significance among business students as future business leaders in Iran with its particular business environment. Findings implied employees' gender, age and living background have no impact on CSR, while employees' religiosity and Machiavellianism and relativism influence CSR significantly. Results for Iran are the same, in comparison to the other countries which were studied before and are useful for managers to plan for improving the CSR. Key words: Corporate social responsibility (CSR), relativism, religiosity, Machiavellianism, determinant.

This study aims to investigate empirically the valuerelevance of accounting information to domestic investors in Tehran stock exchange from 1999 to 2006. During the present research impacts of two factors, including positive vs. negative earnings and the firm size are considered as well. The authors used earnings per share and annual change of earnings per share as the income statement indices, and book value of equity per share as the balance sheet index. Return and Price models through regression analysis are deployed in order to test the research hypothesis. Results depicted that accounting information is value-relevance to domestic investors in Tehran Stock Exchange according to both studied models. However, income statement information has more value-relevance than the balance sheet information. Furthermore, positive vs. negative earnings and firm size seems to have significant impact on valuerelevance of accounting information., {"references":["Alford, A., Jones, J., Leftwich, R., Zmijewski, M., \"The relative\ninformativeness of accounting disclosures in different countries\",\nJournal of Accounting Research, 1993, Supplement 31: 183-229.","Amir, E., Harris, T.S., Venuti, E.K., \"A comparison of value relevance\nof US versus non-USGAAP accounting measures using Form 20-F\nreconciliations\", Journal of Accounting Research, 1993, Supplement 31:\n230-264.","Barth, M. E., Beaver, W. H., & Landsman, W. R. \"The Relevance of the\nValue Relevance Literature for Financial Accounting Standard Setting:\nAnother View\", Journal of Accounting and Economics, 2001, 31: 77-\n104.","Bauman, M.P., \"A Review of Fundamental Analysis Research in\nAccounting\", Journal of Accounting Literature, 1996, 15: PP 2-10.","Beaver, W.H., Financial Reporting: An Accounting Revolution,\nPrentice-Hall, Englewood Cliffs, NJ, 1998.","Benayed, M.R., & Abaoud, E., \"Value relevance of Accounting\nEarnings and the Information Contents: Empirical Evidence in Tunisian\nStock Exchange\", 2006, WWW.SSRN.COM.","Chen, J., Chen, S., & Su, X., \"Is accounting information value-relevant\nin the emerging Chinese stock market?\" , Journal of International\nAccounting Auditing and Taxation, 2001, 10(1): 1-22.","Collins, D. W., Maydew, E. L., & Weiss, I. S., \"Changes in the valuerelevance\nover the past forty years\", Journal of Accounting and\nEconomics, 1997, 24: 39-67.","Dumontier, P., Raffournier, B., \"Accounting and Capital Market: A\nSurvey of the European Evidence\", European Accounting Review, 2002,\nNO.11, PP: 119 - 124.\n[10] Easton, P., & Harris. T., \"Earnings as an Explanatory Variable for\nReturns\", Journal of Accounting Research, 1991, 29: PP 19-36..\n[11] Easton P.D., Sommers G.A., \"Scale and the Scale Effect in Market-\nBased Accounting Research\", Journal of Business Finance and\naccounting, 2002, NO.30, PP: 25 - 35.\n[12] Elliott, J., & Hanna, J.,\"Repeated accounting write-offs and the\ninformation content of earnings\", Journal of Accounting Research, 1996,\n34: 135-155.\n[13] Feltham, G.A., Ohlson, J.A., \"Valuation and clean surplus accounting\nfor operating and financial activities.\" Contemporary Accounting\nResearch, 1995, 11: 689-732.\n[14] Financial Accounting Standards Board, Statement of Financial\nAccounting Concepts No. 2, Qualitative characteristics of accounting\ninformation. FASB, Stamford, Connecticut, 1980.\n[15] Francis, J., & Schipper, K., \"Have Financial Statements Lost their\nRelevance?\", Journal of Accounting Research, 1999, 37: 319-324.\n[16] Gjerde Ø., Knivsflå K., Sættem F., \"The Value-Relevance of Financial\nReporting in Norway 1965-2004.\", 2007, WWW.SSRN.COM.\n[17] Harries, T.S., Lang, M. and Möller, H.P., \"The value relevance of\nGerman accounting measures: An empirical analysis.\" Journal of\nAccounting Research, 1994, 32: 187-209.\n[18] Hayn, C., \"The information content of losses\", Journal of Accounting\nand Economics, 1995, 20: PP 125-153.\n[19] King, R. D. and Langli, J. C., \"Accounting diversity and firm valuation.\"\nThe International Journal of Accounting, 1998, 33: 529-567.\n[20] Lev, B. and Zarowin, P., \"The boundaries of financial reporting and how\nto extend them.\", Journal of Accounting Research, 1999, 37: 353-385.\n[21] Ohlson, J., \"Earnings, Book Values, and Dividends in Equity\nValuation\", Contemporary Accounting Research, 1995, (spring): 661-\n687.\n[22] Sami, H., & Zhou, H., ., \"A Comparison of Value Relevance of\nAccounting Information in Different Segments of the Chinese Stock\nMarket\", The International of Accounting, 2004, 39: 403-427."]}

This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis., {"references":["Azar, A. and Momeni,M., Business statistics, 3th Edition, Samt\nPublications,Tehran, 2008.","Anderson, D. R. and Sweeney, D. J, Statistics for Business and\nEconomics, 7th edition, Williams, T.A., South western college, 1998.","Bamett, V. and Lewis. T, Outliers in Statistical data, Third ed., Wiley,\nChichester, 1993.","Chatterjee, S. and Hadi A.S., Regression analysis by example, 4th\nedition, Wiley, New Jersey, 2006.","Chatterjee, S. and Hadi A.S., \"Influential Observations, High Leverage\nPoints, and Outliers in Linear Regression\", Statical Science, Vol. 1, No.\n3, 1986, pp.379-416.","Chatterjee, S. and M├ñchler, M., \"Robust regression: A weighted least\nsquares approach\", Communications in Statistics ÔÇö Theory and\nMethods, No.26, 1997, pp.1381-1394.","Chen, C., \"Robust Regression and Outlier Detection with the\nROBUSTREG Procedure\", presented at SUGI, No.27, 2002, pp.265.","Cook, R. D., \"Detection of influential observations in linear regression\",\nTechnometrics, No. 19,1977, pp.15-18.","Easton, P., & Harris. T., \"Earnings as an Explanatory Variable for\nReturns\", Journal of Accounting Research, 1991, No.29, pp.19-36.\n[10] Field, C. and Zhou, J., \"Confidence intervals based on robust\nregression\", Journal of Statistical Planning and Inference, No.115, 2003,\npp.425 - 439.\n[11] Gujarati, D.N., Basic Econometrics, Third edition, Mc Graw-Hill\ninternational edition, 1995.\n[12] Hoaglin,D.C. and Welsch,R.E., \"The hat matrix in regressin and\nANOVA\", The American Statistician, No.32, 1978, pp.17-22.\n[13] Hodges J.L., Proc. Fifth Berkeley Symp. Math. Stat. Probab.,No.1, 1967,\npp.163-168. http: // www. 2sas . com / proceedings /sugi27/pdf.\n[14] Liang,Y.Z. and Kvalheim O.M., \"Robust methods for multivariate\nanalysis - a tutorial review\", Chemometrics and Intelligent Laboratory\nSystems, No.32, 1996, pp.1-10.\n[15] Martin, R. D. \"Robust Statistics with the S-Plus Robust Library and\nFinancial Applications\", Vol. 1 and 2, Insightful Corp Presentation, New\nYork, NY, 2002, No.17-18.\n[16] Neter, J. and Kunter, M. H., Nachtsheim, C. J. and Wasserman. W.,\nApplied Linear Regression Models, Third edition, Mc Graw-Hill, USA,\n1996.\n[17] Ohlson, J., \"Earnings, Book Values, and Dividends in Equity\nValuation\", Contemporary Accounting Research, (Spring), 1995,\npp.661-687.\n[18] Preminger, A. and Franck, R., \"Forecasting exchange rates: A robust\nregression approach\", International Journal of Forecasting, No.23, 2007,\npp.71- 84.\n[19] Rousseeuw P. J. and Zomeren B. C., \"A comparison of some quick\nalgorithms for robust regression\", Computational Statistics & Data\nAnalysis, Vol.14, 1992, pp.107-116.\n[20] Rousseeuw, P.J. and Leroy, A.M., Robust Regression and Outlier\nDetection, Wiley, New York, 1987.\n[21] Rousseeuw, P.J. and Van Driessen, K., \"Computing LTS Regression for\nLarge Data Sets\", Technical Report, University of Antwerp, 1998.\n[22] Rousseeuw, P.J., \"Least Median of Squares Regression\", Journal of the\nAmerican Statistical Association, No.79, 1984, pp. 871-880.\n[23] Yaffee, R. A., \"Robust Regression Analysis: Some Popular\nStatistical Package Options\", Statistics, Social Science, and Mapping\nGroup,Academic Computing Ser, 2002.\nwww.nyu.edu/its/socsci/docs/RobustReg2.pdf."]}

In order to develop any strategy, it is essential to first identify opportunities, threats, weak and strong points. Assessment of technology level provides the possibility of concentrating on weak and strong points. The results of technology assessment have a direct effect on decision making process in the field of technology transfer or expansion of internal research capabilities so it has a critical role in technology management. This paper presents a conceptual model to analyze the technology capability of a company as a whole and in four main aspects of technology. This model was tested on 10 automotive parts manufacturers in IRAN. Using this model, capability level of manufacturers was investigated in four fields of managing aspects, hard aspects, human aspects, and information and knowledge aspects. Results show that these firms concentrate on hard aspect of technology while others aspects are poor and need to be supported more. So this industry should develop other aspects of technology as well as hard aspect to have effective and efficient use of its technology. These paper findings are useful for the technology planning and management in automotive part manufactures in IRAN and other Industries which are technology followers and transport their needed technologies., {"references":["Arasti M & Pakniat M, (2006), A Classification of Models for\nTechnology Strategy Formulation, EUROMOT conference Birmingham,\nUnited Kingdom.","Pall R., Farrukh C.J.P., Probert D.R. (2001), Technology management\nprocess assessment: a case study, international journal of operations and\nproduction management, Vol.21, No.8, PP.1116-1132.","Pall R., Farrukh C.J.P., Probert D.R. (2004), a framework for supporting\nthe management of technological knowledge, international journal of\ntechnology management, Vol.27, No.1, PP.1-15.","Panda H. & Ramanathan K. (1996), \"Technological Capability\nAssessments of a Firm in the Electricity Sector\", Technovation, Vol 16,\nNo. 10.","Farzipoor Saen.reza(2006), A decision model for technology selection in\nthe existence of both cardinal and ordinal data, Applied Mathematics\nand Computation, No.181, pp.1600-1608.","Chan, F.T.S, M.H. Chan and N.K.H. Tang(2000), Evaluation\nmethodologies for technology selection, Journal of Materials Processing\nTechnology, No.107, pp. 330-337.","Torkkeli, Marko, Markku Tuominen(2001), The contribution of\ntechnology selection to core competencies, Int. J. Production Economics\n, No.77, pp. 271-284.","Yap.C. And Wm Souder (1993), a filter system for technology\nevaluation and selection, Technovation 13 449-469.","Arbel.A, and Y. Shapira(1986), A decision framework for evaluating\nvacuum pumping technology, Journal of Vacuum Science &\nTechnology, Vol.4,No.2, pp.230-236.\n[10] Miles, D. (1995), Constructive Change, Managing International\nTechnology Transfer, International Labour Office, Geneva.\n[11] Porter, M.E. (1985), Competitive Advantage: Creating and Sustaining\nSuperior Performance. New York: Free Press.\n[12] Shamsuddin & Bititci (2006), Technological Competency Assessment\nProcess (TeCAP): A Case Study, EUROMOT conference Birmingham,\nUnited Kingdom.\n[13] Domie, Draaijer and Harry, Boer (1995), Designing market-oriented\nproduction systems: theory and practice, Integrated Manufacturing\nSystems, Vol. 6 No. 4, pp. 4-15.\n[14] Kline.s (2003), what is technology?, in philosophy of technology edited\nby Scharff and Dusek, Blackwell publishing.\n[15] APCTT (1989), Atlas Technology: A Framework for Technology\nPlanning, APCTT Publications.\n[16] Zeleny M. (1986), High Technology management, Human systems\nmanagement, vol.6, pp.109-120.\n[17] Radosevic S. (1999), International Technology Transfer and Catch-up in\nEconomic Development, Edward Elgar Pub.\n[18] Nonaka I., Takeuchi H. (2004), Hitotsubashi on knowledge\nmanagement, john wiley & sons.\n[19] Cowan, M., David, P., Foray, D., (2001), The explicit economics of\nknowledge codification and tacitness. Industrial and Corporate Change\n9, 211-253.\n[20] Johnson, B., Lorenz, E., Lundvall, B.-A(2002), Why all this fuss about\ncodified and tacit knowledge. Industrial and Corporate Change 11, 245-\n262.\n[21] Gregory M.J. (1995), Technology management: a process approach,\nProc IMechE, Vol.209, PP.347-356.\n[22] Ford D.& Saren M. (1996), \"Technology Strategy for Business\",\nInternational Thomason Publishing.\n[23] Chiesa V. (2001), R&D strategy and organization, imperial college\npress.\n[24] Irani, Z., Sharif, A.M. and Love, P.E.D. (2001), \"Transforming failure\ninto success through organizational learning: an analysis of a\nmanufacturing information system\", European Journal of Information\nSystems, Vol. 10 No. 1, pp. 55-66.\n[25] Remenyi, D., Money, A., Sherwood-Smith, M. and Irani, Z. (2000), The\nEffective Measurement and Management of IT Costs and Benefits, 2nd\ned., Butterworth Heinemann, Oxford.\n[26] Sharif, A.M., Irani, Z. and Love, P.E.D. (2005), \"Integrating ERP with\nEAI: a model for post-hoc evaluation\", European Journal of Information\nSystems, Vol. 14 No. 2, pp. 162-74."]}