A study on important factors influencing on the effectiveness of futures research , training and employment

Article history: Received January 10, 2013 Received in Revised form March, 26, 2013 Accepted 17 June 2013 Available online 18 June 2013 This paper presents an empirical investigation to find out important factors influencing future research, training and employment in Iranian scientific environment. The proposed model of this paper prepares a questionnaire consists of various questions and distributes it among some experts and analyze them using DEMATEL model. The model divides the 7 factors into two groups of cause and effect. On the cause side, there are four factors including preventing future unemployment crises, detecting future skills, finding future profitable/unprofitable job opportunities and prevention on having open position with no volunteer to work. On the effect factors, there are three factors influencing the future research including detecting the present risk associated with jobs, detecting necessary standards for future works and creating a balance between demand and supply.


Introduction
During the past two decades, there have been tremendous changes on information technology and scientific development in many areas.Many business units appear, which are solely relying on knowledge-based sciences.The development on knowledge-based organizations is so fast that there is a necessity to plan for future job requirements in advance.Obviously, there are different factors influencing future jobs such as job security, level of knowledge needed, etc.Therefore, in prioritizing possible factors influencing future jobs, we need to use multiple criteria decision making techniques.There are literally various techniques used for ranking different factors influencing future job opportunities such as analytical hierarchy process, data envelopment analysis, etc. Bottani and Rizzi (2006), for instance, used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for outsourcing activities.TOPSIS, originally developed by Hwang and Yoon in 1981, is a simple but sophisticated ranking methodology used in many real-world applications of science and engineering (Chang et al., 2010).The standard TOPSIS method chooses alternatives, which simultaneously have the shortest distance from the positive ideal solutions and the longest distance from the negative-ideal solutions.The positive ideal solution maximizes the desirable criteria and minimizes the undesirable criteria, whereas the negative ideal solution maximizes the undesirable criteria and minimizes the desirable criteria.
TOPSIS makes full implementation of attribute information, provides a cardinal ranking of alternatives, and does not need attribute preferences to be independent.To apply this technique, attribute values must be numeric, monotonically increasing or decreasing, and have commensurable units (Chen & Hwang, 1992;Yoon & Hwang, 1995).Hsu and Hsu (2008), for instance, used TOPSIS with an adatpation of entropy for optimizing the information outsourcing practices of primary care medical organizations.To handle outsourcing decision-making problems, Opricovic and Tzeng (2007) used an extended VIKOR method in comparison with outranking methods for an application of outsourcing problem.Amiri et al. (2011) presented a method to prioritize the locations of distribution centers in a supply chain using balanced scorecard to categorize the most important attributes affecting the location of distribution centers and the attributes are ranked based on decision making trial and evaluation laboratory (DEMATEL) method (Fontela & Gabus, 1976).The implementation of the proposed model of this paper was also applied for a real-world case study of oil company and the results are analyzed under different scenarios.Tseng (2009) in another assignment used a causal and effect decision making model of service quality expectation based on another extension of DEMATEL technique called grey-fuzzy DEMATEL approach.Zhou et al. (2011) determined critical success factors in emergency management using a fuzzy DEMATEL method.Klaas et al. (2001) performed another investigation on the effect of outsourcing human resources on increasing the efficiency of organizations.Lin et al. (2011) used DEMATEL method to explore the core competences and causal effect of the IC design service company for an empirical case study.
The organization of this paper first presents details of our survey characteristics in section 2 and section 3 demonstrates details of our results.Finally, concluding remarks are given in the last to summarize the contribution of the paper.

The proposed DEMATEL technique
As explained earlier, DEMATEL is a multi-criteria decision making technique for constructing a structural model involving causal relationships among complex factors (Gabus & Fontela, 1972, 1973).DEMATEL was originally developed by the science and human affairs plan of the Battelle Memorial Institute of Geneva.It was then implemented to handle the complicated and intertwined problem group.The infrastructure, based on the properties of objective affairs, can verify the interdependence among the variables/attributes and restrict the relationships, which preserve the properties of the system and development trend.The procedures of the DEMATEL method (Fontela & Gabus, 1976) as follows, Step 1: Generating the direct relationship matrix.We implement five scales for measuring the relationship among various criteria: 0 (no influence), 1 (low influence), 2 (average influence), 3 (high influence), and 4 (very high influence).Then, decision makers obtain sets of the pair-wise comparisons in terms of impacts and direction among criteria.Next, the initial data can be prepared as the direct-relation matrix in terms of an n × n matrix A where each element of a ij denotes the degree in which the criterion i influences the criterion j.
Step 2: Normalize the direct relationship matrix.Normalization is executed using the following, . (1) Step 3: Attain the total relationship matrix.Once the normalized direct relationship matrix X is built, the total relationship matrix T can be acquired by using Eq. ( 3), where I denotes as the identity matrix Step 4: Produce a causal diagram.The sum of rows and the sum of columns are summarized in vector D and vector R through Eqs.(4-6).Then, the horizontal axis vector (D + R) named ''Prominence'' is constructed by converting D to R, which discloses the relative importance of each criterion.Similarly, the vertical axis (D -R) named ''Relation'' is constructed by subtracting D from R, which may divide criteria into a cause-effect groups.Normally, when (D -R) becomes positive, the criterion belongs to the cause group and when the (D -R) becomes negative, the criterion represents the effect group.Therefore, the causal diagram is obtained by mapping the dataset of the (D + R, D -R), providing some insight for making decisions. . ( . ( where vector D and vector R, respectively represent the sum of rows and the sum of columns from total-relation matrix T = [t ij ] n×n .
Step 5: Calculate the inner dependence matrix.In this step, the sum of each column in total relationship matrix becomes 1 using the normalization method, and then the inner dependence matrix can be prepared.

The case study
In this paper, we perform an empirical investigation to find the important factors influencing future research in some Iranian firms.Table 1 demonstrates some of the most important factors affecting future research, which are determined based on Delphi investigation on some Iranian organizations.Help to detect future skills

Table 2
The relative effects of all 7 factors Total Next, we calculated I-M where the results are summarized in Table 3 as follows,

Table 4
The summary of direct effects In addition, the indirect effects are also calculated using Eq.(3) and Table 5 demonstrates the results of our computations.The implementation of DEMATEL model yields the causal and effect factors given in Table 3 as follows,

Table 7
Causal and effect factors Cause As we can observe from the results of Table 7, the implementation of DEMATEL has provided us some insight to detect cause-effect factors influencing future research and works.On the cause side, there are four factors including preventing future unemployment crises, detecting future skills, detecting future profitable/unprofitable job opportunities and preventing on having open position with no volunteer to work.On the effect factors, there are three factors influencing the future research including detecting the present risk associated with jobs, detecting necessary standards for future works and creating a balance between demand and supply.Fig. 1 demonstrates the summary of causal effects.factors into two groups of cause and effect factors.On the cause side, there were four factors including preventing future unemployment crises, detecting future skills, finding future profitable/unprofitable job opportunities and preventing on having open position with no volunteer to work.On the effect factors, there are three factors influencing the future research including detecting the present risk associated with jobs, detecting necessary standards for future works and creating a balance between demand and supply.

Fig. 1 .
Fig. 1.The cause and effect factors

Table 1
Important factors influencing future research

Table 5
The summary of indirect effectsNext, we present details of cause and effect relationships in Table6as follows,