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«VOLUM E 1 1, N UM B E R 1 I S SN 2 1 6 8 - 0 6 1 2 F L ASH DR I V E I S SN 1 9 4 1 - 9 5 8 9 ON L I N E T h e In s t it ut e f o r Bu s i n e s s an ...»

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2 1.962 (0.5)(0.5) = = = 384.16 2 0.052 Where: n= simple size; Z: desired confidence level 95% = 1.965; e: error, and p: represents success. As finite population has a formula that allows us to reduce some surveys applied to the total sample. The elements of this formula are specified according to the following: n0 is the result just obtained recently by considering an infinite population, N is the total population, and n is the total survey. Applying the

correction factor for finite population:

0 348.16(1979) = = = 344 + 0 − 1 1979 + 348.16 − 1 The collected data were obtained directly, through the application of a survey of 372 members of the UACA UAZ through a questionnaire of 24 multiple-choice and using the Likert scale. The questionnaire was applied in June 2013 to a position corresponding to the different segments that can be grouped existing players total stratified sample.

RESULTS AND DISCUSSION

In analyzing the results of social environmental factor we realize that the variables with greater results have to do with the questions listed below The question that the level of support between colleagues has as much help more frequently with 39%, this situation is repeated across sectors, programs and gender, varying only the percentages in each questioned case. Table 1 shows the frequencies obtained in first factor.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 407 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Table 1: Frequencies in Social Environment Factor

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In analyzing the results obtained with respect to decision factor we find that the results are as follows. Table 2 shows frequencies obtained to Decisions factor Table 2: Frequencies in Decisions Factor

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The main correlations were found between the variables listed below: With respect to factor called social environment all variables have correlations with significance level higher than 99% to each other. The same situation applies to the factor decisions. The first factor we have variables “Having the right means” and “the institutional means” have the highest correlation 0.734, variables “Avoid conflicts” and “avoid friction” has the value of 0.725, “aid” and “response to help” variables correlate of 0.678. Con respecto a las variables que integran el factor toma de decisiones encontramos las correlaciones más altas en las variables en las que la toma de decisiones es evaluada y ésta se hace de manera periódica con 0.740, la siguiente correlación en grado más alto la generan las variables que miden el cumplimiento de las metas y lo favorable de la toma de decisiones con 0.677 y la correlación tercera más alta la dan las variables el beneficio de las decisiones y la mejora de la escuela con 0.629.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 408 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 With respect to the variables that make up the factor decision making are the highest correlations in variables in which “decision making is evaluated”, and “this is done on a regular basis” with 0.740, the following correlation highest degree generate variables measuring “compliance with the targets” and “favorable decisions” in 0.677 and the third highest correlation variables “benefits” and “school improvement” with 0.629

CONCLUDING COMMENTS

In general we see that the first factor is mostly favorable responses that support the organizational climate Similarly the data found in the second factor Decision making most of the data are favorable organizational climate for most. Most favorable response data for the social environment is perceived fellowship members The answer less favorably to any variable is the flow of information that does not by favorable channels.

In the decision-making factor variable with mostly favorable data is the fulfillment of institutional goals In this variable factor less favorable data is the fact that decisions are taken collectively All variables have correlations with meanings under 1%, except variable responsibility is left to others for decision-making.

We can mention that do not have statistical evidence to reject the hypothesis that the social environment and decision-making factors positively contribute to organizational climate.

REFERENCES

Becker F (2007) Organizational Ecology and Knowledge Networks. California Management Review Vol.

49, No. 2 Winter 2007 Berenson M, Levine D (1996) Estadística básica en administración, concepto y aplicaciones. Prentice Hall Hispanoamericana. México. 1996 Bierly P, Gallagher S, Spendery J (2014) Innovation decision making in high-risk organizations: A comparison of the US and Soviet attack submarine programs. Industrial and Corporate Change, Volume 23, Number 3, pp. 759–795 doi:10.1093/icc/dtt026 Davis T (1984) The Influence of the Physical Environment in Oflices. Academy of Management Review.

1984. Vol. 9, No. 2, 271-283.

Hess J, Bacigalupo A (2013) Applying Emotional Intelligence Skills to Leadership and Decision Making in Non-Profit Organizations. Adm. Sci. 2013, 3, 202–220; doi:10.3390/admsci3040202 Kupritz V, Hillsman T (2011) The Impact Of The Physical Environment On Supervisory Communication Skills Transfer. Journal of Business Communication, Volume 48, Number 2, April 2011 148-185 Marques Miragaia D, Ferreira J, Carreira A (2014) Do Stakeholders Matter In Strategic Decision Making Of A Sports Organization? Revista de Administração de Empresas. São Paulo, V. 54, n. 6, nov-dez 2014, 647-658 Mykkänen M, Tampere K (2014) Organizational Decision Making: The Luhmannian Decision Communication Perspective. Journal of Business Studies Quarterly 2014, Volume 5, Number 4 Pfeffer J (2010) Building Sustainable Organizations: The Human Factor. Academy of Management Perspectives. February GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 409 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Tabak RG, Hipp JA, Marx CM, Brownson RC (2015) Workplace Social and Organizational Environments and Healthy-Weight Behaviors. PLoS ONE 10(4): e0125424. doi:10.1371/journal.





pone.0125424 Tjosvold D, Peng A, Chen N, Fang S (2013) Individual Decision-Making in Organizations:Contribution of Uncertainty and Controversy in China. Group Decis Negot (2013) 22:801–821 DOI 10.1007/s10726BIOGRAPHY Eduardo Alejandro Carmona is Dr in Administration by Universidad Juárez del Estado de Durango, currently works in Unidad Académica de Contaduría y Administración in Universidad Autónoma de Zacatecas as a Profesor Researcher and is chief of investigation departament.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 410 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

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This paper theoretically and empirically investigates the relationship between the number of product varieties and the extent of intra-industry trade (IIT). IIT provides more trade opportunities for countries in which differentiated products are produced. The model presented shows that the extent of bilateral IIT is higher the smaller the gap in the number of export varieties between two countries. The empirical analysis of Japan and twenty-five countries provides support for the theoretical model presented in this paper. The theory also shows that similar number of export varieties between two countries, that is, more IIT can be a tool to redress trade imbalance between two countries. The policy implication of the results is that promoting higher product variety will increase the opportunity for IIT regardless of country specific effects.

JEL: F00, F10, F14, F19 KEYWORDS: Export variety, Trade, Intra-industry trade

INTRODUCTION

This paper theoretically and empirically investigates the relationship between the number of product varieties and the extent of intra-industry trade (IIT). IIT provides more trade opportunities for countries in which differentiated products are produced, and has become to play a key role in the world in concert with the global economic integration.The model presented shows that the extent of bilateral IIT is higher the closer the number of export varieties between two countries. The empirical analysis of Japan and twentyfive countries for the electrical and optical equipment industry provides support for the theoretical model presented in this paper regardless of country specific effects. The theory also shows that more IIT can play a role in redressing trade imbalance between two countries. The remainder of the paper is organized as follows. The next section describes the theoretical model developed in this research. The empirical results are presented and discussed in the following section. The paper closes with some concluding comments.

Theoretical Model This section, at first, summarizes the measure method of product variety developed by Feenstra and Kee industry trade and product variety, which is the contribution of the paper. Assume that country c(c = (2004). The latter part of the section is used for theoretical discussion on the relationship between intra

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Equation (4) is equal to the ratio of the CES cost functions between countries 1 and 2. See Feenstra(1994), Diewert(1976), Sato(1976), and Vartia(1976) for details.

The weights ( ) in equation (4) is the revenue shares as follows.

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∑∈ ∈

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is ( ∩ ) = . Thus, the equations (8-1) and (8-2) are derived from equation (7).

∗ country c to the world (*) and exports from the world (*) to the world (*), the common set of goods exported

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Therefore, export variety of country c can be measured as the export value from country c to the world (*) relative to the export value from the world (*) to the world (*), which is called Feenstra and Kee (2004) method. This paper measures the number of export varieties of each country c extending Feenstra and Kee (2004) empirical method as in Equation (9)’ in the next section. In this paper, it is implicitly assumed that a certain portion η of the aggregate production is exported. The assumption is not explicitly explained in Feenstra and Kee(2004).This paper defines the degree of intra-industry trade (IIT) between countries a and b for product variety i as the value of trade overlap as follows Several previous studies such as Fukao, Ishido and Ito (2003); Greenaway, Hine and Milner(1995);

Fontagné, Freudenberg and Péridy(1997); Oguro, Fukao and Khatri(2008); and Oguro(2011) use the same definition of IIT. Another famous IIT measure is Grubel and Lloyd(1975) index.

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between IIT and product variety. Equation (10) can be developed as follows assuming and value of trade overlap is exactly the same between two countries. This paper now shows the relationship using equations (8) and (9). The paper defines exports from one country to another country as exports from an exporting country to the world times an importer’s share in the world market as shown in Equation (11Equation (11-1) can be rewritten as Equation (11-2) using two countries’ export variety.

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Consider the case = 1 using Equation (11-3). Equation (13-1) is the expanded form of Equation (11 3).

<

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Equations (13-1) and (13-2) show that the theory developed in this paper also suggests that similar number of export varieties between two countries, that is, more IIT can be a tool to redress trade imbalance between two countries.

EMPIRICAL RESULTS AND DISCUSSIONS

Table 1 shows subject countries analyzed in this research. Twenty-five countries are selected based on the Industry-specific Real Effective Exchange Rate database produced by International Macroeconomics

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Research Program at The Research Institute of Economy, Trade and Industry (RIETI). This paper empirically analyzes Japan and the twenty-five countries shown in Table 1.

Figure 1 shows the number of product varieties of Japan’s and the twenty-five countries’ exports in the electrical and optical equipment industry in 2012. The number of export varieties is measured using Equation (9)’, that is, the value of exports from country c to the world (*) relative to the value of exports from the twenty-six countries to the world (*). Equation (9)’ is the empirical measure, which is the application of Equation (9).

Table 1: Subject Countries

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Value of exports from 26 subject countries to the world (∗) Figure 1: Export Variety (Electrical and Optical Equipment, 2012) 0.350 0.300 0.250 0.200 0.150 0.100 0.050 0.000 Data source: Comtrade HS 1996 This figure shows the number of product varieties of Japan’s and the twenty-five countries’ exports in the electrical and optical equipment industry in 2012. The number of export varieties is measured using Equation (9)’.

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Figure 2: Plot of Export Variety and Intra-Industry Trade (Iit)(Electrical and Optical Equipment, 2012) 0.00

-8.00 -7.00 -6.00 -5.00 -4.00 -3.00 -2.00 -1.00 0.00

-0.50

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