Staffing and Recruitment of the United States Military Term Paper

Staffing and Recruitment of the United States Military - Term Paper Example Determining staffing needs and recruiting the best employees to fill these needs is the first step in effective HRM. If the first step is appropriately performed i.e. getting the best employees, the remaining functions are facilitated because of it. Every field has different staffing requirements as per the nature of the job. Recruitment in the military is not just a ‘jobs program.’ It’s a very serious business which includes the security and national interests of any nation. In this report, we discuss the staffing and recruitment requirements in military with particular emphasis on the US Military’s recruiting in light of human resource management. ______________________________________________ Introduction Human resource management has gained a lot of importance in the past few years. The concept of managing your company’s most valuable assets i.e. the people to get maximum results has gained a great deal of momentum and has become one of the integ ral functions of any organization (Schuler & MacMillan, 1984). An effective human resource system has various functions. Staffing and recruiting is first and very important function of effective HRM. This includes understanding the appropriate hiring needs of a particular organization and sector and appointing the best personnel according to these needs. Research shows that organizations which use sophisticated recruitment and selection strategies have positive effect on labor productivity (Koch & McGrath, 1996). Along with the respective education, various other criteria are present for each different job. The more difficult the nature of the job, the more complicated its recruiting requirements; Military is one these fields, and therefore it is not a simple business with simple job descriptions. It involves the defense of a country and a lot of care is required in military recruitment. Taking the U.S. Military as an example, let us discuss in detail about military staffing and rec ruitment. Overview of United States Military The military organizational structure of the United States Military, first known as the ‘War Department’, and later restructured as the ‘Department of Defense’, is headed by a civilian, The Secretary of Defense. Under this, there are three military departments, namely: The Department of the Army The Department of the Air Force The Department of the Navy These three departments are also headed by civilians. The secretary of defense and the three department heads, all are appointed by the President of the United States. The military has five branches, The Army, Air Force, Navy, Marine Corps, and Coast Guard. The first three are commanded by four star generals who report to their secretaries’ respectively. The Marine Corps also commanded by a four star general report to the Secretary of Navy as well. Now for the Coast Guard; these did not fall under the Department of Defense until recently, but now it is cons idered as a military service. This is because during periods of conflict, the President can transfer any or all assets of the Coast Guard to the Department of Navy. This is also commanded b

Factors afecting enzyme activity Essay Example for Free

Factors afecting enzyme activity Essay Below is a table of result which I obtained when conducting these experiments. Time (s) Amount of gas given off using 1cm3 of liver suspension and 1cm3 of hydrogen peroxide (cm3) 0. 0 Amount of gas given off using 2cm3 of liver suspension and 3cm3 of hydrogen peroxide(cm3)   Amount of gas given off using 1cm3 of liver suspension and 3cm3 of hydrogen peroxide(cm3) 0 Amount of gas given off using 1cm3 of liver suspension and 4cm3 of hydrogen peroxide(cm3). Amount of gas given off using 1cm3 of liver suspension and 5cm3 of hydrogen peroxide(cm3)   Amount of gas given off using 0. 5cm3 of liver suspension and 5cm3 of hydrogen peroxide(cm3) 0By looking at these results I can see that the best ratio I have tested so far is the ratio of 10:1 (5cm3 hydrogen peroxide to 0. 5 cm3 liver suspension) as it gives me a good spread of results and does not happen so quickly that I cannot take results from it. I obtained a 1cm3 syringe with which I could accurately measure to the nearest 0. 02 cm3 I used 0. 2cm3 of liver suspension and 5cm3 of hydrogen peroxide my results are in the table below. Time (s): 0. 0   Amount of gas given off using 0. 2cm3 of liver suspension and 1cm3 of hydrogen peroxide (cm3his ratio gave me good results which I can easily analyse so I have decided to use this ratio in my final experiment. I will use the concentrations 0%, 2%, 4%, 6%, 8% and 10% of liver suspension in my experiments as these should give me sensible results. Also, after conducting these experiments I have decided to use a 1cm3 syringe as this will give me accurate results and I have decided to use a gas cylinder rather than a measuring cylinder to collect gas as this is more accurate and easy to read. Fair testing In order to ensure that my results are reliable and accurate I will endeavour to make sure that there is only one variable in all of my experiments. Given that I only wish to test one variable I will make sure that I control all other variables that could affect the amount of gas produced in my experiments. Factors that could affect the results of my experiments are temperature, pH, apparatus and substances. To maintain a constant temperature throughout all my experiments I will conduct all of my experiments with the side arm boiling tube in an electronically heated water bath set at a temperature of 30i C. I have chosen this temperature as it is higher than room temperature so this will not affect it and it is not at a temperature high enough to denature the enzymes. To control pH I will add a pH buffer to the liver suspension buffering the pH at seven. I have chosen seven as it is neutral and therefore should not affect my experiments. I will also test the pH of the liver suspension at the start of each experiment using universal indicator if the paper turns light green I will know the pH is seven. To ensure that my apparatus is in working order I will ensure that I assemble the apparatus well and double check that all connections are well made and are therefore as air tight as possible. This will mean that any all gas produced goes into the gas cylinder and does not escape the apparatus so that my results are accurate. Given that all substance concentration that I will ask for will be mixed by the school biology department I cannot be sure exactly what is in them so unfortunately I will have to trust that all the substances I use are what they should be. Before using any of my substances I will stir them for ten seconds using a glass rod, this is to ensure that there is an even distribution of liver in my suspension so my results are accurate. Also, I will always use the same concentration of 10 vols hydrogen peroxide. Another factor which could affect my results is human error. When I am conducting my experiments it is possible that I could inaccurately measure my substances and I could start the stop clock at the wrong time. To accurately measure all of my substances I have chosen the most accurate apparatus to measure them with. I will measure as closely as is possible with the human eye measuring my liver suspension accurate to the nearest 0. 01cm3 and my hydrogen peroxide to the nearest 0. 1cm3. Also, when injecting my hydrogen peroxide I will inject it as quickly as possible so the full amount is in the boiling tube as soon as possible. I will start the stop clock at the very moment I inject the hydrogen peroxide so my results are accurate. Safety When conducting my experiments it is vital that I take the utmost care to be safe in the laboratory. I will wear safety glasses to protect my eyes as well as tucking my tie into my shirt to avoid it coming into contact with any apparatus or substances. Also, when dealing with hydrogen peroxide I will only remove the stopper from the bottle when I need to use it, I will extract it with a syringe and replace the stopper immediately. I will do this as hydrogen peroxide is highly dangerous and can cause the spontaneous combustion of clothing if applied in high concentrations. Also I will take great care when handling glass equipment and will ensure that all apparatus is properly clamped in place before beginning my experiments. I will be aware of others around me, tidying my apparatus away so it does not pose a danger to others. Also, I will wash my hands after conducting my experiments so as not to leave any dangerous substances on my hands which may be ingested if I put my hands near my mouth. MethodApparatus   A water bath   A trough A side arm boiling tube   A test tube rack   A gas cylinder   A stopwatch   A clamp and retort stand   1x 1cmi syringe   1x 5cmi syringe   A bung with a space for a syringe. Thermometer Diagram Procedure Before conducting any experiments I will ensure that the gas cylinder is full of water with no air bubbles by filling it then placing my thumb over it whilst submersing it in the trough. I will also ensure that the water bath is at a temperature of 30i C. After collecting the listed apparatus I will set it up as in the diagram above. I will then remove the bung and collect a liver suspension. I will begin with a concentration of 2% then proceed to use 4%, 6%, 8% and 10% as well as a control of distilled water. I will firstly take a sample of the solution in a test tube and universal indicator solution to it to in order to monitor the pH of the suspension. I will not add a buffer to control the pH as the chemicals in the buffer could interfere with the reaction and alter my results. I will measure out 0. 2cm3 of the suspension using a 1 cm3 syringe being as accurate as is possible with the naked eye when measuring. I will inject this suspension into the side arm boiling tube and replace the bung. I will then use the 5cm3 syringe to measure out 5cm3 of hydrogen peroxide. I will place the syringe into the bung and ensure that all my apparatus is air tight. I will then quickly inject all of the hydrogen peroxide into the boiling tube whilst simultaneously starting the stop clock. I will then record the amount of gas collected in the gas cylinder at ten second intervals for a period of two minutes. I will then repeat each experiment three times for each concentration of liver. I will record all my results on the table below. After collecting my results I will go on to test a different liver concentration until I have three sets of data for five different concentrations as well as a control. I will then tabulate these results and use the average results for each experiment to plot a graph of my results allowing me to analyse them easily. Taking the average of three experiments for each suspension will give me more reliable results as it will reduce the affect of anomalous results. Also, to ensure that all my tests are fair I will endeavour to use exactly the same conditions for each experiment by maintaining a constant temperature and by measuring all substances as accurately as is possible. I will also ensure that my apparatus is set up in exactly the same way for each experiment. When conducting my experiments I will consider all factors which I discussed in the fair testing section of this project making my results as reliable and accurate as possible. Observations On the next page is a table showing my results for the experiments described in my plan. I will go on to analyse these results in the analysis section. Analysis On the next page is a graph showing volume of gas produced against time for the six concentrations of liver suspension I used in my experiments. Although the graph of my results that I produced may at first appear not to agree with my predicted graph it does in fact indicate that my hypothesis was true. The lines on my graph are in the same positions as I predicted apart from the control graph which was higher than I expected. The lines did not flatten out as I predicted however, I believe this is due to the fact that I used a short time scale and the graphs would have flattened out had I used a longer time scale. I did not use a longer time scale as it would have been pointless given that I am investigating the rate of reaction of several different liver concentrations rather than the rate of reaction changing over time in one concentration of liver. On the whole I am happy with my graphs and I think they verify my hypothesis. My graph for the reaction rate of the 2% liver concentration followed this pattern well. Initially there was a dramatic increase in the amount of gas released. After the first ten seconds this slowed significantly, the amount of gas released still increased but it increased much more slowly and steadily. This was because the catalase broke down the hydrogen peroxide into water and oxygen slowly and steadily. The reason for the graph continuously going up slowly throughout the entire two minute period was because there was little catalase compared to hydrogen peroxide meaning that even by the end of the two minutes there was still a lot of hydrogen peroxide which had not been decomposed and so the enzymes were still working at the same rate. I think that if I had let the experiment continue for a longer time period the gradient of the graph would have flattened to zero as all of the hydrogen peroxide would have been decomposed. My line is approximately straight showing me that the reaction rate was roughly the same throughout the experiment however, the gradient is steepest at approximately sixty seconds meaning that the rate was highest here. In order to compare the rates of reaction of all my graphs I will need to calculate the rate of reaction for each graph. I will do this by dividing the amount of oxygen produced by the time taken to produce it. This will give me a rate in cmi of oxygen per second (cmi /s). I have chosen to take the rates of reaction from the points on the graphs after thirty seconds. Although it may seem that the maximum rate is at ten seconds and so I should take this rate I think this is due to the hydrogen peroxide I injected displacing air in the apparatus. I have chosen thirty seconds as I think this is sufficient time for the experiment to have settled after the initial burst of gas and it is not too late that some of the reactions had begun to slow down. The rate of reaction at thirty seconds for the 2% concentration suspension is as follows: 3. 83cmi 30s =0. 13cmi /s (2dp) I chose to take my rates to two decimal places as this is accurate enough for me to analyse my rate graph well and it is not so accurate that it would be difficult to plot on a graph. My graph for the rate of reaction of the 4% concentration liver suspension further supports my hypothesis. It has the same initial increase in gas from when I injected the hydrogen peroxide, it then continues to slope upwards but not as fast as before. As I predicted the 4% graph slopes up at a higher gradient than the 2% solution graph. This can be seen by simply looking at the graph. As predicted the gradient of each line is higher than the one that preceded it. This is because as the concentration of the suspension increases there is more catalase to break down the hydrogen peroxide into its component parts. I will now proceed to compare the gradients and rates of reaction for each concentration. On this graph the gradient of the line and the rate of reaction are the same thing as gradient=change in Y Change in X And rate of reaction=amount of gas produced (change in Y axis) Time (change in x axis) Below is a table showing the rates of reaction for each of my concentrations of catalase including the control experiment of 0% catalase. As before I will take the gradient of the line after thirty seconds. concentration of liver suspension amount of gas produced (cmi ) time (s) rate of reaction (cmi /s). On the next page is a graph of my results, I have plotted concentration of liver suspension against rate of reaction, this will better show my results and will help to verify my hypothesis. As you can see from the graph there is clearly a relationship of proportionality between the rate of reaction and the concentration of liver suspension as I predicted in my hypothesis. I have added a best-fit line to my graph to better show this trend. As I predicted the line is slopes upwards showing that as the concentration of liver suspension increases the rate of reaction increases, this is due to the fact that there was more catalase to collide with the hydrogen peroxide in the higher concentrations. One problem with these results is that my control experiment seems to have a rate of reaction however, this is simply due to the initial burst of gas at the start of the experiment when the hydrogen peroxide that I injected displaced air in my apparatus. Although this effectively means that all of my results are wrong I can still draw sensible conclusions from my graphs as every one of my results had the same displacement of air so when comparing my results this in fact has no effect. If there had been no displacement of air in my apparatus I think this line would have been straight and through the origin showing that rate of reaction and concentration of liver suspension are directly proportional. I will not attempt to subtract the gas displaced by the hydrogen peroxide from my results as this could further magnify any inaccuracies in my experiments and I do not need to in order to draw reliable conclusions from my graph. The conclusion I have come to by looking at my graphs is that my hypothesis was correct. I think that as the concentration of the liver suspension increases so does the rate of reaction proportionally.

Inter Firm Relationships in the Silicon Fen

Inter Firm Relationships in the Silicon Fen The attention that clusters have received from policy makers and academics has substantially increased in the last 20 years. Since Porters seminal work on The Competitive Advantage of Nations (1990) presented clusters as one of the determinants of the international competitiveness of nations and regions, many scholars have adopted and further developed his approach. Porter bases his arguments on what he describes as the globalization paradox, pointing out that despite the logical implications that the globalisation process might have in dismissing the relevance of regional factors, the most competitive firms in world are located in groups geographically concentrated in specific locations. That perspective contributed to attracting attention to the existence of characteristics tied to a local context that could not be accessed by firms positioned elsewhere, and more, to the positive effects that the concentration and the geographic proximity could exert on the firms competitiveness. However influential, Porters ideas were not the precursor to discussing the competitive outcomes originating from the geographic concentration of firms (Martin and Sunley, 2003). The roots of cluster theory go back to the industrial districts identified by Marshall (1890), who offered the first detailed description about the economic and social systems created as a result of the spacial concentrations of industrial activities. The Marshallian industrial districts were arrangements of small firms interconnected by commercial operations (buyers and sellers) and other firms engaged in the same or similar activities, that shared productive factors, such as the labour market, infrastructure and tacit knowledge (Becattini, 2004, p. 68). According to Marshals descriptions, a group of firms operating in one specific sector within a well-defined, concentrated and relatively small geographic area would experience higher levels of productivity and innovation, indeed the emergence of a fertile e nvironment for technical and organisational developments. Thus the local characteristics would enable the emergence of an industrial atmosphere that would increase the firms potential to acquire (especially tacit) knowledge, and create positive external economies accessible only to the firms located within the district (Asheim, 2003, p. 416). That perspective tried to evidence that firms geographically concentrated could accesses restricted positive exogenous benefits (exogenous to firms, but endogenous to the district), which would be an alternative to the scale economies achieved by a single (integrated) firm. Additionally, following some of the seminal ideas proposed by Marshall, it is possible to observe a significant number of economic geographers that also explored regional development using the spatial economic agglomeration to support their ideas. Some examples of concepts emerging from that theoretical trend are regional innovation milieux (Crevoisier, 2004), neo-Marshallian nodes (Amin and Thrift, 1992) and learning regions (Asheim, 1995). More examples can be found in Markusen (1996, p. 297), in which another three different types of industrial districts are described according to the firms configurations, internal versus external orientations and governance structures: a hub-and-spoke industrial district, which is concentrated around one or more dominant firms; a satellite platform, formed by a group of unconnected branches embedded in external links; and the state-anchored district, concentrated on one or more public-sector institutions. Despite the logical and robust assumption s found in many of those concepts, their influence and dissemination were not as successful as the more general cluster framework proposed by Porter. Martin and Sunley (2003) attributes the successful dissemination of the Porter concepts to the very general descriptions and delimitations that encompass a wide range of actors and many different structures. Following much of the concepts proposed by Porter, the description of advantages conferred on clustered firms associated with a general and structured analytical framework stimulated the development and dissemination of academic studies and subsidized the creation of supply-side competitiveness policies directed at structuring and supporting the development of clusters (Pitelis, 2010). That fact resulted in what Martin and Sunley (2003) describe as a policy panacea in the use of clusters as a standard (sometimes the unique) target for promoting competitiveness, innovation and economic growth. Moreover, in the last 20 years an increasing number of empirical studies in different countries and sectors have been observed, which aim to identify and discuss the competitive outcomes originating from the concentration of firms and other actors in the same location, for example: Brazil à ¢Ã¢â€š ¬Ã¢â‚¬Å" shoe manufacturing in the Sinos Valley (Schmitz, 2000); Spain à ¢Ã¢â€š ¬Ã¢â‚¬Å" the text ile and clothing industries in Catalonia (Porter, 1998); Taiwan à ¢Ã¢â€š ¬Ã¢â‚¬Å" electronic products at the Hsinchu Science Park (Chen, 2008); and the United States à ¢Ã¢â€š ¬Ã¢â‚¬Å" computer and information systems at the Silicon Valley (Saxenian, 1994). The large significant number of academic studies has resulted in a large number of definitions aiming to describe and establish an accepted cluster template (e.g., Enright, 1996; Swann and Prevezer, 1996, Rosenfeld, 1997; Porter, 1998) to support policy makers and academics has led to intense debates and controversial perspectives. Even though the concept of clusters has been increasingly widely disseminated and used by geographers, economists and policy makers, it has suffered from some conceptual confusion. Porter defines a cluster as a geographic concentration of interconnected companies, specialized suppliers, service providers firms in related industries, and associated institutions (for example, universities, standards agencies and trade associations) in particular fields that compete but also co-operate (1998, p. 197). However, Martin and Sunley (2003, p. 12) present consistent arguments that indicate the vagueness and superficiality of the concept proposed by Porter. Accordin g to their arguments, those characteristics make the concept of cluster means different things to different researchers and policy makers, creating problems for its proper use in the guidance of academics and governments. Those highly controversial aspects of the cluster theory have stimulated the continuous emergence of new concepts and definitions for clusters. Proposing a definition aiming to fill some of the gaps and failures found in extant cluster theory, Pitelis (2010, p. 5) defines clusters as geographical agglomerations of firms in particular, related, and/or complementary, activities, with a geographical dimension, that exhibit horizontal and/or vertical intra- and/or inter-sectoral linkages, which operate in the context of a facilitatory socio-institutional setting, and which co-operate and compete (co-opete) in inter-national markets. That definition tries offer to a more delimited approach that incorporates four major elements: geographical agglomeration, linkages, social-capital  [1]  and co-opetition (competition and cooperation). The use of those four elements in a single definition offers the possibility to cover the cluster characteristics using delimited criteria to identify and distinguish developed clusters from less complex geographical agglomerations of firms and institutions. Although it is possible to observe some level of ambiguity encompassing the clusters theory, the existence of links interconnecting local actors complemented by geographical dimensions constitute some of the main common points used to guide academics and policy makers with interesting by the competitive outcomes originating from clusters. Those characteristics have frequently been used as the starting point to understand the economic dynamics of clustered firms, putting emphasis on the levels of innovation and productivity emerging from the concentration of different actors in the same area. Suggesting conditional characteristics to the presence of competitive advantages obtained by firms inside clusters, Ketels (2004) considers that the positive economic effects originated from the geographical concentration will only take place if four critical characteristics are shared among firms and institutions: Proximity: they must be geographically close to allow the emergence of knowledge spillovers and to share the same common resources; Linkages: the necessity of similarities in their activities leading to the establishment of connections and synergies; Interactions: the social interactions developed among firms, clients, suppliers, research institutes, and so on, is what forms the social capital that becomes possible firms to achieve differentiated competitive performances; Critical mass: it is important to have a significant number of firms and institutions in order to create meaningful impacts on performance of the local actors. Those characteristics described by Ketels may be used to guide the identification and distinction between developed clusters (Pitelis, 2010) from incipient clusters (Schmitz, 1999) in order to dismiss some incorrect interpretations associated with the clusters dynamics. Considering that the presence of geographic concentration of firms in the same industry is strikingly common around the world (Porter, 1990, p.120), it is necessary the use of specific benchmarks to distinguish and classify different groups of firms geographically concentrated according to their specific characteristics (Gordon and McCann, 2000; Isbasoiu, 2006). Describing how the existence of local capabilities  [2]  create differentiated conditions for companies within real clusters, Menzel and Fornahl (2010) argue that clusters are essentially formed from path dependencies (Martin and Sunley, 2006), transaction costs economies (McCann and Sheppard, 2003) and small cognitive distances originating from spatial proximity (Maskell, 2001). Thus, that set of factors are expected to create a specific regional dynamics with influence on the firms economic performance. Taking into consideration the different stages of a cluster life cycle, and the misunderstandings related to the claims associated with the clusters and competitiveness, Schmitz points out that A group of small producers making the same or similar things in close vicinity to each other constitutes a cluster, but such concentration in itself brings few benefits (1999, p. 4), emphasizing that the mere presence of firms in a delimited area does not represent a source of value creat ion able to improve in a significant way the local economic performance. Following the arguments above, the differences between regional clusters and simple agglomerations (groups of firms) lie mainly on the interconnected nature and spatial proximity. Thus, clusters are characterized by intense collaborative networks and concentrations of collaboration and competition (co-opetitition) (Pitelis, 2010), conditions which offer significant opportunities and stimulate the emergence of regional competitive advantages (Steinle and Schiele, 2002). Complementarily, another critical characteristic observed within clusters is the diversity of actors. According to Porter (1990, 1998, 2000), an industrial cluster includes suppliers, consumers, related industries, governments, and supporting institutions such as universities. This way, the existence of a regional network formed by a significant group of interconnected local actors is one of the critical factors to understand the differentiated competitive performance of firms within clusters (Steinle and Schiele, 2002 ). Illustrating that argument, Saxenian (1994) observed that Hewlett Packard and other firms at the Silicon Valley had their performance improved by the development long-term partnerships with suppliers located geographically close. Moreover, based on that observation, Saxenian concluded that, especially in high-tech industries, the physical proximity represents a facilitator to the establishment of efficient collaborative arrangements required to create and manage complex products and services. 1.2 Evolutionary Stages of Industrial Clusters Despite the vast cluster literature, the number of academic works discussing the evolutionary patterns of clusters overtime is not so extensive. Some examples can be found in Pouder and John (1996), Klepper (2001, 2007), Wolter (2003) and Andersson et al., (2004), and despite the divergent perspectives, it is accepted that clusters follow a kind of life cycle comprised by different phases that significantly differ in their characteristics and influence on firms performance. Regarding the cluster dynamics, Pouder and John (1996) argue that comparative analysis between clustered and non-clustered firms during the industry life cycle reveal that firms within clusters outperform those geographically dispersed at the initial stages of development, and have a worse performance at its end. That fact suggests that the cluster life cycle is not just a local representation of the industry trajectory, but is a result from local peculiarities. The comparative analysis developed by Saxenian (1994 ) between the computer industry in Boston and Silicon Valley illustrates how different clusters belonging to the same industry are very likely to follow different trajectories (Menzel and Fornahl, 2010). Proposing a different perspective, Klepper (2001, 2007) suggests a model to demonstrate how the clusters life cycle is determined by some the industry patterns. Klepper analyzed the automobile, tire and television industries and observed that at the beginning of the industry life cycle it was not possible to observe clear geographic concentrations of firms, with most of the firms spatially dispersed. He observed that in those industries clusters started to emerge and develop according to the industry growth rates. Klepper argues that the local characteristics originating from the spacial proximity (e.g., intensive spin-off process) give the stimulus for the geographic agglomeration of the whole industry, not only for specific groups. At the time the industry growth rate reduces, the attractiveness to remain agglomerated will also decrease and the industry will become dispersed again. That model proposed by Klepper represents a Technology-Product- Industry (TIP) life cycle. The logic behind this model is on the impact that the evolution of products and innovations has on the size, number, and location of firms. Wolter (2003) criticizes the model proposed by Klepper arguing that the growth rate cannot explain the agglomeration process in all industries on equal basis. Moreover, Wolter disagrees with the determinist perspective proposed by the TIP model, once it neglects that mature industries can be reinvented by radical or incremental innovations of new products and process. Analyzing the economic performance of firms within clusters Pouder and John (1996) attribute to the existence of mental models and biased cognitive focus the characteristics responsible for shaping the movement through the clusters life cycle. Following that perspective, at initial stages the cluster dynamics creates an innovative environment that exerts positive impacts on the firms performance. However, overtime that initial condition is eroded by strong institutional pressures that create a homogeneous macroculture that acts inhibiting the innovative capacity of the firms within the cluster. As in the model presented by Klepper (2001, 2007), that trajectory proposed by Pouder and John may also be criticized by the determinism that ignores the possibility of adaptations or reconfigurations in order to avoid lock-ins and other negative effects. Considering the arguments proposed by Menzel and Fornahl (2010, p. 8) that very few clusters follow a rigid life cycle from emergence to growth and decline, it is expected that clusters evolve overtime according to the local dynamics created by economic and social interactions among firms and institutions. That dynamics may be influenced, but not strictly determined by industry patterns (Wolter, 2003). Following a generic and stylized trajectory, within successful clusters the local network formed by inter-firm connections will tend to be intensified overtime, with an increasing number of formal and informal interactions between the long-established companies and new the ones attracted to the cluster. Even though it is more conceivable to assume that the decisions adopted by firms and institutions are shaped by specific circumstances, a generic trajectory can be described following the stages illustrated in Figure 1. Figure 1: The cluster life cycle C:UsersLucasAppDataLocalMicrosoftWindowsTemporary Internet FilesContent.WordSem tÃÆ' ­tulo.jpg Source: Andersson et al. (2004, p. 43) Agglomeration: It is possible to observe the existence of a number of companies and other actors (e.g. banks, government agencies, universities, accountants, and lawyers offices) in a specific region working around the same or interrelated activities. Emerging cluster: Forming the embryo to the cluster some actors start to cooperation around some core activities, and start to realize the existence of common linkages. Developing cluster: The linkages are intensified by the emergence and attraction of new actors to the region, resulting in the creation of more interaction. In this context the development of inter-firm-cooperation becomes more evident through the development of joint efforts. The Mature cluster: This stage is configured by the presence of a certain critical mass of factors that consistently influence the competitive performance of the firms inside the cluster. The internal dynamics is characterized by the presence of an institutional environment, strong linkages, complementarities and the emergence of new firms through startups, joint ventures and spin-offs. Transformation: Indeed the process of continuous environmental change in markets, technologies, regulations and other process, to be successful a cluster have to innovate and adapt to these new conditions, other way stagnation and decay may affect the cluster dynamics. That process of change/adaptation may happen through the emergence of one or several interconnected clusters with focus in other activities, or by new configuration in terms of networks of firms and institutions. The presence of economic benefits for clustered firms described by authors like Schmitz and Nadvi (1999), Ketels (2004), Isbasoiu (2006) and Pitelis and Pseiridis (2006) are closely related to the stage of development that a cluster is experiencing. For example, an emerging cluster is not actually a cluster, since the small number of firms is not expected to present a high level of linkages and do not form a critical mass. Moreover, the absence of strong interdependencies such as labour mobility, spin-off, socioeconomic networks and intense exchange of good and services prevent the emergence of local capabilities. Thus, same considering that this stage constitutes the embryo that determines the future cluster orientation, at this point the firms are not expected to be strongly influenced by a complex local dynamics. Observing that fact, Menzel and Fornahl (2010) present a skeptical position regarding the effectiveness of any competitiveness policy intended to stimulate the development of clusters at initial stages (agglomeration and emerging), since the existence of horizontal and vertical links among firms concentrated in the same region constitutes a very common fact around the world. Thus, it is almost impossible to distinguish agglomerations with real potential to become a cluster from less complex structures. Consequently, emerging clusters are almost always only described ex-post. After the initial stages of the clusters life cycle it is expected the development and intensification of interdependencies between firms within cluster boundaries (Press, 2006). Indeed the development of those interdependencies, firms start to resemble more with each other, being observe the emergence of convergent designs in terms of technological models (Menzel and Fornahl, 2010), specialized labour market (Cooke et al., 2007), production systems (Pitelis, 2010) and inter-firm relationships (Blien and Maier, 2008). Moreover, developing clusters also attract a high number of start-ups that act stimulating the intensification of intra-cluster relationships. This way, that process of convergence and expansion of the number of firms within the cluster boundaries culminates in the development of self-reinforcing external economies that decrease the heterogeneity among firms at the same time that creates benefits like transactions cost economies and the privileged access to local knowle dge. As clusters reach the stage of maturity, the standards and configurations originating from past decisions become consolidate and it is observed a reduction in the growth rate of firms attracted to the cluster (Klepper, 2007). At this point the cluster trajectory may take two different directions. Keep unchanged, and suffer with a homogenization process that creates bias economic activities and therefore prevent firms to adapt to external shocks (Menzel and Fornahl, 2010). That situation traps firms in previous successful development path and lead to the geographic dispersion of the local actors and to the deterioration of the interdependencies and capabilities. The other possible trajectory is observed in clusters that reach the stage of maturity and successfully sustain the local dynamics by a continuous process of reconfiguration and adaptation to the external shocks (Wolter, 2003). 1.2 Clusters and Economic Performance The extant theory offers a wide range of explanations to justify the economic and competitive benefits experienced by firms located within clusters. Krugman (1991) stress the existence of increasing returns originating from the concentration of firms in the same area, arguing that the geographic proximity puts together the main parts related to firms activities (e.g., labours, firms, suppliers and costumers) resulting in transaction costs economies. Following other perspective, Schmitz and Nadvi (1999) argue that unintentional external economies are not sufficient to explain the competitiveness of firms located within clusters, attributing to the existence of deliberate joint actions (e.g., sharing equipments, associations, strategic alliances and producers improving components) a critical source of the competitive advantages. Pitelis and Pseiridis (2006) explain the levels of competitiveness and productivity associated with clustered firms considering the existence of specialized hu man resources, infrastructure and befits associated with unit costs economies complemented by the presence of an institutional atmosphere. Stressing a different point of view, Bahlmann and Huysman (2008) adopts the knowledge-based view of clusters to emphasize the relevance of knowledge spillovers among the firms to explain the advantages originated from the agglomeration process. Dupuy and Torre (2006) explains the existence of cluster in terms of the advantages originating from trust relationships that increase confidence and reduce risk and uncertainty about the intra-cluster operations taking place among the firms. Moreover, Zyglidopoulos et al. (2003), describe the positive effects that the reputation of a cluster may exert on the internalization process of small and micro enterprises through the alleviation of strategic constraints associated with factors like qualified work force, financing and reduction of the firms legitimation expenses. Despite that wide range of arguments, the most traditional perspective found in the cluster literature has explained the competitive advantages of clusters in terms of productivity and innovation (Pitelis, 1998; Porter, 1998), suggesting that the special characteristics originated from the economic and geographic proximity have significant impact on those two factors. Supplementary, Enright (1998) considers that the characteristics present inside the clusters local environment result in pressures, incentives and capabilities that increase the firms competitiveness comparatively to dispersed competitors, explaining the clustering process in terms of geographically restricted characteristics. Moreover, Solvell et al. (2003) suggests that the competitive advantages emerging from regional clusters may be classified as static and dynamics. According to this perspective, while the agglomeration process triggered and sustained intensively or exclusively by factors like natural resources, low cost labors and government subsidies offers a vulnerable (easy to be copied, substituted or simple eroded by environmental changes) competitive position, clusters based on dynamics characteristics like multi-sectorial externalities, advantages of scale and scope and specific knowledge spillovers are more dynamics and competitive. Extending the arguments presented by Solvell and his colleagues, Andersson et al. (2004) considers that the sustainability of static and dynamic competitive advantages is not strictly determined, arguing that static factors are the main responsible for the emergence of clusters, while the dynamics factors are only developed along the different stages of the cluste r life cycle. Complementing the understanding about the influence of the cluster dynamics on the firms economic performance some authors like Porter (2001); Garnsey and Heffernan (2007); Karlsson (2008) and Mason (2008) describe the existence of a self-reinforcing process originating from the agglomeration externalities that contribute to create a regional virtuous-circle of increasing productivity, competitiveness and value creation. Following that argument, the economic and geographic proximity will stimulate firms to innovate more indeed benefits originating from local capabilities, which will stimulate even more the agglomeration process through the intensification of inter-firm relationships and the attraction of other firms from outside the cluster, which in turn will strength the local capabilities (Blandy, 2003, p. 101). Thus, the dynamics of clusters is expected to be self-reinforced by agglomeration benefits with significant influence on the firms performance. Putting together the arguments associated with the economic impacts experienced by clustered firms indeed the existence of local factors, it is possible to identify and describe the following positive location-specific externalities: Cost savings indeed the geographic proximity with specialized suppliers, labours and distributors; Knowledge-spillovers (intentional and unintentional), since firms inside clusters can benefit from the knowledge dissemination process that may take place especially through inter-firm cooperation, specific linkages and labour mobility; Deliberate joint actions facilitated by the engagement in alliances and partnerships to achieve strategic objectives; Trust relationships, that through the geographic and economic proximity minimize the uncertainty associated with commercial operations, resulting in transactions costs economies; Pressures for higher performance, stimulated by the proximity with competitors; Specific Infrastructure and public goods that are oriented to attend the cluster demands, like roads, ports, laboratories and telecommunication networks; Complementarities, associated with firms in different activities but sharing common factors like raw material, clients and technologies that may enhance the cluster efficiency as whole. Discussing the role of regional clusters in shaping competitive patterns, Tallman et al. (2004) proposes a distinction between the types of competitive advantages emerging from clustered firms: based on traded interdependencies and based on untraded interdependencies. The concept of traded interdependencies is related to the existence of inter-firm transactions inside the cluster, and is observed in formal exchange operations that may take place in form of alliances, commercial operations and acquisitions. On the other hand, untraded interdependencies are related to less tangible effects, and are based on shared knowledge for which no market mechanism exists; with no formal exchange of value for value (Tallman et al. 2004, p. 261). To illustrate the mechanisms by which the untraded interdependencies take place, it is possible to mention unintentional external economies associated with tacit knowledge shared through mechanisms like labor mobility. Those different types of interdependencies, especially untraded, present at the cluster level, represent a source of competitive advantage that is likely to be causal ambiguous (for firms inside and outside the cluster) and high complex in terms of their origins, what consequently constitutes attributes difficult to be replicated by competitors. However, the presence of untraded effects, especially unintentional knowledge spillovers, is viewed Enright (1998) as a constraining factor for firms within clusters, since the establishment of an efficient information flow may limit the firms capacity to obtain monopoly profits from the development of innovations. Complementing the negative effects originating from the clusters dynamics, some authors also describe agglomeration diseconomies that have a negative impact firms located within clusters. For example, congestion effects (Arthur, 1990), institutional sclerosis (Pouder and John, 1996; Pitelis, 2010), rigidities associated with labour mobility and natural resources (Krugman, 1989) and pollution (Fan and Scott, 2003). This way, the dynamics and performance of a cluster is determined by the interplay between positive and negative externalities observed during the different stages of development that a cluster is expected to pass overtime (Wolter, 2003) Limitations in the Cluster Theory Notwithstanding the advances in the cluster theory some questions still remain insufficiently explored. One of the main limitations observed in the current state of the cluster literature is the lack of comparative perspectives to explain the advantages and disadvantages of clusters relatively to other alternative models of organization of economic activities. In his very novel approach, Pitelis (2010) suggests that any perspective trying to explain clusters in terms of absolute advantages is at the very best incomplete. In this context, Pitelis proposes the comparison of clusters vis-ÃÆ'  -vis to markets and hierarchies in order to understand the reasons and conditions that lead firms to engage in intra-clusters relationships, market operations (outside the cluster) or integrate within the firms hierarchy. In fact it is not necessary a great effort to conclude that most of the cluster theory has been developed following a mono-institutional approach (e.g. Porter, 1990, 1998; Saxe nian, 1994; Rosenfeld, 1998; Swann and Sennett, 1998; Schmitz and Nadvi, 1999), while some few exceptions concentrated on transactions costs (e.g. Fujita and Thisse, 1996; Iammarino and McCann, 2006; Takeda et al., 2008) and knowledge creation efficiency (e.g. Hendry et al., 2000; Tracey and Clark, 2003; Reinau, 2007; Kongmanila and Takahashi, 2009) have been drawn on a comparative approaches between clusters and open-market operations. Assuming the arguments proposed by many scholars that clusters are engines of innovation (Davis, 2006, p. 32), the lack of comparative perspectives do not answer the question why clusters are more efficient than markets or the hierarchy to improve the firms innovative capacity (Pitelis, 2010). Thus, despite the wide number of ramifications observed in the cluster theory such as innovative efficiency, productivity, social capital and social interactions, its explicative power remains almost always restricted Inter Firm Relationships in the Silicon Fen Inter Firm Relationships in the Silicon Fen The attention that clusters have received from policy makers and academics has substantially increased in the last 20 years. Since Porters seminal work on The Competitive Advantage of Nations (1990) presented clusters as one of the determinants of the international competitiveness of nations and regions, many scholars have adopted and further developed his approach. Porter bases his arguments on what he describes as the globalization paradox, pointing out that despite the logical implications that the globalisation process might have in dismissing the relevance of regional factors, the most competitive firms in world are located in groups geographically concentrated in specific locations. That perspective contributed to attracting attention to the existence of characteristics tied to a local context that could not be accessed by firms positioned elsewhere, and more, to the positive effects that the concentration and the geographic proximity could exert on the firms competitiveness. However influential, Porters ideas were not the precursor to discussing the competitive outcomes originating from the geographic concentration of firms (Martin and Sunley, 2003). The roots of cluster theory go back to the industrial districts identified by Marshall (1890), who offered the first detailed description about the economic and social systems created as a result of the spacial concentrations of industrial activities. The Marshallian industrial districts were arrangements of small firms interconnected by commercial operations (buyers and sellers) and other firms engaged in the same or similar activities, that shared productive factors, such as the labour market, infrastructure and tacit knowledge (Becattini, 2004, p. 68). According to Marshals descriptions, a group of firms operating in one specific sector within a well-defined, concentrated and relatively small geographic area would experience higher levels of productivity and innovation, indeed the emergence of a fertile e nvironment for technical and organisational developments. Thus the local characteristics would enable the emergence of an industrial atmosphere that would increase the firms potential to acquire (especially tacit) knowledge, and create positive external economies accessible only to the firms located within the district (Asheim, 2003, p. 416). That perspective tried to evidence that firms geographically concentrated could accesses restricted positive exogenous benefits (exogenous to firms, but endogenous to the district), which would be an alternative to the scale economies achieved by a single (integrated) firm. Additionally, following some of the seminal ideas proposed by Marshall, it is possible to observe a significant number of economic geographers that also explored regional development using the spatial economic agglomeration to support their ideas. Some examples of concepts emerging from that theoretical trend are regional innovation milieux (Crevoisier, 2004), neo-Marshallian nodes (Amin and Thrift, 1992) and learning regions (Asheim, 1995). More examples can be found in Markusen (1996, p. 297), in which another three different types of industrial districts are described according to the firms configurations, internal versus external orientations and governance structures: a hub-and-spoke industrial district, which is concentrated around one or more dominant firms; a satellite platform, formed by a group of unconnected branches embedded in external links; and the state-anchored district, concentrated on one or more public-sector institutions. Despite the logical and robust assumption s found in many of those concepts, their influence and dissemination were not as successful as the more general cluster framework proposed by Porter. Martin and Sunley (2003) attributes the successful dissemination of the Porter concepts to the very general descriptions and delimitations that encompass a wide range of actors and many different structures. Following much of the concepts proposed by Porter, the description of advantages conferred on clustered firms associated with a general and structured analytical framework stimulated the development and dissemination of academic studies and subsidized the creation of supply-side competitiveness policies directed at structuring and supporting the development of clusters (Pitelis, 2010). That fact resulted in what Martin and Sunley (2003) describe as a policy panacea in the use of clusters as a standard (sometimes the unique) target for promoting competitiveness, innovation and economic growth. Moreover, in the last 20 years an increasing number of empirical studies in different countries and sectors have been observed, which aim to identify and discuss the competitive outcomes originating from the concentration of firms and other actors in the same location, for example: Brazil à ¢Ã¢â€š ¬Ã¢â‚¬Å" shoe manufacturing in the Sinos Valley (Schmitz, 2000); Spain à ¢Ã¢â€š ¬Ã¢â‚¬Å" the text ile and clothing industries in Catalonia (Porter, 1998); Taiwan à ¢Ã¢â€š ¬Ã¢â‚¬Å" electronic products at the Hsinchu Science Park (Chen, 2008); and the United States à ¢Ã¢â€š ¬Ã¢â‚¬Å" computer and information systems at the Silicon Valley (Saxenian, 1994). The large significant number of academic studies has resulted in a large number of definitions aiming to describe and establish an accepted cluster template (e.g., Enright, 1996; Swann and Prevezer, 1996, Rosenfeld, 1997; Porter, 1998) to support policy makers and academics has led to intense debates and controversial perspectives. Even though the concept of clusters has been increasingly widely disseminated and used by geographers, economists and policy makers, it has suffered from some conceptual confusion. Porter defines a cluster as a geographic concentration of interconnected companies, specialized suppliers, service providers firms in related industries, and associated institutions (for example, universities, standards agencies and trade associations) in particular fields that compete but also co-operate (1998, p. 197). However, Martin and Sunley (2003, p. 12) present consistent arguments that indicate the vagueness and superficiality of the concept proposed by Porter. Accordin g to their arguments, those characteristics make the concept of cluster means different things to different researchers and policy makers, creating problems for its proper use in the guidance of academics and governments. Those highly controversial aspects of the cluster theory have stimulated the continuous emergence of new concepts and definitions for clusters. Proposing a definition aiming to fill some of the gaps and failures found in extant cluster theory, Pitelis (2010, p. 5) defines clusters as geographical agglomerations of firms in particular, related, and/or complementary, activities, with a geographical dimension, that exhibit horizontal and/or vertical intra- and/or inter-sectoral linkages, which operate in the context of a facilitatory socio-institutional setting, and which co-operate and compete (co-opete) in inter-national markets. That definition tries offer to a more delimited approach that incorporates four major elements: geographical agglomeration, linkages, social-capital  [1]  and co-opetition (competition and cooperation). The use of those four elements in a single definition offers the possibility to cover the cluster characteristics using delimited criteria to identify and distinguish developed clusters from less complex geographical agglomerations of firms and institutions. Although it is possible to observe some level of ambiguity encompassing the clusters theory, the existence of links interconnecting local actors complemented by geographical dimensions constitute some of the main common points used to guide academics and policy makers with interesting by the competitive outcomes originating from clusters. Those characteristics have frequently been used as the starting point to understand the economic dynamics of clustered firms, putting emphasis on the levels of innovation and productivity emerging from the concentration of different actors in the same area. Suggesting conditional characteristics to the presence of competitive advantages obtained by firms inside clusters, Ketels (2004) considers that the positive economic effects originated from the geographical concentration will only take place if four critical characteristics are shared among firms and institutions: Proximity: they must be geographically close to allow the emergence of knowledge spillovers and to share the same common resources; Linkages: the necessity of similarities in their activities leading to the establishment of connections and synergies; Interactions: the social interactions developed among firms, clients, suppliers, research institutes, and so on, is what forms the social capital that becomes possible firms to achieve differentiated competitive performances; Critical mass: it is important to have a significant number of firms and institutions in order to create meaningful impacts on performance of the local actors. Those characteristics described by Ketels may be used to guide the identification and distinction between developed clusters (Pitelis, 2010) from incipient clusters (Schmitz, 1999) in order to dismiss some incorrect interpretations associated with the clusters dynamics. Considering that the presence of geographic concentration of firms in the same industry is strikingly common around the world (Porter, 1990, p.120), it is necessary the use of specific benchmarks to distinguish and classify different groups of firms geographically concentrated according to their specific characteristics (Gordon and McCann, 2000; Isbasoiu, 2006). Describing how the existence of local capabilities  [2]  create differentiated conditions for companies within real clusters, Menzel and Fornahl (2010) argue that clusters are essentially formed from path dependencies (Martin and Sunley, 2006), transaction costs economies (McCann and Sheppard, 2003) and small cognitive distances originating from spatial proximity (Maskell, 2001). Thus, that set of factors are expected to create a specific regional dynamics with influence on the firms economic performance. Taking into consideration the different stages of a cluster life cycle, and the misunderstandings related to the claims associated with the clusters and competitiveness, Schmitz points out that A group of small producers making the same or similar things in close vicinity to each other constitutes a cluster, but such concentration in itself brings few benefits (1999, p. 4), emphasizing that the mere presence of firms in a delimited area does not represent a source of value creat ion able to improve in a significant way the local economic performance. Following the arguments above, the differences between regional clusters and simple agglomerations (groups of firms) lie mainly on the interconnected nature and spatial proximity. Thus, clusters are characterized by intense collaborative networks and concentrations of collaboration and competition (co-opetitition) (Pitelis, 2010), conditions which offer significant opportunities and stimulate the emergence of regional competitive advantages (Steinle and Schiele, 2002). Complementarily, another critical characteristic observed within clusters is the diversity of actors. According to Porter (1990, 1998, 2000), an industrial cluster includes suppliers, consumers, related industries, governments, and supporting institutions such as universities. This way, the existence of a regional network formed by a significant group of interconnected local actors is one of the critical factors to understand the differentiated competitive performance of firms within clusters (Steinle and Schiele, 2002 ). Illustrating that argument, Saxenian (1994) observed that Hewlett Packard and other firms at the Silicon Valley had their performance improved by the development long-term partnerships with suppliers located geographically close. Moreover, based on that observation, Saxenian concluded that, especially in high-tech industries, the physical proximity represents a facilitator to the establishment of efficient collaborative arrangements required to create and manage complex products and services. 1.2 Evolutionary Stages of Industrial Clusters Despite the vast cluster literature, the number of academic works discussing the evolutionary patterns of clusters overtime is not so extensive. Some examples can be found in Pouder and John (1996), Klepper (2001, 2007), Wolter (2003) and Andersson et al., (2004), and despite the divergent perspectives, it is accepted that clusters follow a kind of life cycle comprised by different phases that significantly differ in their characteristics and influence on firms performance. Regarding the cluster dynamics, Pouder and John (1996) argue that comparative analysis between clustered and non-clustered firms during the industry life cycle reveal that firms within clusters outperform those geographically dispersed at the initial stages of development, and have a worse performance at its end. That fact suggests that the cluster life cycle is not just a local representation of the industry trajectory, but is a result from local peculiarities. The comparative analysis developed by Saxenian (1994 ) between the computer industry in Boston and Silicon Valley illustrates how different clusters belonging to the same industry are very likely to follow different trajectories (Menzel and Fornahl, 2010). Proposing a different perspective, Klepper (2001, 2007) suggests a model to demonstrate how the clusters life cycle is determined by some the industry patterns. Klepper analyzed the automobile, tire and television industries and observed that at the beginning of the industry life cycle it was not possible to observe clear geographic concentrations of firms, with most of the firms spatially dispersed. He observed that in those industries clusters started to emerge and develop according to the industry growth rates. Klepper argues that the local characteristics originating from the spacial proximity (e.g., intensive spin-off process) give the stimulus for the geographic agglomeration of the whole industry, not only for specific groups. At the time the industry growth rate reduces, the attractiveness to remain agglomerated will also decrease and the industry will become dispersed again. That model proposed by Klepper represents a Technology-Product- Industry (TIP) life cycle. The logic behind this model is on the impact that the evolution of products and innovations has on the size, number, and location of firms. Wolter (2003) criticizes the model proposed by Klepper arguing that the growth rate cannot explain the agglomeration process in all industries on equal basis. Moreover, Wolter disagrees with the determinist perspective proposed by the TIP model, once it neglects that mature industries can be reinvented by radical or incremental innovations of new products and process. Analyzing the economic performance of firms within clusters Pouder and John (1996) attribute to the existence of mental models and biased cognitive focus the characteristics responsible for shaping the movement through the clusters life cycle. Following that perspective, at initial stages the cluster dynamics creates an innovative environment that exerts positive impacts on the firms performance. However, overtime that initial condition is eroded by strong institutional pressures that create a homogeneous macroculture that acts inhibiting the innovative capacity of the firms within the cluster. As in the model presented by Klepper (2001, 2007), that trajectory proposed by Pouder and John may also be criticized by the determinism that ignores the possibility of adaptations or reconfigurations in order to avoid lock-ins and other negative effects. Considering the arguments proposed by Menzel and Fornahl (2010, p. 8) that very few clusters follow a rigid life cycle from emergence to growth and decline, it is expected that clusters evolve overtime according to the local dynamics created by economic and social interactions among firms and institutions. That dynamics may be influenced, but not strictly determined by industry patterns (Wolter, 2003). Following a generic and stylized trajectory, within successful clusters the local network formed by inter-firm connections will tend to be intensified overtime, with an increasing number of formal and informal interactions between the long-established companies and new the ones attracted to the cluster. Even though it is more conceivable to assume that the decisions adopted by firms and institutions are shaped by specific circumstances, a generic trajectory can be described following the stages illustrated in Figure 1. Figure 1: The cluster life cycle C:UsersLucasAppDataLocalMicrosoftWindowsTemporary Internet FilesContent.WordSem tÃÆ' ­tulo.jpg Source: Andersson et al. (2004, p. 43) Agglomeration: It is possible to observe the existence of a number of companies and other actors (e.g. banks, government agencies, universities, accountants, and lawyers offices) in a specific region working around the same or interrelated activities. Emerging cluster: Forming the embryo to the cluster some actors start to cooperation around some core activities, and start to realize the existence of common linkages. Developing cluster: The linkages are intensified by the emergence and attraction of new actors to the region, resulting in the creation of more interaction. In this context the development of inter-firm-cooperation becomes more evident through the development of joint efforts. The Mature cluster: This stage is configured by the presence of a certain critical mass of factors that consistently influence the competitive performance of the firms inside the cluster. The internal dynamics is characterized by the presence of an institutional environment, strong linkages, complementarities and the emergence of new firms through startups, joint ventures and spin-offs. Transformation: Indeed the process of continuous environmental change in markets, technologies, regulations and other process, to be successful a cluster have to innovate and adapt to these new conditions, other way stagnation and decay may affect the cluster dynamics. That process of change/adaptation may happen through the emergence of one or several interconnected clusters with focus in other activities, or by new configuration in terms of networks of firms and institutions. The presence of economic benefits for clustered firms described by authors like Schmitz and Nadvi (1999), Ketels (2004), Isbasoiu (2006) and Pitelis and Pseiridis (2006) are closely related to the stage of development that a cluster is experiencing. For example, an emerging cluster is not actually a cluster, since the small number of firms is not expected to present a high level of linkages and do not form a critical mass. Moreover, the absence of strong interdependencies such as labour mobility, spin-off, socioeconomic networks and intense exchange of good and services prevent the emergence of local capabilities. Thus, same considering that this stage constitutes the embryo that determines the future cluster orientation, at this point the firms are not expected to be strongly influenced by a complex local dynamics. Observing that fact, Menzel and Fornahl (2010) present a skeptical position regarding the effectiveness of any competitiveness policy intended to stimulate the development of clusters at initial stages (agglomeration and emerging), since the existence of horizontal and vertical links among firms concentrated in the same region constitutes a very common fact around the world. Thus, it is almost impossible to distinguish agglomerations with real potential to become a cluster from less complex structures. Consequently, emerging clusters are almost always only described ex-post. After the initial stages of the clusters life cycle it is expected the development and intensification of interdependencies between firms within cluster boundaries (Press, 2006). Indeed the development of those interdependencies, firms start to resemble more with each other, being observe the emergence of convergent designs in terms of technological models (Menzel and Fornahl, 2010), specialized labour market (Cooke et al., 2007), production systems (Pitelis, 2010) and inter-firm relationships (Blien and Maier, 2008). Moreover, developing clusters also attract a high number of start-ups that act stimulating the intensification of intra-cluster relationships. This way, that process of convergence and expansion of the number of firms within the cluster boundaries culminates in the development of self-reinforcing external economies that decrease the heterogeneity among firms at the same time that creates benefits like transactions cost economies and the privileged access to local knowle dge. As clusters reach the stage of maturity, the standards and configurations originating from past decisions become consolidate and it is observed a reduction in the growth rate of firms attracted to the cluster (Klepper, 2007). At this point the cluster trajectory may take two different directions. Keep unchanged, and suffer with a homogenization process that creates bias economic activities and therefore prevent firms to adapt to external shocks (Menzel and Fornahl, 2010). That situation traps firms in previous successful development path and lead to the geographic dispersion of the local actors and to the deterioration of the interdependencies and capabilities. The other possible trajectory is observed in clusters that reach the stage of maturity and successfully sustain the local dynamics by a continuous process of reconfiguration and adaptation to the external shocks (Wolter, 2003). 1.2 Clusters and Economic Performance The extant theory offers a wide range of explanations to justify the economic and competitive benefits experienced by firms located within clusters. Krugman (1991) stress the existence of increasing returns originating from the concentration of firms in the same area, arguing that the geographic proximity puts together the main parts related to firms activities (e.g., labours, firms, suppliers and costumers) resulting in transaction costs economies. Following other perspective, Schmitz and Nadvi (1999) argue that unintentional external economies are not sufficient to explain the competitiveness of firms located within clusters, attributing to the existence of deliberate joint actions (e.g., sharing equipments, associations, strategic alliances and producers improving components) a critical source of the competitive advantages. Pitelis and Pseiridis (2006) explain the levels of competitiveness and productivity associated with clustered firms considering the existence of specialized hu man resources, infrastructure and befits associated with unit costs economies complemented by the presence of an institutional atmosphere. Stressing a different point of view, Bahlmann and Huysman (2008) adopts the knowledge-based view of clusters to emphasize the relevance of knowledge spillovers among the firms to explain the advantages originated from the agglomeration process. Dupuy and Torre (2006) explains the existence of cluster in terms of the advantages originating from trust relationships that increase confidence and reduce risk and uncertainty about the intra-cluster operations taking place among the firms. Moreover, Zyglidopoulos et al. (2003), describe the positive effects that the reputation of a cluster may exert on the internalization process of small and micro enterprises through the alleviation of strategic constraints associated with factors like qualified work force, financing and reduction of the firms legitimation expenses. Despite that wide range of arguments, the most traditional perspective found in the cluster literature has explained the competitive advantages of clusters in terms of productivity and innovation (Pitelis, 1998; Porter, 1998), suggesting that the special characteristics originated from the economic and geographic proximity have significant impact on those two factors. Supplementary, Enright (1998) considers that the characteristics present inside the clusters local environment result in pressures, incentives and capabilities that increase the firms competitiveness comparatively to dispersed competitors, explaining the clustering process in terms of geographically restricted characteristics. Moreover, Solvell et al. (2003) suggests that the competitive advantages emerging from regional clusters may be classified as static and dynamics. According to this perspective, while the agglomeration process triggered and sustained intensively or exclusively by factors like natural resources, low cost labors and government subsidies offers a vulnerable (easy to be copied, substituted or simple eroded by environmental changes) competitive position, clusters based on dynamics characteristics like multi-sectorial externalities, advantages of scale and scope and specific knowledge spillovers are more dynamics and competitive. Extending the arguments presented by Solvell and his colleagues, Andersson et al. (2004) considers that the sustainability of static and dynamic competitive advantages is not strictly determined, arguing that static factors are the main responsible for the emergence of clusters, while the dynamics factors are only developed along the different stages of the cluste r life cycle. Complementing the understanding about the influence of the cluster dynamics on the firms economic performance some authors like Porter (2001); Garnsey and Heffernan (2007); Karlsson (2008) and Mason (2008) describe the existence of a self-reinforcing process originating from the agglomeration externalities that contribute to create a regional virtuous-circle of increasing productivity, competitiveness and value creation. Following that argument, the economic and geographic proximity will stimulate firms to innovate more indeed benefits originating from local capabilities, which will stimulate even more the agglomeration process through the intensification of inter-firm relationships and the attraction of other firms from outside the cluster, which in turn will strength the local capabilities (Blandy, 2003, p. 101). Thus, the dynamics of clusters is expected to be self-reinforced by agglomeration benefits with significant influence on the firms performance. Putting together the arguments associated with the economic impacts experienced by clustered firms indeed the existence of local factors, it is possible to identify and describe the following positive location-specific externalities: Cost savings indeed the geographic proximity with specialized suppliers, labours and distributors; Knowledge-spillovers (intentional and unintentional), since firms inside clusters can benefit from the knowledge dissemination process that may take place especially through inter-firm cooperation, specific linkages and labour mobility; Deliberate joint actions facilitated by the engagement in alliances and partnerships to achieve strategic objectives; Trust relationships, that through the geographic and economic proximity minimize the uncertainty associated with commercial operations, resulting in transactions costs economies; Pressures for higher performance, stimulated by the proximity with competitors; Specific Infrastructure and public goods that are oriented to attend the cluster demands, like roads, ports, laboratories and telecommunication networks; Complementarities, associated with firms in different activities but sharing common factors like raw material, clients and technologies that may enhance the cluster efficiency as whole. Discussing the role of regional clusters in shaping competitive patterns, Tallman et al. (2004) proposes a distinction between the types of competitive advantages emerging from clustered firms: based on traded interdependencies and based on untraded interdependencies. The concept of traded interdependencies is related to the existence of inter-firm transactions inside the cluster, and is observed in formal exchange operations that may take place in form of alliances, commercial operations and acquisitions. On the other hand, untraded interdependencies are related to less tangible effects, and are based on shared knowledge for which no market mechanism exists; with no formal exchange of value for value (Tallman et al. 2004, p. 261). To illustrate the mechanisms by which the untraded interdependencies take place, it is possible to mention unintentional external economies associated with tacit knowledge shared through mechanisms like labor mobility. Those different types of interdependencies, especially untraded, present at the cluster level, represent a source of competitive advantage that is likely to be causal ambiguous (for firms inside and outside the cluster) and high complex in terms of their origins, what consequently constitutes attributes difficult to be replicated by competitors. However, the presence of untraded effects, especially unintentional knowledge spillovers, is viewed Enright (1998) as a constraining factor for firms within clusters, since the establishment of an efficient information flow may limit the firms capacity to obtain monopoly profits from the development of innovations. Complementing the negative effects originating from the clusters dynamics, some authors also describe agglomeration diseconomies that have a negative impact firms located within clusters. For example, congestion effects (Arthur, 1990), institutional sclerosis (Pouder and John, 1996; Pitelis, 2010), rigidities associated with labour mobility and natural resources (Krugman, 1989) and pollution (Fan and Scott, 2003). This way, the dynamics and performance of a cluster is determined by the interplay between positive and negative externalities observed during the different stages of development that a cluster is expected to pass overtime (Wolter, 2003) Limitations in the Cluster Theory Notwithstanding the advances in the cluster theory some questions still remain insufficiently explored. One of the main limitations observed in the current state of the cluster literature is the lack of comparative perspectives to explain the advantages and disadvantages of clusters relatively to other alternative models of organization of economic activities. In his very novel approach, Pitelis (2010) suggests that any perspective trying to explain clusters in terms of absolute advantages is at the very best incomplete. In this context, Pitelis proposes the comparison of clusters vis-ÃÆ'  -vis to markets and hierarchies in order to understand the reasons and conditions that lead firms to engage in intra-clusters relationships, market operations (outside the cluster) or integrate within the firms hierarchy. In fact it is not necessary a great effort to conclude that most of the cluster theory has been developed following a mono-institutional approach (e.g. Porter, 1990, 1998; Saxe nian, 1994; Rosenfeld, 1998; Swann and Sennett, 1998; Schmitz and Nadvi, 1999), while some few exceptions concentrated on transactions costs (e.g. Fujita and Thisse, 1996; Iammarino and McCann, 2006; Takeda et al., 2008) and knowledge creation efficiency (e.g. Hendry et al., 2000; Tracey and Clark, 2003; Reinau, 2007; Kongmanila and Takahashi, 2009) have been drawn on a comparative approaches between clusters and open-market operations. Assuming the arguments proposed by many scholars that clusters are engines of innovation (Davis, 2006, p. 32), the lack of comparative perspectives do not answer the question why clusters are more efficient than markets or the hierarchy to improve the firms innovative capacity (Pitelis, 2010). Thus, despite the wide number of ramifications observed in the cluster theory such as innovative efficiency, productivity, social capital and social interactions, its explicative power remains almost always restricted

Humane Migration for Citizens Around the World Essay -- Refugees, Pro

Studies have been conducted on the definition of humane migration and how to achieve this in countries around the world. However, contradictory studies have been found to show that many people are displeased with the rate people are migrating as the years proceed. To understand how both of these viewpoints affect the IOM and their work with countries around the world, both viewpoints should be taken into consideration. This paper examines the studies that are for humane migration around the world to propose that additional research needs to be conducted to better understand the viewpoints these authors are making. Literature Review The book Globalization: The Making of World Society by Frank J. Lechner written in 2009, explores the dimensions of globalization and the debated issues that surround it. Lechner argues that globalization involves people becoming connected through a sharing of ideas and cultures. This sharing of ideas and cultures is what humane migration aims to do. Lechner claims that globalization involves interdependence of people around the world and that citizens need to work together, regardless of their nationality. Frank J. Lechner has a PhD in sociology and has published three books about globalization and the world society. This book is fairly recent and was published in 2009. Its ideas of globalization are up to date and include statistics and research from previous years. Lechner seems to present an unbiased opinion throughout most of his book, but because some of the book is based off of his personal beliefs, some bias seems to be inevitable. Human migration could be executed by using Lechner’s ideas of globalization forcing people to become interdependent on each other. IOM could simplify its multi... ... Print. Koser, Khalid. "Introduction: International Migration and Global Governance." Global Governance (2010): 301-15. Web. 2 May 2014 Lechner, Frank J. Globalization: The Making of World Society. Chichester, U.K.: Wiley-Blackwell, 2009. Print. Munck, Ronaldo. "Globalization, Migration and Work: Issues and Perspectives." Labour, Capital & Society 43 (2010): 155-77. Web. 2 May 2014. Thomas, Chantal. "What Does the Emerging International Law of Migration Mean for Sovereignty?" Melbourne Journal of International Law 14 (2013): 392-450. Web. 2 May 2014. Rodriguez, Robyn Magalit. Migrants for Export: How the Philippine State Brokers Labor to the World. Minneapolis: U of Minnesota, 2010. Print. Zapata-Barrero, R., and A. Pecoud. "New Perspectives on the Ethics of International Migration." American Behavioral Scientist 56.9 (2012): 1159-164. Print.

Lean Management :: Business, Manufacturing

Lean management is a thought process and a philosophy, not a tool, used to look at a business weather it is manufacturing, service or any other activity with a supplier and a customer relation with the goal of eliminating non-value added tasks (Womack, Jones, Ross, 1990). The principles of lean production include teamwork, communication, efficient use of resources and continuous improvement (Kaizen). It can be said that they pioneered the idea of applying the concepts outside of manufacturing environments. The objective of lean production is a system for organising and managing product development, operations, suppliers, and customer relation that requires less human effort, less space, less capital, less material and less time to make products with fewer defects to precise customer desires, compared with the previous system of mass production (Marchwinski & Shook, 2004). The concepts of both Ohno (1988) and Womack and Jones (2003) search for ways to reduce lead time by eliminating w aste it can be said that the terms â€Å"Lean† and â€Å"Toyota Production System† are synonymous. Lean management is not restricted to the actions that take place in the manufacturing function of a company, rather it relates to activities range from product development, procurement and manufacturing over to distribution. Together these areas create the lean enterprise. The ultimate goal of implementing lean production in an organization is to have the customer in focus when improving productivity, enhancing quality, shortening lead times, reducing costs etc. These are factors representing the performance of a lean production system. The determinants of a lean production system are the actions taken, the principles implemented and the changes made to the organization to achieve the desired performance (Karlsson & Ahlstrom, 1996) There are multiple ways to combine the individual practices to represent the multi-dimensional nature of lean manufacturing. In combining these practices, the researcher has to compete with the technique used to combine and the actual content of the combinations. The dominant method in operations management literature has been to use exploratory or confirmatory factor analysis to combine individual practices in a multiplicative function to form orthogonal and unidimensional factors (Flynn et al., 1995; Cua et al., 2001; Shah & Goldstein, 2006). A review of research from organization theory, and labour and human resource management shows less reliance on factor analysis and offers multiple ways for combining individual practices and creating an index. One such method is the additive index used by Osterman (1994) and MacDuffie (1995) in developing â€Å"bundles† of interrelated human-resource management practices.

Weight Management Essay examples -- essays research papers

Weight management has been thought of as only weight loss by many. Weight management covers all aspects of attaining and maintaining optimum weight for a healthy lifestyle. Health professionals now realize that prevention of weight gain as well as weight loss and improving health status are important goals. These goals must be individualized for success. At the outset of treatment, the patient and health care provider should discuss and agree upon goals. The goals must take into account the food habits, exercise behaviors, psychological outlook and support systems of the individual. Realistic expectations, short- and long-term, may be promoted by a discussion of a healthy weight versus an ideal body weight. Features of weight management interventions may include behavior modification, dietary principles, energy balance components, and a sound food plan. In order to create a behavior modification plan that will be successful for the individual, identifying cues, responses and consequences of eating behaviors is necessary. Control of eating behavior, physical activity, emotional, social, and psychological health must all be analyzed and interventions applied. Behaviors related to problems with intake and expenditure of energy must be specifically defined. Recording and analyzing eating and exercise behaviors to develop strategies aimed at learning new behaviors are essential. Dietetic practice in weight management is complex and challenging. Assessment of weight and health should guide weight management goals and outcomes. The weight and dieting history should include age of onset of weight problems, number and types of diets, exercise history, possible triggers to weight gains and losses, and an appropriate range of weight change. The Body Mass Index (BMI) is considered the standard measurement for weight status. The BMI is estimated by weight (kg) divided by height (m2). A BMI of 25 – 29 is considered overweight and some weight loss is recommended. There is debate surrounding how much weight loss should be attained. In considering a weight management plan, it is necessary to incorporate each of the following: †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  realistic goals (weight loss average of  ½ to 1 pound per week); †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  energy intake limits related to the energy output of the individual (the Basal Metabolic Rate (BMR) + activity level shoul... ...he obesity epidemic. Science. 1998;280:1371-1374. 2. Willett WC, Dietz WH, Colditz GA. Primary Care: Guidelines for healthy weight. New England Journal of Medicine. 1999;341:427-434. 3. National Task Force on the Prevention and Treatment of Obesity, National Institutes of Health. Very low-calorie diets. Journal of the American Medical Association. 1993;270:967-974. 4. Stein K. High-protein, low carbohydrate diets: Do they work? Journal of the American Dietetic Association. 2000,100:760-761. 5. Jeffery RW, Drewnowski A, Epstein LH, Stunkard AJ, Wilson GT, Wing RR, Hill DR. Long-term maintenance of weight loss: current status. Health Psychology. 2000;1(Suppl):5-16. 6. Byfield C. A lifestyle physical activity intervention for obese sedentary women: Effect on cardiovascular disease risk factors. American College of Sports Med: Indianapolis IN; 2000. 7. Rosen JC. Improving body image in obesity. In: Thompson, JK, ed. Body Image, Eating Disorders and Obesity. Washington, DC: American Psychological Association; 1996: 425-550. 8. Nix S. Basic Nutrition & Diet Therapy, 12th edition. 2005: 268-287. 9. Martini F. Fundamentals of Anatomy & Physiology, 6th edition. 2004: 928-964.

Reynolds and Reynolds Essay

Reynolds & Reynolds Case Study The Reynolds and Reynolds case about team selling had very many positives and few negatives, and was a very well rounded and planned way for the American Ford Dealership to improve its customer service sector. First, I wanted to point out the effectiveness of team selling that the Reynolds team did well. They had three people comprise the team, Mr. Sherman, Mr. Wiltgen, and Mr. O’Neill. Sherman would pitch the plan to the dealership and discuss the reports with them, Wiltgen was the implementation guy, meaning that he would set everything into place if and when they agreed on what plans to use, and O’Neill was the manager overseeing everything and was there for backup if needed. The three positions and roles they played stayed the same throughout the plan and they followed through with how they wanted to present. Another thing I feel was very effective was how Sherman brought to the attention of the dealership the â€Å"lost opportunities† they had and how more much profit they could have made the previous year. O’Neill confirmed these numbers, thus making a good team decision and presentation of the facts. Also, another effective point they made was bringing up the way the dealership’s competitors were doing business and what systems they were using. The one and only disadvantage I found during this team selling presentation was that Sherman took on multiple roles as the team leader and the business consultant, and the other two were basically just there on an as-needed basis. The Reynolds team also showed great execution to the client access, client education, and fulfillment perspectives. To satisfy client access, they split the client base into three categories: Actives (customers who have been in for service in the previous 6 months), Inactives, and New Customers. Splitting customers into these categories made it easy for the dealership to see who is coming in for service, who should be notified they are due for service, and those new customers they still want to target. They executed client education/ persuasion by creating the â€Å"Preferred Customer Card† program. They also did this by implementing over 100 different types of coupons that can be easily customized to each individual customer by type of car, zip code, etc. and with the service reminder program where customers would be mailed letters, coupons, and notified via phone call that they are due for service or there was a deal going on for them. Fulfillment was achieved by the dealership choosing to implement the Direct Drive program and the service reminder program ideas that the Reynolds team had presented to them, and setting up future plans between the marketing firm and the car dealership.