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Acquiring of conditions for achieving a permanent mission of the organization of annual conferences, publishing journals and various forms of education.
Due to its central location, it attracts millions of visitors every year. This report will provide an in-depth presentation of the results pertaining to this overall study intertwined with relevant literature.
The population for the study included key stakeholders who are responsible for or own tourism businesses within the boundaries of the Peak District. A total of 53 stakeholders took part in the primary research from different tourism businesses.
Some use social media just to inform customers about their service offerings and to increase sales. The main reason being lack of knowledge with regards to the different social media platforms. Business owners realise that the advances in information and communication technology has brought both opportunities and challenges Munar, Gyimothy and Cai The impact and importance of social media within the tourism industry has come to the fore in recent years Sigala et al.
This is due to the fact that tourism social media is a relatively novel form of value creation, based on a fast evolving technological structure which is shaping tourism culture both on and off line Munar et al. Visit Britain identified that social media does in fact play an important role in relation to the decision process when planning a trip.
Multiple sources or strands of social media information can influence potential visitors Visit Britain, b so a social media strategy must be managed correctly. Therefore, this study presents a discussion pertaining to an analysis of the primary data conducted in relation to developing a best practice social media marketing model for rural tourism SMES in the Peak District, England.
The following sections will highlight the results of the data from an SPSS analysis. The tables in the appendices indicate the frequency of responses in terms of numbers, percentage, valid percentage and cumulative percentage and the number of missing responses. In the case of this study, there are several basic concepts which are operationalized in the stakeholder survey which include:. The sections will be divided into the basic concepts which were operationalized in the survey.
Each respondent indicated whether they owned or worked in a tourism business within the boundaries of the Peak District. Tourism suppliers act as providers of services which are consumed by tourists, as Inskon and Minnaert ;p. With regard to categorising the types of businesses which the respondents own or work in, the primary data indicates See Table 1. Bhatia indicates that a large number of tourism employment stems from direct service jobs, primarily from the accommodation sector with other employment coming from travel intermediaries, supply services and travel enterprises.
Having ascertained that Therefore, Figure 1. As the respondents could tick more than one response a multiple response variable is used, therefore, the percentage of cases figures are used from Table 1. These percentages are not surprising, of the 8, International visitors who visited Central and Northern England in , As Figure 1. Another Having previously indicated that With regards to the types of visitor attraction, Figure 1.
While Main attractions will include heritage centres Ramsey et al. In relation these visitor attractions it is evident from Figure 1. Creating viable visitor attractions in rural regions is a major task Fyall et al. It must be stated however, that care must be taken in terms of increasing visitor numbers, as Singh indicates, increasing numbers often results in complex management.
Figure 1. To further analyse the visitor numbers with those attractions who are fee paying a descriptive frequency was carried out. Location of businesses in the Peak District Frequency.
The most common location was Matlock where 9. This is closely followed by Bakewell at 7. Monyash, Tideswell and Winster each represent 3. Employees including the respondent who are currently employed in each organisation including those who work 5 or more hours a week and excluding contractors and those not directly employed.
As Table 1. As previously indicated tourism accounted for 3. The Office of National Statistics highlight that the cumulative growth in employment in the tourism industry in the UK between and was 5. Whereas, cumulative jobs in the non-tourism sector experienced 2. Thus, tourism plays an important role in relation to economic growth in the UK.
It is evident from the cross tabulation between the number of employees and the different business categories in Figure 1. In contrast, the visitor attraction sector which accounts for The Office for National Statistics indicated that employment in the accommodation sector showed an increase of 8. With regard to the age groups of the respondents Figure 1. Getz highlighted that the main goals of owners and managers of small businesses in rural areas were lifestyle, stimulus and business opportunity.
A cross tabulation between the age group of the respondents and the business category Figure 1. There are no individuals aged between working in the visitor attraction sector while this age group accounts for Many social media sites offer different platforms for users to communicate with one another Brunty and Helenek, Use social media to stay in contact with friends and family. Individuals utilise social networking in general to stay in touch with friends and family or to find online communities who share the same interests Albarran, ; Minton, ; Waters and Lester, In relation to using social media for personal use Figure 1.
Length of time using social media for personal or business use. The emergence of social media began in the early days of the internet as people began sharing information and in turn communicating with one another. As technology matured platforms were developed and businesses began to realise that by embracing social media they could align themselves or their business to consumer needs Taprial and Kanwar, It is evident form Figure 1. When cross tabulating between the different business categories and social media usage over time Figure 1.
This is not surprising as Mehta ; p. Utilising information communication technologies such as social media platforms adds an additional layer in relation to the marketing of a visitor attraction Staiff et al. The research aim was translated into questions which were analysed in this section.
As previously stated, tourism social media is based on a fast evolving technological structure Munar et al. The choice of social media platform is often influenced by the ease of use and affordability Stefanoug, Business owners realise that the advances in information and communication technology has brought both opportunities and challenges Munar et al.
Albarran, A. Routledge : Oxon. Ali, A. Routledge: England. Augostino, R. LuLu: USA. Belew, S. John Wiley and Sons: New Jersey. Benckendorff, P. Cabi: UK. Bertrand, S. Briassoulis, H. Brunty, J. Routledge: Oxon. Chambers, D. Palgrave Mac Millan: New York. Chaney, P. Booktango: USA. Clapperton, G. Cohen, S. Finton, J. Funk, T.
A Press: USA. Fyall, A. Butterworth-Heinemann: Oxford. Getz, D. Gould, M. Liturgical Press: USA. Hoffman, K. Cengage Learning: USA. Inskon, C. Sage Publications: London. Ivanovic, M. Prentice Hall: India. Jones, K. Jue, A. A Wiley: San Francisco. Kaser, K. Cengage Learning:USA.
Kelly, N. Pearson Education: USA. Lardi, R. AG: Zurich. Lee, I. Business Science: USA. Mancini, M. Maya, L. Mehta, G. Xlibris Publishing: USA. Mensah, I. Xlibris: USA. Middleton, V. Sterling Publishers: New Delhi. Butterworth-Heinemann: UK. Minton, E. Munar, M. Emerald: UK. Ramsey, D.. Koster, R. Rural Development Institute:Canada. Reed, J. FT Delivers: New Jersey. Rich, J. Entrepreneur Press: USA. Safko, L. Sandy, D. Harrim House: Great Britain. M, Christov, E and Gretzel, U.
Ashgate Publishing: England. Singh, S. World Scientific: USA. Staiff, R. Routledge: USA. Stay, J. John Wiley and Sons: UK. Stefanoug, E. European Conference on Social Media. Sumarjan, N. CRC Press: India. Swarbrooke, J. Taprial, V. APS Publishing: India. Taylor, G. Global and Amp: USA. Thomas, R. Elsevier: UK.
T Social Media Secret Tactics. Fresh Mind Research. Visit Britain International Passenger Survey. Visit Britain a Economic Facts and Figures. Visit Britain b Information and Inspiration. Wang , Y and Pizam, A. Waters, J. Xiang, Z and Tussyadiah, L. Springer Publishing: Switzerland. A local search metaheuristic works on a single solution and tries to improve it iteratively.
Tabu search is one of the most famous local search, where at each iteration, a neighbor solution is generated from the current solution by performing a specific modification called a move on the latter. In contrast with most of the existing literature, the goal of this paper is to present tabu search approaches where different neighborhood structures i. The discussion is illustrated for various operations management problems: truck loading, job scheduling, inventory management, and dimensioning of assembly lines.
For these difficult problems, one should prefer to quickly find a satisfying solution, which is the goal of heuristic and metaheuristic solution methods. There mainly exist three families of meta heuristics: constructive algorithms a solution is built step by step from scratch, like the greedy algorithm where at each step, the best element is added to the solution under construction , local search methods a solution is iteratively modified: this will be discussed below , and evolutionary metaheuristics a population of solutions is managed, like genetic algorithms and ant algorithms.
Only the context of local search methods is considered in this work. A local search algorithm starts with an initial solution and tries to improve it iteratively. Let N s denote the set of all neighbor solutions of s. The definition of a move, that is the definition of the neighborhood structure N, depends on the considered problem. Popular local search methods are the descent local search, simulated annealing, tabu search and variable neighborhood search.
In a descent local search, the best move is performed at each iteration and the process stops when a local optimum is found. Tabu search has a good balance between exploitation i. Indeed, to prevent tabu search from being stuck in a local optimum, when a move is performed, the reverse move is forbidden i. In most tabu search algorithms, only one neighborhood structure N is used. The motivation of using several neighborhood structures is the following.
A local optimum according to neighborhood structure N is a solution s such that there is no solution in N s which is better than s. Let N1 and N2 be two different neighborhood structures.
Obviously, if s is a local optimum according to N1, it may not be a local optimum according to N2. In other words, if a N1-move is not able to improve s anymore, then a N2-move may do it. Presentation of the considered problem P The French car manufacturer Renault daily faces a complex truck loading problem P , where items need to be placed in a truck while satisfying different constraints.
More than a thousand trucks are daily considered to deliver components to the car plants. As a single truck can deliver goods to different delivery points, classes of items are defined, where a class is associated with a delivery point.
Each problem instance contains the size of the truck and the various sizes of all the items that must fit in. The heights of the items can be ignored as they rely on complex factory constraints which are supposed to be already satisfied. At first sight, P seems related to a strip-packing 2D problem with rotation, which has been already covered by many research papers e. Therefore, meta heuristics are more than relevant. In addition, the classes must be placed in an increasing fashion from the front to the rear of the truck.
The truck size is a hard constraint to fulfill, as it is not allowed to exceed neither its length nor its width. A possible solution view from the top of the truck, with rear on the left. Because of the specificity of P e. Additional references on the topic can be found in Lodi et al. To solve P , Renault proposes a simple but efficient greedy heuristic denoted SG , and an advanced greedy heuristic called look-ahead greedy denoted LAG. An important aspect is the tradeoff between the computing time and the solution quality.
As LAG is fast, another metaheuristic is only relevant if it is fast and leads to improvements. SG builds a solution from scratch, and at each iteration, selects an item following different possible rules from a list L of nonalready inserted items, and adds it to the solution at minimum cost i.
This process stops when L is empty. In LAG, at each iteration, the algorithm tries each item j of L, and for each j, tries the next p parameter insertions following this possible insertion of j look-ahead process. At the end of the iteration, the item j that would involve the lowest cost in the next p iterations is selected and inserted at the best position. As before, this process stops when L becomes empty. The two methods perform restarts as long as a given time limit T is not reached.
When T is reached, the best generated solution is returned. Tabu search for P Working with encoded solutions is relevant to tackle P within the framework of local search methods. An encoded solution s is actually a list of elements. To build a solution and compute its quality, a decoding greedy algorithm DGA is performed on the encoded solution. It decodes the solution s into a real solution sreal, and then returns the total length of the truck load f.
To drive DGA, information some are mandatory and others are optional are carried in each element of s, and can contain the item identifier ID, mandatory , the class identifier C, mandatory , the item orientation O, optional , and the item side S, optional. DGA thus decodes the vector s into a real solution sreal by inserting in sreal the items from s in a FIFO order, and using the O and S information if provided while respecting the class constraint. If Oi or Si is not provided, DGA can decide by itself its value minimizing the augmentation of f , and thus owns more freedom.
The decoding process is illustrated on an example. Five items, initially oriented as presented in Figure 2, have to be placed in a truck. The corresponding decoded solution sreal is illustrated in Figure 3.
To generate this solution, DGA performs the following steps: it pops the first element ID1 and loads the corresponding item on the right side, without rotation. At that time, the next item, namely ID3, is loaded on the right with a degrees rotation. Then item ID2 is loaded on the left with rotation whereas ID5 is inserted at the best possible position tried by DGA while respecting the class constraint.
Figure 2: Items with initial orientations. N2 resp. N3 : move item j from position x to y, and switch to the opposite value its orientation O resp.
All the moves are performed while respecting the class constraint, and ties are broken randomly. The notation TS is simply used if it refers to common features of the four tabu search algorithms.
TS starts from an initial encoded solution where items are ordered by decreasing areas. Thus, for TS1, DGA can decide on its own the orientations and the sides while focusing on the smallest augmentation of f. Finally TS4 constraints DGA at the maximum level due to the complete information set contained in each component si of the vector s.
After each move, the tabu tenure tab is set to a uniformly distributed value between 25 and Results The above methods are compared on a set of 30 real benchmark instances provided by Renault. Tests were performed on an Intel Quad-core i7 3. The time limit T is seconds as validated by practitioners.
The results are summarized in Table 1. The first two columns indicate the values of n number of items and m number of classes of each instance. The next columns provide the same information for the other methods. The last rows give the average gap and computing time for each method. As SG and LAG are greedy constructive methods with restarts, it is not relevant to indicate their computing times.
Table 1. Computational results averaged over 5 runs for tabu search n. This is mainly due to the fact that LAG, with the parameter p, explores the impact of future possible insertions. Tabu search does not show amazing improvements on the greedy heuristics. TS1 is a method to avoid as it obtains poor results. This is probably due to the full freedom given to DGA to build a solution, when compared to the other algorithms.
This can be easily explained by the fact that TS4 does not let DGA any choice on the solution building. It is thus not surprising that TS4 is the fastest method regarding the time needed per iteration of the TS family, as DGA does not have to perform many tests to insert an item at the best position.
However, even if TS4 it is the fastest method per iteration, it is the slowest method to find its best solutions. As TS2 has the minimum gap on 29 instances over 30, it shows that O is the most important feature to carry in the encoded solution s. This can be explained in the sense that the orientation O has an important impact, as it clearly drives sreal by forcing the orientation of each item. Presentation of the considered problem P On the one hand, the range of problems consisting in selecting a subset of given jobs, and then in scheduling them in order to minimize rejections and some other costs, are called order acceptance and scheduling problems OAP.
It has been studied in various scheduling environments and a review is given in Slotnick, Such problems are particularly relevant in make-to-order production systems Zorzini et al. On the other hand, earliness and tardiness penalties have captured a lot of attention due to their correspondence with the just-in-time paradigm.
In Valente et al. When the production capacity of a company is overloaded, all received orders cannot be performed on time.
It then makes sense to reject some of them. The deadline corresponds to the point in time where the dissatisfaction associated with the rejection of the order, modeled by a rejection penalty, is equal to the dissatisfaction of delivering late. In other words, it is preferable to reject the order to allow the client to get its goods by another supplier. Usually, no job can be scheduled before its release date. It often corresponds to the date at which all the necessary raw material is ready to be used.
In contrast, release dates can be reduced in P if a quadratic earliness penalty depending on the starting time of the job is paid. Obviously, there is a lower bound and no job can start before its available date. The use of controllable release dates is relevant in practice. In counterpart, the manufacturer will pay a higher price to the involved supplier, which creates a win-win situation. The considered problem P can be formally stated as follows.
A set of n jobs is given, a subset of these jobs has to be selected and scheduled without preemptions but idle times are allowed on a single machine which can handle only one job at a time.
For each job j are given: a processing time pj, an available date Rj, a release date rj, a due date dj, a deadline Dj, and a rejection penalty uj. Let Cj and Bj respectively denote the completion time and the starting time of job j. In a feasible solution, each accepted i. The earliness and tardiness penalties are respectively defined as follows:. In Thevenin et al. In contrast with most scheduling objective functions, the one considered in P is not regular since earliness penalties are decreasing functions of the completion times.
When objective functions are regular, most algorithms solving a single machine scheduling problem consist in finding an ordered sequence of jobs. From such a sequence, a schedule is easily built by starting each job as early as possible. In case of non-regular cost functions, the insertion of idle times may decrease the costs. Therefore, building an optimal schedule when a production sequence is given is not as easy, and can be time-consuming.
Solution methods for P To solve P , a solution s is modeled by an ordered sequence s s of jobs, and a set W s of rejected jobs. Given such a solution representation, the timing procedure computes the starting and ending times of each job of s s , such that the objective function is minimized.
A greedy algorithm and a tabu search approach are now presented for P. In a second phase, jobs are taken one by one in the previously defined order, and inserted in the schedule at the position minimizing the costs. Note that a job is rejected if it is better than inserting it. The insertions are enforced, that is, other jobs can be deleted to maintain feasibility.
The associated repairing procedure is explained below. Four types of straightforward moves can be used for tabu search. All moves are enforced by using one of the repairing procedures described later. Five different tabu structures are designed. The first resp. As the cost function associated with each job is constant over the interval [rj, dj], it induces plateaus in the search space.
To escape from them, a tabu status is associated with the cost of the most recently visited solutions during t5 iterations: it is forbidden to visit a solution whose cost is tabu. As mentioned above, inserting a job may lead to an unfeasible solution due to available dates and deadlines constraints. To maintain feasibility, a repairing procedure must delete some jobs, and the choice of those jobs is a crucial point in local search methods for OAP.
Note that a Reinsert move can be performed by a Drop move followed by an Add move, and a Swap move consists of two Drops followed by two Adds. As dropping a job cannot lead to unfeasible solutions, a repairing procedure is only needed for the move Add.
Assuming that job j is inserted at position p, three repairing procedures can be compared. Remove randomly a job adjacent to position p until the insertion of j is possible.
Deleting jobs which are adjacent to the insertion position reduces the shifting of other jobs, which is expensive with quadratic penalties. Results To generate a set of instances for P , two critical values are used: the number n of jobs, and a parameter a which controls the interval of time in which release dates and due dates are generated. Basically, methods are likely to reject more jobs for small values of a.
One instance for each pair n, a is generated. The number of job families belongs to [10, 20]. Four methods are compared. Greedy refers to the greedy method combined with REPAIR2 the other repairing procedures do not provide good results when combined with Greedy. The results clearly show the superiority of tabu search over Greedy, as the gap obtained by the best tabu search is 9.
Tabu search with repairing procedure REPAIR3 obtains the best results for 8 instances over 12, however the results obtained for large instances are bad. Table 2. Computational results n 25 25 25 50 50 50 Presentation of the considered problem P In most inventory management problems, two types of decision have to be made at the manufacturer level: when and how much to order to suppliers.
It is assumed that setup, carrying and shortage costs are encountered during the year. Usually, inventory management models are characterized by stochastic demand and constant lead times.
The lead times for different orders are assumed to be independent, thus crossovers can occur. Therefore, the interactive effects between different cycles a cycle is defined as the time between two consecutive orders due to the occurrence of shortages are difficult to model.
Consequently, even if the annual approximated costs can be analytically computed with a mathematical function f, simulation of the lead times is the only way to compute the annual actual costs F of a solution. The considered problem P was motivated by the management of raw material at a sawmill in North America. Without loss of generality, consider a weeks planning horizon a time period is a week.
A solution P,S can be modeled by. The following reasonable assumptions are made. As a consequence, anytime P is modified, its associated S vector can be immediately updated with Compute S P. Moreover, simulation is required to compute the actual cost of a solution.
Thus, it makes sense to use meta heuristics. At the end, the overall best solution is returned to the user. For a fixed solution space X N , the following steps are performed. S1 Generate an initial solution P,S with N orders as equi-spaced as possible.
In TSf P,S , a move in the neighborhood structure N1 consists in putting an order earlier or later, but without changing the global sequence of orders. When an order is moved, then it is forbidden tabu to move it again for tab parameter depending on N iterations. The stopping condition is a maximum number Iter parameter of iterations without improvement of the best visited solution.
Instead of only providing a single solution, a set M containing m parameter promising local optima is provided promising according to the quality function f and a diversity function Div M.
To achieve this, additional ingredients are now defined. M is initialized with solutions randomly generated. Let P be a solution found by tabu search at the end of an iteration. The key idea is the following: P should replace a bad according to f solution of M which poorly contributes to its diversity Div M. Otherwise, P replaces P div. The resulting metaheuristic is summarized in Algorithm 2, relying on neighborhoods N1 modify P and N2 modify S. Results The experiments were performed on a PC Pentium 4 1.
As the method has to plan the orders for a whole year, the computing time is not an issue but an hour of computation is never exceeded. Each instance is characterized by its cost parameters the fixed setup cost A per order, the inventory cost h per unit per period, the shortage cost B per missing unit. For each period t is known the minimum resp.
From these three values, discrete triangular distributions can be easily constructed. Two types T1 and T2 of instances were generated according to two sets of lead time distributions, with 24 instances per type which differ according to A, h and B. The results are shown for three levels of B and for the two sets T1 and T2. Unsurprisingly, the potential benefit of the three methods augments as the seasonality is increased. Table 3.
Compact comparative results Set T1. As random events can occur, it is assumed that f can only be evaluated with a simulation tool. In such a context, within a local search framework, it is straightforward to define a move in three steps: A select a decision variable type i; B augment resp.
Within a tabu search framework, if a decision variable xik is augmented resp. I1 Which type of decision variable should be selected in A? I2 What is the amplitude w of the move in B? I3 How should the solution be adjusted in C? Each phase of type i can be performed during Iimax parameter uses of the simulator.
Working with phases i. To tackle issue I2 , and in contrast with classical tabu search approaches, the move amplitude w is dynamically updated during each phase of the search, within interval [wimin, wimax] parameters. This strategy allows to progressively focus on a promising region of the solution space. Issue I3 depends on the two other issues: if xik has been selected for a variation of w, the search process should focus on that decision and not modify as much the other decision variables of the same type in order to adjust the solution with respect to the associated constraint ci.
Thus, if xik was augmented resp. At each iteration, the neighbor solution can be the best among a set of N parameter candidate neighbor solutions. By dimensioning, one can refer to maximizing the production rate of a machine without successors, with respect to limited resources e.
Papers in the field are Dolgui et al. As random failures might occur on the machines, the software Arena is appropriate to evaluate a solution.
Two types of decision variables i. In Figure 4, a solution for a production network. The considered limitations are 60 for the total cycle time i. Indeed, it was tested on the production network associated with Figure 4, for which each machine has its own role: machines 1, 2, 4, 8 and 9 are classical processing machines, machines 3 and 5 are assembling machines, and machines 6 and 7 are disassembling machines.
The objective consists in maximizing the production rate of machine 5. In TS, a sample of all possible amplitudes is considered at each iteration to modify the decision variable. A pool of 50 initial solutions were generated, which have an average production rate of 1. DLS was able to reach an average production rate of 1. It was also observed that the quality of the resulting solution negligibly depends on the initial solution, which indicates that MNTS is a robust approach.
With a unified terminology, the success of some existing solutions methods is presented, which can be considered as belonging to the MNTS methodology. Problems in four domains are successively discussed: truck loading, job scheduling, inventory management, and assembly line dimensioning. It is important to mention that MNTS approaches were also successfully adapted in other fields e. The performance of a metaheuristic can be evaluated according to several criteria Zufferey, b : 1 quality: value of the obtained results; 2 speed: time needed to get good results; 3 robustness: sensitivity to variations in problem characteristics and data quality; 4 ease of adaptation: the ability to organize the method so that it can appropriately apply to different specific classes of problems; 5 ability to take advantage of problem structure considering that efficiency often depends on making effective use of properties that differentiate a given class of problems from other classes.
MNTS has a good behavior according to these criteria. Indeed, the following ingredients make it possible: 1 the best non-tabu move is performed at each iteration, which contributes to quality; 2 speed is mainly allowed by the incremental computation used to compute the value of any neighbor solution; 3 the use of various neighborhood structures enhances the robustness as well as the quality; 4 only one solution is handled during the search, which makes the method fairly easy to adapt and quick; 5 a dedicated in contrast with generic solution encoding allows to accounting for the structure of the problem, and it also contributes to quality.
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Zufferey, N. Metaheuristics: some Principles for an Efficient Design. Computer Technology and Applications, 3 6 , pp. Abstract The financial incentives aimed at both the fiscal balances and increasing productivity, which are primary objectives by developing countries.
This fact has an important systematic role as a systematic plan including the innovation strategies. All over the world, governments should be making financial development programs to the future while they keep the aimed fiscal balances and fiscal incentives in the related other macroeconomic facts.
In addition, developing countries, like Turkey, need taking lessons from EU countries because of their developed financial management and profitable growth, which is only occurred by means of the suitable fiscal allocation in the productivity market. Hence, the innovation strategies related to financial incentives in the scope of financial management have to being constructed by public decision makers oriented toward entrepreneurs. These structural approaches assist in GDP by the way of investments and market dynamics.
On the other hand, the financial applications can inhibit some the aimed utility of financial incentives, but the national tax structure and tax applications would not be suitable and congenial in the current fiscal events. However, the public financial management that is carried on by public fiscal approach must be revised and regulated aimed at entrepreneurships and national investments.
This means that financial incentives decisions have to be considered by financial market dynamics together with in the production and market entrepreneurs. It is appear that this approach results in fiscal enabling factors, which regard the fiscal knowledge capabilities in the innovation-cycle management.
The financial application plans that go into effects have to comprise benchmarking process consulting practical process oriented towards the long-term. There are structural questions which has been inevitably deal with in the financial applications process required because of the many studies have explored the effect of varying levels of incentive in many different tasks.
Undoubtedly, in the same process, these analyzes of structural about financial incentives are very important both the impact of trend and the course of financial inventions that are effected by public decision-making process drawing up the attention of probably business innovations throughout the extensive markets and sectoral dynamics1. We know well that the entrepreneurs assume to reach the expected utility of their investments via the aimed maximum profits within respect to the financial market dynamics together with the currency public financial politics, which should be taken into consideration macroeconomic balances in the same period.
In the point, sometime the market structure covering the financial market risks directed towards entrepreneurs can be in the opposite of liberal politic approaches or this phenomenon needs to public interventions aiming to regulate the structure of investment dynamics to bring into macro balance.
In the other hand,. Therefore, the entrepreneurs have to consider some important points in contact with public financial management including financial incentives in term of public applications.
Certainly, these mutual approachable relations have some important futures pertaining to ensure the financial dynamics in order to maintain the desired effects of financial incentives inclined towards entrepreneurs that plan for the investments in the complex market relationships.
Many developing countries simply do not possess the resources to make these kinds of investments and the importance of innovations capability for investment decisions depend on the largely location in public financial incentives4 as the divided financial values. Therefore, some the prime costs for assessment of investment risks can bring up sectoral awareness programs and policy coordination to improve understanding of sustainable investing throughout financial decision making process in term of entrepreneurs in order to establish the optimal investments conditions5.
In addition, investors and entrepreneurs need to more understand the level of financial management effects considered in the scope of public financial supports connecting with the macroeconomics objectives8.