The Negative Effects of Gluten in Food Research Paper

The Negative Effects of Gluten in Food - Research Paper Example As a result, the gluten sensitive people fall victim to different diseases like obesity, osteoporosis, depression, celiac disease and non-celiac related food allergies. Generally, intestinal biopsy is conducted in people to detect if they are sensitive to gluten. Researches are underway to know more about how gluten affects the health of a person. The best way to avoid diseases for a gluten sensitive patient is to be on a gluten-free diet. Many researchers and physicians have been pondering lately over the considerable rise of diseases like obesity, osteoporosis, depression, celiac disease and non-celiac related food allergies among common people. There is much discussion ongoing in the medical world as to the major cause of such chronic diseases. Gluten is thought to be one factor which is recognized to be causing these diseases in people who are allergic to it. Research suggests the negative effects of gluten in food are obesity, osteoporosis, depression, celiac disease and non-celiac related food allergies. Gluten causes damage to the small intestine and starts giving off symptoms in people who are gluten sensitive. The damaged intestine give way to inadequate absorption of nutrition and the gluten sensitive person suffers from different diseases. Gluten is a form of protein which is generally found in wheat, barley and rye. It can be said that it is found in many types of cereals and various types of bread. However, gluten is not present in all types of food from the grain family. Some grains like rice, millet, corn quinoa and oats do not contain gluten. Hunter (1987) states that â€Å"One of the gluten’s main protein fractions is gliadin, which is a complex mixture†(pg. 3). Books (2005) explains that â€Å"Gluten is a type of protein found in wheat, barley, rye, triticale and oats†(pg.7). When this protein is metabolized in the body of a certain person, it can at times give a tremendous problem.  Ã‚  

Principles of assessment in lifelong learning Essay Example for Free

Principles of assessment in lifelong learning Essay 1.1Explain the types of assessment used in lifelong learning. (150 words approx.) Initial/diagnostic assessment can be taken before learner’s enrollment for a course. This is a way of finding out whether the prospective course is suitable for a student and meets the learner’s needs or not. Formative assessment can be taken during the programme or a course. Teachers use assessments in their teaching sessions to make judgement about their learners. Summative assessment is used to outline or work out the level of achievement. Summative assessment is used for a final judgement about the learning achievements. Formal assessment is used where there is a need to assess learners under controlled conditions. Informal assessment is used as an ongoing check on understanding without control conditions. This is an aid for a teacher to monitor progress. Independent assessment applies to courses where the learners are assessed by someone other than their teacher. Peer assessment is used where other learners are at the same level of skill and knowledge and can play a vital role in judging a learners achievement level. 1.2 Explain the use of methods of assessment in lifelong learning. (150 words approx.) Different methods can be used for assessment in the lifelong learning. Short answers: is a good way of keeping student activities in their learnings. Multiple –choice: is a task in which learner has to select the correct answer from a number of alternative options. Observation: Observation is used i this programme for assessment of micro-teach/ teaching practice delivery. It can be used in any situation where practical skills are being assessed. Project work: involves a piece of written work in which learners take responsibility. Essays: This is a substantial piece of written work as well. It asks learners to show understanding of the subject. Exams: can be taken either by written tests or completion of a practical task under controlled conditions. Oral and aural: These assessment test speaking and listening skills. In this assessment, learners are required to listen to something and respond . Electronic assessment: refers to the use of information technology for any assessment-related activity.

Subject Of Language And Identity

Subject Of Language And Identity I have chosen this subject of language and identity, which leads to the death of a language, if language dies. Language and identity comes under my course, part 1, under language and cultural context. On the 4th of February 2010, while browsing through BBCs website I stumbled upon a captivating and according to me a very sad article. It read last speaker of ancient language of Bo dies in India, Boa sr.s story saddened me, she died at the age of 85 and for almost thirty years she didnt have anyone to converse with in her native language. Imagine not being able to use English for thirty years, you loose the freedom to express in your first language. As a journalist I knew what it meant for the world to loose a language, its disheartening, in essence a piece of history and culture is lost, I believe it is as important to preserve and save a language as it is to save and preserve the environment, but everyone is not aware of the adverse affects language death can cause. As a journalist, I thought of it as my moral responsibility to throw light on language death and its adverse effects. Thus, I wrote this article and decided on publishing it in a newspaper as it would reach a larger group of people and educate them on why they should preserve their native language. Language death Approximately 7000 languages exist in todays world and this number is rapidly dwindling, is it a cause for concern? As globalization spreads around the world, it is natural that smaller communities would like to move out of their isolation and seek interaction with the rest of the world. The number of languages dying is sorrowful. People naturally tend to shift their language use due to globalization and they leave behind their native language if it is not spoken by a lot of people. Asking them to hold onto a language they do not want anymore and preserve it, just for the sake of linguists and not the community itself, it is a bit too much to ask for, isnt it?But theres actually more to it than what meets the eye. Why fight this? A national geographic study states that every 14 days a language dies. By 2100 more than half of the languages spoken on this earth may disappear, taking away with them a wealth of knowledge on world history, culture and natural environment. Language is the road map of a culture. It tells you where its people come from and where they are going. Rita Mae Brown This quote by the American writer Rita Mae Brown gives us an insight into why preserving a language is of importance. A language defines a culture, through the people who speak it. Every language has words that describe a particular cultural practice or idea, when translated into another language, the precise meaning might not come across. What we essentially lose is cultural heritage. The way of expressing the relationship with nature, with the world, it is also the way in which people express humor, their love, their life; most importantly communicating effectively with family is lost. Languages are living, breathing organisms holding connections that define a culture. When a language dies a culture is lost. Because of the close links language and identity share, if an individual or group thinks of their language as useless, they think of their identity as the same. This could have adverse effects; it could lead to depression, drug abuse and social disruption. And as parents no longer pass on their language to their children the connection between grandparents and children is lost which leads to traditional values not being handed on and theres a vacuum that remains where people for generations realize they have lost something. Many languages are in danger of extinction thathave rich oral cultures with stories, songs, and histories passed on from generation to generation, but with no particular written form. Much of what us humans know about nature is encoded in oral languages. For thousands of years now native groups have interacted closely with the natural world and have insightful understanding on local lands, plants, animals, and ecosystems. Many still are not documented by science itself. Therefore studying indigenous languages proves to be beneficial while learning about the environment and conservation. Sanskrit is one such ancient language that is loosing its prominence and its speakers decreasing everyday. It was said to be the mother of all languages. Sanskrit is not practically used and maybe that is one of reasons of its decline but I believe it should be conserved because of the traditional values it possesses and because of its richness in culture. Take for instance Arthashastra, it is an Indian treatise written in Sanskrit which deals with statecraft, economic policy and military strategy it was written all the way back in 4th century BC. These concepts are not new and modern, they have been around for a long time now, if we do not conserve Sanskrit we will loose all of this valuable knowledge and also lose a piece of history. All is not lost for those who want the smaller languages to survive. Another such language dying out is Palenquero. Palenquero is thought to the one and only Spanish-based Creole language in Latin America. Fewer than half of the community speaks it. It is spoken in the village of San Basilio De Palenque. Many children and young adults understand the language and pronounce a few phrases, which is a great sign as the village of San Basilio De Palenque is trying to preserve its language and spread it, the villages resilience is commendable. And other communities whose languages are close to extinction should look at them as an example. Why do languages die out though? Throughout history, the languages of powerful groups and imperial countries have spread while the languages of the smaller cultures and groups have become extinct. This happens due to official language policies and also the allure of speaking a highly prestigious global language such as English. These trends explain why a small country like Bolivia would have more of language diversity rather than a big country like the USA. As big languages spread, children whose parents speak a comparatively smaller language tend to grow up learning the more dominant language. Those children may never learn the smaller language, or they may just forget it as it falls out of use. These trends have occurred throughout history, but what is alarming and worrying is the rate at which languages are disappearing, it has significantly accelerated over the recent years. Associations and initiatives such as Enduring voices, Living tongue, and the endangered languages project by Google are trying to preserve language and that is a sign of hope. The organizations that are involved and that have come up with these ideas are national geographic and Google. The death of a language is an indication of a human crisis: the loss of a store of wisdom, the sense of a community being thrown away. As we try to stop global warming and save the environment, we should also try and save our languages, as they are an integral part of our heritage.

The Great Saljuq Sultanate :: essays research papers

The Great Saljuq Sultanate! Although the Turks had played an important role in the Islamic world, before the 11th century, the arrival of the Saljuq Turks marks a new era in Islamic history. The purpose of this paper is to discuss the role of the Saljuq Turks’ in Islam. In doing so, the paper will be divided in two parts. The first part will present the historical background of the arrival of the Saljuqs and their participation in Islamic politics. The second part will discuss the contribution of the Saljuq administrative system to Islamic politics. Turks had been participating in the Islamic politics well before the 11th century. For example, the Mamluks and the Ghaznawids were from Turkish origin. What made the Saljuqs distinct from these earlier Turks is how they have penetrated Islamic politics. Before the 11th century, Mamluks and the Ghaznawids were slaves recruited as individuals and took power from inside. However, the Saljuqs came in as organized tribal groups and conquered the Persia and much of the border lands.   Ã‚  Ã‚  Ã‚  Ã‚  The Saljuq conquest marked the beginning of Turkish rule in Persia. This rule arguably lasted until 1925. In 426/1035, the Saljuq brothers Toghril Beg and Chaghri Beg led the Saljuq tribe to move into Khurasan. The brothers battled against the Ghaznawids to take over Khurasan. According to the course reader, the Khurasan population accepted the Saljuq rule just as they had earlier accepted the Ghaznawids. Five years later the Ghaznawids regrouped and waged war against the Saljuqs. The Ghaznawids were defeated and never came back. The Beg brothers ruled together until the death of Chaghri Beg in 452/1060. Morgan notes that this shared power between the two brothers was â€Å"in accordance with the Turkish conception of the nature of political sovereignty, which the Saljuqs had brought with them from central Asia.† After they had defeated the Ghaznawids from Khurasan, the brothers perceived that their major threat was the Buyids in western Persia and Iraq. It did not take long for the Saljuqs to eliminate the Buyids from Persia and Iraq. Toghril conquered Baghdad in 447/1055 and restored the Sunni rule. Consequently, Caliph Qa’im granted the title of Sultan on Toghril. Although the Buyids and the Caliph coexisted in Baghdad, their relationship was not based on the Caliph’s consent. The Buyids knew that most of their subjects in Baghdad were Sunni and half of their army were of Turkish origin whom may ally with the Sunnis.

Advertising Impact

Quant Mark Econ (2009) 7:207–236 DOI 10. 1007/s11129-009-9066-z The effect of advertising on brand awareness and perceived quality: An empirical investigation using panel data C. Robert Clark  · Ulrich Doraszelski  · Michaela Draganska Received: 11 December 2007 / Accepted: 2 April 2009 / Published online: 8 May 2009  © Springer Science + Business Media, LLC 2009 Abstract We use a panel data set that combines annual brand-level advertising expenditures for over three hundred brands with measures of brand awareness and perceived quality from a large-scale consumer survey to study the effect of advertising.Advertising is modeled as a dynamic investment in a brand’s stocks of awareness and perceived quality and we ask how such an investment changes brand awareness and quality perceptions. Our panel data allow us to control for unobserved heterogeneity across brands and to identify the effect of advertising from the time-series variation within brands. They also allow us to account for the endogeneity of advertising through recently developed dynamic panel data estimation techniques. We ? nd that advertising has consistently a signi? cant positive effect on brand awareness but no signi? ant effect on perceived quality. Keywords Advertising  · Brand awareness  · Perceived quality  · Dynamic panel data methods JEL Classi? cation L15  · C23  · H37 C. R. Clark Institute of Applied Economics, HEC Montreal and CIRPEE, 3000 Chemin de la Cote-Sainte-Catherine, Montreal, Quebec H3T 2A7, Canada e-mail: robert. [email  protected] ca U. Doraszelski Department of Economics, Harvard University, 1805 Cambridge Street, Cambridge, MA 02138, USA e-mail: [email  protected] edu ) M. Draganska (B Graduate School of Business, Stanford University, Stanford, CA 94305-5015, USA e-mail: [email  protected] tanford. edu 208 C. R. Clark et al. 1 Introduction In 2006 more than $280 billion were spent on advertising in the U. S. , well above 2% of GDP. By inve sting in advertising, marketers aim to encourage consumers to choose their brand. For a consumer to choose a brand, two conditions must be satis? ed: First, the brand must be in her choice set. Second, the brand must be preferred over all the other brands in her choice set. Advertising may facilitate one or both of these conditions. In this research we empirically investigate how advertising affects these two conditions.To disentangle the impact on choice set from that on preferences, we use actual measures of the level of information possessed by consumers about a large number of brands and of their quality perceptions. We compile a panel data set that combines annual brand-level advertising expenditures with data from a large-scale consumer survey, in which respondents were asked to indicate whether they were aware of different brands and, if so, to rate them in terms of quality. These data offer the unique opportunity to study the role of advertising for a wide range of brands ac ross a number of different product categories.The awareness score measures how well consumers are informed about the existence and the availability of a brand and hence captures directly the extent to which the brand is part of consumers’ choice sets. The quality rating measures the degree of subjective vertical product differentiation in the sense that consumers are led to perceive the advertised brand as being better. Hence, our data allow us to investigate the relationship between advertising and two important dimensions of consumer knowledge.The behavioral literature in marketing has highlighted the same two dimensions in the form of the size of the consideration set and the relative strength of preferences (Nedungadi 1990; Mitra and Lynch 1995). It is, of course, possible that advertising also affects other aspects of consumer knowledge. For example, advertising may generate some form of subjective horizontal product differentiation that is unlikely to be re? ected in ei ther brand awareness or perceived quality. In a recent paper Erdem et al. (2008), however, report that advertising focuses on horizontal attributes only for one out of the 19 brands examined.Understanding the channel through which advertising affects consumer choice is important for researchers and practitioners alike for several reasons. For example, Sutton’s (1991) bounds on industry concentration in large markets implicitly assume that advertising increases consumers’ willingness to pay by altering quality perceptions. While pro? ts increase in perceived quality, they may decrease in brand awareness (Fershtman and Muller 1993; Boyer and Moreaux 1999), thereby stalling the competitive escalation in advertising at the heart of the endogenous sunk cost theory.Moreover, Doraszelski and Markovich (2007) show that even in small markets industry dynamics can be very different depending on the nature of advertising. From an empirical perspective, when estimating a demand mo del, advertising could be modeled Effect of advertising on brand awareness and perceived quality 209 as affecting the choice set or as affecting the utility that the consumer derives from a brand. If the role of advertising is mistakenly speci? ed as affecting quality perceptions (i. e. , preferences) rather than brand awareness as it often is, then the estimated parameters may be biased.In her study of the U. S. personal computer industry, Sovinsky Goeree (2008) ? nds that traditional demand models overstate price elasticities because they assume that consumers are aware of—and hence choose among—all brands in the market when in actuality most consumers are aware of only a small fraction of brands. For our empirical analysis we develop a dynamic estimation framework. Brand awareness and perceived quality are naturally viewed as stocks that are built up over time in response to advertising (Nerlove and Arrow 1962).At the same time, these stocks depreciate as consumers forget past advertising campaigns or as an old campaign is superseded by a new campaign. Advertising can thus be thought of as an investment in brand awareness and perceived quality. The dynamic nature of advertising leads us to a dynamic panel data model. In estimating this model we confront two important problems, namely unobserved heterogeneity across brands and the potential endogeneity of advertising. We discuss these below. When estimating the effect of advertising across brands we need to keep in mind that they are different in many respects.Unobserved factors that affect both advertising expenditures and the stocks of perceived quality and awareness may lead to spurious positive estimates of the effect of advertising. Put differently, if we detect an effect of advertising, then we cannot be sure if this effect is causal in the sense that higher advertising expenditures lead to higher brand awareness and perceived quality or if it is spurious in the sense that different brand s have different stocks of perceived quality and awareness as well as advertising expenditures.For example, although in our data the brands in the fast food category on average have high advertising and high awareness and the brands in the cosmetics and fragrances category have low advertising and low awareness, we cannot infer that advertising boosts awareness. We can only conclude that the relationship between advertising expenditures, perceived quality, and brand awareness differs from category to category or even from brand to brand. Much of the existing literature uses cross-sectional data to discern a relationship between advertising expenditures and perceived quality (e. g. Kirmani and Wright 1989; Kirmani 1990; Moorthy and Zhao 2000; Moorthy and Hawkins 2005) in an attempt to test the idea that consumers draw inferences about the brand’s quality from the amount that is spent on advertising it (Nelson 1974; Milgrom and Roberts 1986; Tellis and Fornell 1988). With cross -sectional data it is dif? cult to account for unobserved heterogeneity across brands. Indeed, if we neglect permanent differences between brands, then we ? nd that both brand awareness and perceived quality are positively correlated with advertising expenditures, thereby replicating the earlier studies.Once we make full use of our panel data and account for unobserved 210 C. R. Clark et al. heterogeneity, however, the effect of advertising expenditures on perceived quality disappears. 1 Our estimation equations are dynamic relationships between a brand’s current stocks of perceived quality and awareness on the left-hand side and the brand’s previous stocks of perceived quality and awareness as well as its own and its rivals’ advertising expenditures on the right-hand side. In this context, endogeneity arises for two reasons.First, the lagged dependent variables are by construction correlated with all past error terms and therefore endogenous. As a consequence, traditional ? xed-effect methods are necessarily inconsistent. 2 Second, advertising expenditures may also be endogenous for economic reasons. For instance, media coverage such as news reports may affect brand awareness and perceived quality beyond the amount spent on advertising. To the extent that these shocks to the stocks of perceived quality and awareness of a brand feed back into decisions about advertising, say because the brand manager opts to advertise less if a news report has generated suf? ient awareness, they give rise to an endogeneity problem. To resolve the endogeneity problem we use the dynamic panel data methods developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). The key advantage is that these methods do not rely on the availability of strictly exogenous explanatory variables or instruments. This is an appealing methodology that has been widely applied (e. g. , Acemoglu and Robinson 2001; Durlauf et al. 2005; Zhang and L i 2007) because valid instruments are often hard to come by. Further, since these methods involve ? st differencing, they allow us to control for unobserved factors that affect both advertising expenditures and the stocks of perceived quality and awareness and may lead to spurious positive estimates of the effect of advertising. In addition, our approach allows for factors other than advertising to affect a brand’s stock of perceived quality and awareness to the extent that these factors are constant over time. Our main ? nding is that advertising expenditures have a signi? cant positive effect on brand awareness but no signi? cant effect on perceived quality.These results appear to be robust across a wide range of speci? cations. Since awareness is the most basic kind of information a consumer can have for a brand, we conclude that an important role of advertising is information provision. On the other hand, our results indicate that advertising is not likely to alter consum ers’ quality perceptions. This conclusion calls for a reexamination of the implicit assumption underlying Sutton’s (1991) endogenous sunk cost theory. It also suggests that advertising should be modeled as affecting the choice set and not just utility when estimating demand.Finally, our ? ndings lend empirical 1 Another way to get around this issue is to take an experimental approach, as in Mitra and Lynch (1995). 2 This source of endogeneity is not tied to advertising in particular; rather it always arises in estimating dynamic relationships in the presence of unobserved heterogeneity. An exception is the (rather unusual) panel-data setting where one has T > ? instead of N > ?. In this case the within estimator is consistent (Bond 2002, p. 5). Effect of advertising on brand awareness and perceived quality 211 upport to the view that advertising is generally procompetitive because it disseminates information about the existence, the price, and the attributes of product s more widely among consumers (Stigler 1961; Telser 1964; Nelson 1970, 1974). The remainder of the paper proceeds as follows. In Sections 2 and 3 we explain the dynamic investment model and the corresponding empirical strategy. In Section 4 we describe the data and in Section 5 we present the results of the empirical analysis. Section 6 concludes. 2 Model speci? cation We develop an empirical model based on the classic advertising-as-investment model of Nerlove and Arrow (1962).Related empirical models are the basis of current research on advertising (e. g. , Naik et al. 1998; Dube et al. 2005; Doganoglu and Klapper 2006; Bass et al. 2007). Naik et al. (1998), in particular, ? nd that the Nerlove and Arrow (1962) model provides a better ? t than other models that have been proposed in the literature such as Vidale and Wolfe (1957), Brandaid (Little 1975), Tracker (Blattberg and Golanty 1978), and Litmus (Blackburn and Clancy 1982). We extend the Nerlove and Arrow (1962) framework in two respects. First, we allow a brand’s stocks of awareness and perceived quality to be affected by the advertising of its competitors.This approach captures the idea that advertising takes place in a competitive environment where brands vie for the attention of consumers. The advertising of competitors may also be bene? cial to a brand if it draws attention to the entire category and thus expands the relevant market for the brand (e. g. , Nedungadi 1990; Kadiyali 1996). Second, we allow for a stochastic component in the effect of advertising on the stocks of awareness and perceived quality to re? ect the success or failure of an advertising campaign and other unobserved in? uences such as the creative quality of the advertising copy, media selection, or scheduling.More formally, we let Qit be the stock of perceived quality of brand i at the start of period t and Ait the stock of its awareness. We further let Eit? 1 denote the advertising expenditures of brand i over the cou rse of period t ? 1 and E? it? 1 = (E1t? 1 , . . . , Ei? 1t? 1 , Ei+1t? 1 , . . . , Ent? 1 ) the advertising expenditures of its competitors. Then, at the most general level, the stocks of perceived quality and awareness of brand i evolve over time according to the laws of motion Qit = git (Qit? 1 , Eit? 1 , E? it? 1 , ? it ), Ait = hit (Ait? 1 , Eit? 1 , E? it? 1 , ? t ), where git ( ·) and hit ( ·) are brand- and time-speci? c functions. The idiosyncratic error ? it captures the success or failure of an advertising campaign along with all other omitted factors. For example, the quality of the advertising campaign may matter just as much as the amount spent on it. By recursively substituting 212 C. R. Clark et al. for the lagged stocks of perceived quality and awareness we can write the current stocks as functions of all past advertising expenditures and the current and all past error terms. This shows that these shocks to brand awareness and perceived quality are persistent ov er time.For example, the effect of a particularly good (or bad) advertising campaign may linger and be felt for some time to come. We model the effect of competitors’ advertising on brand awareness and perceived quality in two ways. First, we consider a brand’s â€Å"share of voice. † We use its advertising expenditures, Eit? 1 , relative to the average amount spent on advertising by rival brands in the brand’s subcategory or category, E? it? 1 . 3 To the extent that brands compete with each other for the attention of consumers, a brand may have to outspend its rivals to cut through the clutter.If so, then what is important may not be the absolute amount spent on advertising but the amount relative to rival brands. Second, we consider the amount of advertising in the entire market by including the average amount spent on advertising by rival brands in the brand’s subcategory or category. Advertising is market expanding if it attracts consumers to t he entire category but not necessarily to a particular brand. In this way, competitors’ advertising may have a positive in? uence on, say, brand awareness. Taken together, our estimation equations are Qit = ? i + ? t + ? Qit? 1 + f (Eit? 1 , E? it? 1 ) + ? t , Ait = ? i + ? t + ? Ait? 1 + f (Eit? 1 , E? it? 1 ) + ? it . (1) (2) Here ? i is a brand effect that captures unobserved heterogeneity across brands and ? t is a time effect to control for possible systematic changes over time. The time effect may capture, for example, that consumers are systematically informed about a larger number of brands due to the advent of the internet and other alternative media channels. Through the brand effect we allow for factors other than advertising to affect a brand’s stocks of perceived quality and awareness to the extent that these factors are constant over time.For example, consumers may hear about a brand and their quality perceptions may be affected by word of mouth. Similarl y, it may well be the case that consumers in the process of purchasing a brand become more informed about it and that their quality perceptions change, especially for high-involvement brands. Prior to purchasing a car, say, many consumers engage in research about the set of available cars and their respective characteristics, including quality ratings from sources such as car magazines and Consumer Reports.If these effects do not vary over time, then we fully account for them in our estimation because the dynamic panel data methods we employ involve ? rst differencing. The parameter ? measures how much of last period’s stocks of perceived quality and awareness are carried forward into this period’s stocks; 1 ? ? can 3 The Brandweek Superbrands survey reports on only the top brands (in terms of sales) in each subcategory or category. The number of brands varies from 3 for some subcategories to 10 for others. We therefore use the average, rather than the sum, of competit ors’ advertising.Effect of advertising on brand awareness and perceived quality 213 therefore be interpreted as the rate of depreciation of these stocks. Note that in the estimation we allow all parameters to be different across our estimation equations. For example, we do not presume that the carryover rates for perceived quality and brand awareness are the same. The function f ( ·) represents the response of brand awareness and perceived quality to the advertising expenditures of the brand and potentially also those of its rivals. In the simplest case absent competition we specify this function as 2 f (Eit? ) = ? 1 Eit? 1 + ? 2 Eit? 1 . This functional form is ? exible in that it allows for a nonlinear effect of advertising expenditures but does not impose one. Later on in Section 5. 6 we demonstrate the robustness of our results by considering a number of additional functional forms. To account for competition in the share-of-voice speci? cation, we set f Eit? 1 , E? it? 1 = ? 1 Eit? 1 E? it? 1 + ? 2 Eit? 1 E? it? 1 2 and in the total-advertising speci? cation, we set 2 f Eit? 1 , E? it? 1 = ? 1 Eit? 1 + ? 2 Eit? 1 + ? 3 E? it? 1 . Estimation strategy Equations 1 and 2 are dynamic relationships that feature lagged dependent variables on the right-hand side. When estimating, we confront the problems of unobserved heterogeneity across brands and the endogeneity of advertising. In our panel-data setting, ignoring unobserved heterogeneity is akin to dropping the brand effect ? i from Eqs. 1 and 2 and then estimating them by ordinary least squares. Since this approach relies on both cross-sectional and time-series variation to identify the effect of advertising, we refer to it as â€Å"pooled OLS† (POLS) in what follows.To account for unobserved heterogeneity we include a brand effect ? i and use the within estimator that treats ? i as a ? xed effect. We follow the usual convention in microeconomic applications that the term â€Å"? xed effectâ €  does not necessarily mean that the effect is being treated as nonrandom; rather it means that we are allowing for arbitrary correlation between the unobserved brand effect and the observed explanatory variables (Wooldridge 2002, p. 251). The within estimator eliminates the brand effect by subtracting the within-brand mean from Eqs. 1 and 2. Hence, the identi? ation of the slope parameters that determine the effect of advertising relies solely on variation over time within brands; the information in the between-brand cross-sectional relationship is not used. We refer to this approach as â€Å"? xed effects† (FE). While FE accounts for unobserved heterogeneity, it suffers from an endogeneity problem. In our panel-data setting, endogeneity arises for two reasons. First, since Eqs. 1 and 2 are inherently dynamic, the lagged stocks of perceived 214 C. R. Clark et al. quality and awareness may be endogenous. More formally, Qit? 1 and Ait? 1 are by construction correlated with ? s for s < t. The within estimator subtracts the within-brand mean from Eqs. 1 and 2. The resulting regressor, say Qit? 1 ? Qi in the case of perceived quality, is correlated with the error term ? it ? ?i since ? i contains ? it? 1 along with all higher-order lags. Hence, FE is necessarily inconsistent. Second, advertising expenditures may also be endogenous for economic reasons. For instance, media coverage such as news reports may directly affect brand awareness and perceived quality. Our model treats media coverage other than advertising as shocks to the stocks of perceived quality and awareness.To the extent that these shocks feed back into decisions about advertising, say because the brand manager opts to advertise less if a news report has generated suf? cient awareness, they give rise to an endogeneity problem. More formally, it is reasonable to assume that Eit? 1 , the advertising expenditures of brand i over the course of period t ? 1, are chosen at the beginning of perio d t ? 1 with knowledge of ? it? 1 and higher-order lags and that therefore Eit? 1 is correlated with ? is for s < t. We apply the dynamic panel-data method proposed by Arellano and Bond (1991) to deal with both unobserved heterogeneity and endogeneity.This methodology has the advantage that it does not rely on the availability of strictly exogenous explanatory variables or instruments. This is welcome because instruments are often hard to come by, especially in panel-data settings: The problem is ? nding a variable that is a good predictor of advertising expenditures and is uncorrelated with shocks to brand awareness and perceived quality; ? nding a variable that is a good predictor of lagged brand awareness and perceived quality and uncorrelated with current shocks to brand awareness and perceived quality is even less obvious.The key idea of Arellano and Bond (1991) is that if the error terms are serially uncorrelated, then lagged values of the dependent variable and lagged values of the endogenous right-hand-side variables represent valid instruments. To see this, take ? rst differences of Eq. 1 to obtain Qit ? Qit? 1 = (? t ? ?t? 1 ) + ? (Qit? 1 ? Qit? 2 ) + f (Eit? 1 ) ? f (Eit? 2 ) + (? it ? ?it? 1 ), (3) where we abstract from competition to simplify the notation. Eliminating the brand effect ? i accounts for unobserved heterogeneity between brands. The remaining problem with estimating Eq. 3 by least-squares is that Qit? 1 ? Qit? is by construction correlated with ? it ? ?it? 1 since Qit? 1 is correlated with ? it? 1 by virtue of Eq. 1. Moreover, as we have discussed above, Eit? 1 may also be correlated with ? it? 1 for economic reasons. We take advantage of the fact that we have observations on a number of periods in order to come up with instruments for the endogenous variables. In particular, this is possible starting in the third period where Eq. 3 becomes Qi3 ? Qi2 = (? 3 ? ?2 ) + ? (Qi2 ? Qi1 ) + f (Ei2 ) ? f (Ei1 ) + (? i3 ? ?i2 ). Effect of adve rtising on brand awareness and perceived quality 215 In this case Qi1 is a valid instrument for (Qi2 ?Qi1 ) since it is correlated with (Qi2 ? Qi1 ) but uncorrelated with (? i3 ? ?i2 ) and, similarly, Ei1 is a valid instrument for ( f (Ei2 ) ? f (Ei1 )). In the fourth period Qi1 and Qi2 are both valid instruments since neither is correlated with (? i4 ? ?i3 ) and, similarly, Ei1 and Ei2 are both valid instruments. In general, for lagged dependent variables and for endogenous right-hand-side variables, levels of these variables that are lagged two or more periods are valid instruments. This allows us to generate more instruments for later periods. The resulting estimator is referred to as â€Å"difference GMM† (DGMM).A potential dif? culty with the DGMM estimator is that lagged levels may be poor instruments for ? rst differences when the underlying variables are highly persistent over time. Arellano and Bover (1995) and Blundell and Bond (1998) propose an augmented estimator in which the original equations in levels are added to the system. The idea is to create a stacked data set containing differences and levels and then to instrument differences with levels and levels with differences. The required assumption is that brand effects are uncorrelated with changes in advertising expenditures.This estimator is commonly referred to as â€Å"system GMM† (SGMM). In Section 5 we report and compare results for DGMM and SGMM. It is important to test the validity of the instruments proposed above. Following Arellano and Bond (1991) we report a Hansen J test for overidentifying restrictions. This test examines whether the instruments are jointly exogenous. We also report the so-called difference-in-Hansen J test to examine speci? cally whether the additional instruments for the level equations used in SGMM (but not in DGMM) are valid. Arellano and Bond (1991) further develop a test for second-order serial correlation in the ? st differences of the error te rms. As described above, both GMM estimators require that the levels of the error terms be serially uncorrelated, implying that the ? rst differences are serially correlated of at most ? rst order. We caution the reader that the test for second-order serial correlation is formally only de? ned if the number of periods in the sample is greater than or equal to 5 whereas we observe a brand on average for just 4. 2 periods in our application. Our preliminary estimates suggest that the error terms are unlikely to be serially uncorrelated as required by Arellano and Bond (1991).The AR(2) test described above indicates ? rst-order serial correlation in the error terms. An AR(3) test for third-order serial correlation in the ? rst differences of the error terms, however, indicates the absence of second-order serial correlation in the error terms. 4 In this case, Qit? 2 and Eit? 2 are no longer valid instruments for Eq. 3. Intuitively, because Qit? 2 is correlated with ? it? 2 by virtue of Eq. 1 and ? it? 2 is correlated with ? it? 1 by ? rst-order serial correlation, Qit? 2 is correlated 4 Of course, the AR(3) test uses less observations than the AR(2) test and is therefore also less powerful. 16 C. R. Clark et al. with ? it? 1 in Eq. 3, and similarly for Eit? 2 . Fortunately, however, Qit? 3 and Eit? 3 remain valid instruments because ? it? 3 is uncorrelated with ? it? 1 . We carry out the DGMM and SGMM estimation using STATA’s xtabond2 routine (Roodman 2007). We enter third and higher lags of either brand awareness or perceived quality, together with third and higher lags of advertising expenditures as instruments. In addition to these â€Å"GMM-style† instruments, for the difference equations we enter the time dummies as â€Å"IV-style† instruments. We also apply the ? ite-sample correction proposed by Windmeijer (2005) which corrects for the two-step covariance matrix and substantially increases the ef? ciency of both GMM estimators. Finally, we compute standard errors that are robust to heteroskedasticity and arbitrary patterns of serial correlation within brands. 4 Data Our data are derived from the Brandweek Superbrands surveys from 2000 to 2005. Each year’s survey lists the top brands in terms of sales during the past year from 25 broad categories. Inside these categories are often a number of more narrowly de? ned subcategories. Table 1 lists the categories along with their subcategories.The surveys report perceived quality and awareness scores for the current year and the advertising expenditures for the previous year by brand. Perceived quality and awareness scores are calculated by Harris Interactive in their Equitrend brand-equity study. Each year Harris Interactive surveys online between 20, 000 and 45, 000 consumers aged 15 years and older in order to determine their perceptions of a brand’s quality and its level of awareness for approximately 1, 000 brands. 5 To ensure that the respondents accu rately re? ect the general population their responses are propensity weighted. Each respondent rates around 80 of these brands.Perceived quality is measured on a 0–10 scale, with 0 meaning unacceptable/poor and 10 meaning outstanding/ extraordinary. Awareness scores vary between 0 and 100 and equal the percentage of respondents that can rate the brand’s quality. The quality rating is therefore conditional on the respondent being aware of the brand. 6 5 The exact wording of the question is: â€Å"We will display for you a list of brands and we are asking you to rate the overall quality of each brand using a 0 to 10 scale, where ‘0’ means ‘Unacceptable/Poor Quality’, ‘5’ means ‘Quite Acceptable Quality’ and ‘10’ means ‘Outstanding/ Extraordinary Quality’.You may use any number from 0 to 10 to rate the brands, or use 99 for ‘No Opinion’ option if you have absolutely no opinion abou t the brand. † Panelists are being incentivized through sweepstakes on a periodic basis but are not paid for a particular survey. 6 The 2000 Superbrands survey does not separately report perceived quality and salience scores. We received these scores directly from Harris Interactive. 2000 is the ? rst year for which we have been able to obtain perceived quality and salience scores for a large number of brands.Starting with the 2004 and 2005 Superbrands surveys, salience is replaced by a new measure called â€Å"familiarity. † For these two years we received salience scores directly from Harris Interactive. The contemporaneous correlation between salience and familiarity is 0. 98 and signi? cant with a p-value of 0. 000. Effect of advertising on brand awareness and perceived quality Table 1 Categories and subcategories 1. Apparel 2. Appliances 3. Automobiles a. general automobiles b. luxury c. subcompact d. sedan/wagon e. trucks/suvs/vans 4. Beer, wine, liquor a. beer b. wine c. malternatives d. iquor 5. Beverages a. general b. new age/sports/water 6. Computers a. software b. hardware 7. Consumer electronics 8. Cosmetics and fragrances a. color cosmetics b. eye color c. lip color d. women’s fragrances e. men’s fragrances 9. Credit cards 10. Entertainment 11. Fast food 12. Financial services 13. Food a. ready to eat cereal b. cereal bars c. cookies d. cheese e. crackers f. salted snacks g. frozen dinners and entrees Items in italics have been removed 217 h. frozen pizza i. spaghetti sauce j. coffee k. ice cream l. refrigerated orange juice m. refrigerated yogurt n. oy drinks o. luncheon meats p. meat alternatives q. baby formula/electrolyte solutions r. pourable salad dressing 14. Footwear 15. Health and beauty a. bar soap b. toothpaste c. shampoo d. hair color 16. Household a. cleaner b. laundry detergents c. diapers d. facial tissue e. toilet tissue f. automatic dishwater detergent 17. Petrol a. oil companies b. automotive aftercare/ lube 18. Pharmaceutical OTC a. allergy/cold medicine b. stomach/antacids c. analgesics 19. Pharmaceutical prescription 20. Retail 21. Telecommunications 22. Tobacco 23. Toys 24. Travel 25. World Wide WebWe supplement the awareness and quality measures with advertising expenditures that are taken from TNS Media Intelligence and Competitive Media Reporting. These advertising expenditures encompass spending in a wide range of media: Magazines (consumer magazines, Sunday magazines, local magazines, and business-to-business magazines), newspaper (local and national newspapers), television (network TV, spot TV, syndicated TV, and network cable TV), radio (network, national spot, and local), Spanish-language media (magazines, newspapers, and TV networks), internet, and outdoor.After eliminating categories and subcategories where observations are not at the brand level (apparel, entertainment, ? nancial services, retail, world wide web) or where the data are suspect (tobacco), we are left w ith 19 categories (see again Table 1). We then drop all private labels and all brands for which 218 C. R. Clark et al. we do not have perceived quality and awareness scores as well as advertising expenditures for at least two years running. This leaves us with 348 brands. Table 2 contains descriptive statistics for the overall sample and also by category. In the overall sample the average awareness score is 69. 5 and the average perceived quality score is 6. 36. The average amount spent on advertising is around $66 million per year. There is substantial variation in these measures across categories. The variation in perceived quality (coef? cient of variation is 0. 11 overall, ranging from 0. 04 for appliances to 0. 13 for computers) tends to be lower than the variation in brand awareness (coef? cient of variation is 0. 28 overall, ranging from 0. 05 for appliances to 0. 46 for telecommunications), in line with the fact the quality rating is conditional on the respondent being aware of the brand.The contemporaneous correlation between brand awareness and perceived quality is 0. 60 and signi? cant with a p-value of 0. 000. The contemporaneous correlation between advertising expenditures and the change in brand awareness is 0. 0488 and signi? cant with a p-value of 0. 0985 and the contemporaneous correlation between advertising expenditures and the change in perceived quality is 0. 0718 and signi? cant with a p-value of 0. 0150. These correlations anticipate the spurious correlation between both brand awareness and perceived quality and advertising expenditures if permanent differences between brands are neglected (POLS estimator).We will see though that the effect of advertising expenditures on perceived quality Table 2 Descriptive statistics # obs # brands Brand awareness Perceived Advertising (0–100) quality (0–10) ($1,000,000) Mean Std. dev. Mean Std. dev. Mean Std. dev. Overall Appliances Automobiles Beer, wine, liquor Beverages Computers Cons umer electronics Cosmetics and fragrances Credit cards Fast food Food Footwear Health and beauty Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 1,478 348 21 137 98 95 79 29 70 29 60 247 38 54 128 48 56 31 52 25 181 4 30 24 22 17 7 19 6 12 65 8 11 31 13 15 10 11 5 38 69. 5 85. 09 67. 81 62. 23 84. 57 59. 80 67. 83 49. 37 70. 97 93. 83 80. 18 64. 95 82. 50 73. 83 60. 52 76. 96 29. 97 49. 33 72. 12 59. 48 19. 43 4. 54 6. 72 10. 13 13. 84 23. 05 18. 68 15. 75 18. 08 5. 32 14. 94 18. 98 9. 80 16. 03 17. 19 13. 89 9. 69 22. 86 9. 74 15. 43 6. 36 7. 35 6. 51 5. 68 6. 51 6. 41 6. 60 5. 83 6. 24 6. 28 6. 66 6. 39 6. 67 6. 66 5. 95 6. 79 5. 54 5. 28 6. 95 6. 26 0. 70 0. 32 0. 59 0. 72 0. 58 0. 81 0. 73 0. 52 0. 73 0. 42 0. 65 0. 42 0. 41 0. 56 0. 30 0. 37 0. 67 0. 52 0. 32 0. 52 66. 21 118. 52 41. 87 33. 19 99. 85 64. 62 36. 78 45. 11 41. 33 42. 19 130. 43 130. 7 104. 83 160. 66 38. 02 47. 48 174. 54 109. 77 214. 80 156. 23 13. 93 13. 81 40. 27 46. 89 27. 28 33. 44 21. 80 25. 43 33. 54 34. 65 38. 71 18. 13 76. 23 36. 40 367. 93 360. 54 108. 55 54. 36 25. 41 25. 88 Effect of advertising on brand awareness and perceived quality 219 disappears once unobserved heterogeneity is accounted for (FE and GMM estimators). The intertemporal correlation is 0. 98 for brand awareness, 0. 95 for perceived quality, and 0. 93 for advertising expenditures. This limited amount of intertemporal variation warrants preferring the SGMM over the DGMM estimator.At the same time, however, it constrains how ? nely we can â€Å"slice† the data, e. g. , by isolating a brand-speci? c effect of advertising expenditures on brand awareness and perceived quality. Since the FE, DGMM, and SGMM estimators rely on within-brand acrosstime variation, it is important to ensure that there is a suf? cient amount of within-brand variation in brand awareness, perceived quality, and advertising expenditures. Table 3 presents a decomposition of the standard devia tion in these variables into an across-brands and a within-brand component for the overall sample and also by category.The across-brands standard deviation is a measure of the cross-sectional variation and the within-brand standard deviation is a measure of the time-series variation. The across-brands standard deviation of brand awareness is about six times larger than the within-brand standard deviation. This ratio varies across categories and ranges from 2 for automobiles, beer, wine, liquor, and pharmaceutical prescription to 6 for health and beauty and pharmaceutical OTC. In case of perceived quality the ratio is about 4 (ranging from 1 for telecommunications to 5 for consumer electronics, credit cards, and household).Hence, while there is more crosssectional than time-series variation in our sample, the time-series variation is substantial for both brand awareness and perceived quality. Figure 1 illustrates Table 3 Variance decomposition Brand awareness (0–100) Across Ov erall Appliances Automobiles Beer, wine, liquor Beverages Computers Consumer electronics Cosmetics and fragrances Credit cards Fast food Food Footwear Health and beauty Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 20. 117 5. 282 6. 209 10. 181 13. 435 23. 094 19. 952 18. 054 19. 568 6. 132 16. 241 20. 417 10. 36 16. 719 20. 179 13. 339 9. 393 21. 659 11. 217 16. 063 Within 3. 415 1. 334 3. 281 4. 105 2. 915 3. 843 5. 611 3. 684 3. 903 1. 660 2. 255 4. 267 1. 772 3. 896 3. 669 2. 363 5. 772 5. 604 3. 589 3. 216 Perceived quality (0–10) Across 0. 726 0. 323 0. 561 0. 705 0. 582 0. 850 0. 800 0. 563 0. 788 0. 361 0. 702 0. 388 0. 397 0. 561 0. 415 0. 336 0. 753 0. 452 0. 360 0. 516 Within 0. 176 0. 148 0. 141 0. 186 0. 190 0. 313 0. 167 0. 208 0. 159 0. 202 0. 134 0. 167 0. 136 0. 113 0. 116 0. 129 0. 230 0. 334 0. 127 0. 153 Advertising ($1,000,000) Across 100. 823 28. 965 54. 680 41. 713 37. 505 110. 362 105. 49 38. 446 118. 05 9 159. 306 15. 655 45. 791 27. 054 18. 789 27. 227 16. 325 38. 648 317. 434 61. 419 22. 136 Within 43. 625 21. 316 32. 552 12. 406 13. 372 65. 909 114. 381 20. 053 43. 415 33. 527 7. 998 7. 640 19. 075 16. 672 20. 496 9. 080 27. 919 178. 406 18. 584 10. 909 220 .025 . 2 C. R. Clark et al. .02 Density . 01 . 015 0 .005 0 20 40 60 80 Mean brand awareness 100  ® 0 –30 .05 Density . 1 .15 –20 –10 0 10 20 Demeaned brand awareness 30  ® .8 .6 Density . 4 0 .2 0 2 4 6 Mean perceived quality 8 10  ® 0 –1. 5 1 Density 2 3 –1 –. 5 0 . 5 1 Demeaned perceived quality 1. 5  ® .015 Density . 005 . 01 0 0 00 400 600 800 1000 1200 1400 Mean advertising expenditures (millions of $)  ® 0 –600 –400 –200 0 200 400 600 Demeaned advertising expenditures (millions of $)  ® Fig. 1 Variance decomposition. Histogram of brand-mean of brand awareness, perceived quality, and advertising expenditures (left panels) and histogram of de-mean ed brand awareness, perceived quality, and advertising expenditures (right panels) the decomposition for the overall sample. The left panels show histograms of the brand-mean of brand awareness, perceived quality, and advertising expenditures and the right panels show histograms of the de-meaned variables.Again it is evident that the time-series variation is substantial for both brand awareness and perceived quality. 5 Empirical results In Tables 4 and 5 we present a number of different estimates for the effect of advertising expenditures on brand awareness and perceived quality, .005 Density . 01 . 015 .02 .025 Effect of advertising on brand awareness and perceived quality Table 4 Brand awareness POLS Lagged brand awareness Advertising Advertising2 Marginal effect of advertising at: Mean 25th pctl. 50th pctl. 75th pctl. Advertising test: ? 1 = ? 2 = 0 Speci? ation tests: Hansen J Difference-in-Hansen J Arellano & Bond AR(2) Arellano & Bond AR(3) Goodness of ? t measures: R2 -within R2 -between R2 # obs # brands FE DGMM SGMM 221 0. 942*** 0. 223*** 0. 679*** 0. 837*** (0. 00602) (0. 0479) (0. 109) (0. 0266) 0. 00535*** 0. 00687 0. 0152 0. 00627** (0. 00117) (0. 00443) (0. 0139) (0. 00300) ? 0. 00000409*** ? 0. 00000139 ? 0. 0000105 ? 0. 00000524** (0. 000000979) (0. 00000332) (0. 00000745) (0. 00000239) 0. 00481*** (0. 00107) 0. 00527*** (0. 00116) 0. 00514*** (0. 00113) 0. 00470*** (0. 00105) Reject*** 0. 00668 (0. 00412) 0. 00684 (0. 00438) 0. 00679 (0. 00430) 0. 00664 (0. 0405) 0. 0138 (0. 0129) 0. 0150 (0. 0138) 0. 0147 (0. 0135) 0. 0136 (0. 00127) 0. 00558** (0. 00269) 0. 00617** (0. 00296) 0. 00600** (0. 00288) 0. 00544** (0. 00263) Do not reject Do not reject Reject* Do not reject Do not reject Reject** Reject** Do not reject Do not reject 0. 494 0. 940 0. 851 1,148 317 Reject*** 0. 969 1,148 317 819 274 1,148 317 Standard errors in parenthesis * p = 0. 10; ** p = 0. 05; *** p = 0. 01 respectively. Starting with the simplest case absent competition, we present estimates of ? , ? 1 , and ? 2 (the coef? cients on Qit? 1 or Ait? 1 and Eit? 1 and 2 Eit? 1 ) along with the marginal effect ? 1 + 2? Eit? 1 calculated at the mean and the 25th, 50th, and 75th percentiles of advertising expenditures. The POLS estimates in the ? rst column of Tables 4 and 5 suggest a signi? cant positive effect of advertising expenditures on both brand awareness and perceived quality. In both cases we also reject the null hypothesis that advertising plays no role in determining brand awareness and perceived quality (? 1 = ? 2 = 0). Of course, as mentioned above, POLS accounts for neither unobserved heterogeneity nor endogeneity. In the next columns of Tables 4 and 5 we present FE, DGMM, and SGMM estimates that attend to these issues. 7 7 The stimates use at most 317 out of 348 brands because we restrict the sample to brands with data for two years running but use third and higher lags of brand awareness respectively perceived quality and advertising expendit ures as instruments. Different sample sizes are reported for the DGMM and SGMM estimators. Sample size is not a well-de? ned concept in SGMM since this estimator essentially runs on two different samples simultaneously. The xtabond2 routine in STATA reports the size of the transformed sample for DGMM and of the untransformed sample for SGMM. 222 Table 5 Perceived quality FE 0. 391*** (0. 0611) 0. 659*** (0. 204) 1. 47*** (0. 0459) 0. 981*** (0. 0431) DGMM SGMM Objective quality Brand awareness POLS Lagged perceived quality 0. 970*** (0. 0110) Brand awareness Advertising Advertising2 0. 000218** (0. 0000952) ? 0. 000000133 (0. 000000107) 0. 0000822 (0. 000198) 0. 0000000408 (0. 000000162) ?0. 0000195 (0. 000969) 0. 000000108 (0. 000000945) 0. 0000219 (0. 000205) 0. 0000000571 (0. 000000231) 0. 0000649 (0. 000944) 0. 0000000807 (0. 00000308) 0. 937*** (0. 0413) 0. 00596*** (0. 00165) ? 0. 000298 (0. 000256) 0. 000000319 (0. 000000267) Marginal effect of advertising at: Mean 25th pctl. 50th pctl. 75th pctl. 0. 0002** (0. 0000819) 0. 000215** (0. 000933) 0. 000211** (0. 00009) 0. 0001965** (0. 0000793) Do not reject Do not reject Reject*** Do not reject Do not reject Do not reject Reject** Reject** Reject*** Do not reject 0. 0000877 (0. 000180) 0. 000083 (0. 000195) 0. 0000844 (0. 000191) 0. 0000887 (0. 000177) ?5. 13e? 06 (0. 000848) ? 0. 0000174 (0. 000952) ? 0. 0000139 (0. 000922) ? 2. 32e? 06 (0. 000825) 0. 0000295 (0. 000176) 0. 0000230 (0. 000201) 0. 0000249 (0. 000194) 0. 0000310 (0. 000170) 0. 0000594 (0. 000740) 0. 0000642 (0. 000917) 0. 0000623 (0. 000847) 0. 0000588 (0. 000714) Do not reject Do not reject Do not reject Reject*** Do not reject ?0. 000256 (0. 000222) ? 0. 00292 (0. 000251) ? 0. 000282 (0. 000242) ? 0. 000248 (0. 000215) Do not reject Reject** Do not reject Reject*** Do not reject Advertising test: ? 1 = ? 2 = 0 Speci? cation tests: Hansen J Difference-in-Hansen J Arellano & Bond AR(2) Arellano & Bond AR(3) Goodness of ? t measures: R2 -wi thin R2 -between R2 # obs # brands 0. 180 0. 952 0. 909 1,148 317 819 274 1,148 317 Reject** 0. 914 1,148 317 604 178 1,148 317 C. R. Clark et al. Standard errors in parenthesis. SGMM estimates in columns labeled â€Å"Objective quality† and â€Å"Brand awareness† * p = 0. 10; ** p = 0. 05; *** p = 0. 01 Effect of advertising on brand awareness and perceived quality 23 Regardless of the class of estimator we ? nd a signi? cant positive effect of advertising expenditures on brand awareness. With the FE estimator we ? nd that the marginal effect of advertising on awareness at the mean is 0. 00668. It is borderline signi? cant with a p-value of 0. 105 and implies an elasticity of 0. 00638 (with a standard error of 0. 00392). A one-standard-deviation increase of advertising expenditures increase brand awareness by 0. 0408 standard deviations (with a standard error of 0. 0251). The rate of depreciation of a brand’s stock of awareness is estimated to be 1–0. 22 3 or 78% per year.The FE estimator identi? es the effect of advertising expenditures on brand awareness solely from the within-brand across-time variation. The problem with this estimator is that it does not deal with the endogeneity of the lagged dependent variable on the right-hand side of Eq. 2 and the potential endogeneity of advertising expenditures. We thus turn to the GMM estimators described in Section 3. We focus on the more ef? cient SGMM estimator. The coef? cient on the linear term in advertising expenditures is estimated to be 0. 00627 ( p-value 0. 037) and the coef? cient on the quadratic term is estimated to be ? . 00000524 ( p-value 0. 028). These estimates support the hypothesis that the relationship between advertising and awareness is nonlinear. The marginal effect of advertising on awareness is estimated to be 0. 00558 ( p-value 0. 038) at the mean and implies an elasticity of 0. 00533 (with a standard error of 0. 00257). A one-standard-deviation increase of adve rtising expenditures increases brand awareness by 0. 0340 standard deviations (with a standard error of 0. 0164). The rate of depreciation decreases substantially after correcting for endogeneity and is estimated to be 1? . 828 or 17% per year, thus indicating that an increase in a brand’s stock of awareness due to an increase in advertising expenditures persists for years to come. The Hansen J test for overidentifying restrictions indicates that the instruments taken together as a group are valid. Recall from Section 3 that we must assume that an extra condition holds in order for the SGMM estimator to be appropriate. The difference-in-Hansen J test con? rms that it does, as we cannot reject the null hypothesis that the additional instruments for the level equations are valid.While we reject the hypothesis of no second-order serial correlation in the error terms, we cannot reject the hypothesis of no thirdorder serial correlation. This result further validates our instrument ing strategy. However, one may still be worried about the SGMM estimates because DGMM uses a strict subset of the orthogonality conditions of SGMM and we reject the Hansen J test for the DGMM estimates (see Table 4). From a formal statistical point of view, rejecting the smaller set of orthogonality conditions in DGMM is not conclusive evidence that the larger set of orthogonality conditions in SGMM are invalid (Hayashi 2000, pp. 18–221). In Fig. 2 we plot the marginal effect of advertising expenditures on brand awareness over the entire range of advertising expenditures for our SGMM estimates along with a histogram of advertising expenditures. For advertising expenditures between $400 million and $800 million per year the marginal effect of advertising on awareness is no longer signi? cantly different from zero 224 C. R. Clark et al. Marginal effect –. 004 0 . 004 0 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 arginal effect of advertising l ower 90% confidence limit . 015 upper 90% confidence limit 0 0 .005 Density . 01 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400  ® Fig. 2 Pointwise con? dence interval for the marginal effect of advertising expenditures on brand awareness (upper panel) and histogram of advertising expenditures (lower panel). SGMM estimates and, statistically, it is actually negative for very high advertising expenditures over $800 million per year. The former case covers around 1. 9% of observations and the latter less than 0. 5%.One possible interpretation is that brands with very high current advertising expenditures are those that are already wellknown (perhaps because they have been heavily advertised over the years), so that advertising cannot further boost their awareness. Indeed, average awareness for observations with over $400 million in advertising expenditures is 74. 94 as compared to 69. 35 for the entire sample. Turning from brand awareness in Table 4 to perce ived quality in Table 5, we see that the positive effect of advertising expenditures on perceived quality found by the POLS estimator disappears once unobserved eterogeneity is accounted by the FE, DGMM, and SGMM estimators. In fact, we cannot reject the null hypothesis that advertising plays no role in determining perceived quality. Figure 3 graphically illustrates the absence of an effect of advertising expenditures on perceived quality at the margin for our DGMM estimates. While the effect of advertising expenditures on perceived quality is very imprecisely estimated, it appears to be economically insigni? cant: The implied elasticity is ? 0. 0000534 (with a standard error of 0. 00883) and a one-standarddeviation increase of advertising expenditures decrease perceived quality byEffect of advertising on brand awareness and perceived quality 225 Marginal effect –. 001 0 . 001 0 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 marginal effect of adverti sing lower 90% confidence limit . 015 upper 90% confidence limit 0 0 Density . 005 . 01 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400  ® Fig. 3 Pointwise con? dence interval for the marginal effect of advertising expenditures on perceived quality (upper panel) and histogram of advertising expenditures (lower panel). DGMM estimates 0. 000869 standard deviations (with a standard error of 0. 44). Note that the comparable effects for brand awareness are two orders of magnitude larger. Much of the remainder of this paper is concerned with demonstrating the robustness of this negative result. Before proceeding we note that whenever possible we focus on the more ef? cient SGMM estimator. Unfortunately, for perceived quality in many cases, including that in the fourth column of Table 5, the difference-in-Hansen J test rejects the null hypothesis that the extra moments in the SGMM estimator are valid. In these cases we focus on the DGMM estimator. 5. Objective and perceived quality An important component of a brand’s perceived quality is its objective quality. To the extent that objective quality remains constant, it is absorbed into the brand effects. But, even though the time frame of our sample is not very long, it is certainly possible that the objective quality of some brands has changed over the course of our sample. If so, then the lack of an effect of advertising expenditures on perceived quality may be explained if brand managers increase advertising expenditures to compensate for decreases in objective 26 C. R. Clark et al. quality. To the extent that increased advertising expenditures and decreased objective quality cancel each other out, their net effect on perceived quality may be zero. The dif? culty with testing this alternative explanation is that we do not have data on objective quality. We therefore exclude from the analysis those categories with brands that are likely to undergo changes in objective quality (applian ces, automobiles, computers, consumer electronics, fast food, footwear, pharmaceutical OTC, telecommunications, toys, and travel).The resulting estimates are reported in Table 5 under the heading â€Å"Objective quality. † We still ? nd no effect of advertising expenditures on perceived quality. 8 5. 2 Variation in perceived quality Another possible reason for the lack of an effect of advertising expenditures on perceived quality is that perceived quality may not vary much over time. This is not the case in our data. Indeed, the standard deviation of the year-to-year changes in perceived quality is 0. 2154. Even for those products whose objective quality does not change over time there are important changes in perceived quality (standard deviation 0. 130). For example, consider bottled water where we expect little change in objective quality over time, both within and across brands. Nonetheless, there is considerable variation in perceived quality. The perceived quality of Aq ua? na Water ranges across years from 6. 33 to 6. 90 and that of Poland Spring Water from 5. 91 to 6. 43, so the equivalent of over two standard deviations. Across the brands of bottled water the range is from 5. 88 to 6. 90, or the equivalent of over four standard deviations. Further evidence of variation in perceived quality is provided by the automobiles category.Here we have obtained measures of objective quality from Consumer Reports that rate vehicles based on their performance, comfort, convenience, safety, and fuel economy. We can ? nd examples of brands whose objective quality does not change at least for a number of years while their perceived quality ? uctuates considerably. For example, Chevy Silverado’s objective quality does not change between 2000 and 2002, but its perceived quality increases from 6. 08 to 6. 71 over these three years. Similarly, GMC Sierra’s objective quality does not change between 2001 and 2003, but its perceived quality decreases fro m 6. 72 to 6. 26. The ? al piece of evidence that we have to offer is the variance decomposition from Section 4 (see again Table 3 and Fig. 1). Recall that the acrossbrands standard deviation of brand awareness is about six times larger than the within-brand standard deviation. In case of perceived quality the ratio is about 4. Hence, while there is more cross-sectional than time-series variation in our sample, the time-series variation is substantial for both brand aware- 8 The marginal effects are calculated at the mean, 25th, 50th, and 75th percentile for advertising for the brands in the categories judged to be stable in terms of objective quality over time.Effect of advertising on brand awareness and perceived quality 227 ness and perceived quality. Also recall from Section 4 that perceived quality with an intertemporal correlation of 0. 95 is somewhat less persistent than brand awareness with an intertemporal correlation of 0. 98. Given that we are able to detect an effect of advertising expenditures on brand awareness, it seems unlikely that insuf? cient variation within brands can explain the lack of an effect of advertising expenditures on perceived quality; instead, our results suggest that the variation in perceived quality is unrelated to advertising expenditures.The question then becomes what besides advertising may drive these changes in perceived quality. There are numerous possibilities, including consumer learning and word-of-mouth effects. Unfortunately, given the data available to us, we cannot further explore these possibilities. 5. 3 Brand awareness and perceived quality Another concern is that consumers may confound awareness and preference. That is, consumers may simply prefer more familiar brands over less familiar ones (see Zajonc 1968). To address this issue we proxy for consumers’ familiarity by adding brand awareness to the regression for perceived quality.The resulting estimates are reported in Table 5 under the heading â₠¬Å"Brand awareness. † While there is a signi? cant positive relationship between brand awareness and perceived quality, there is still no evidence of a signi? cant positive effect of advertising expenditures on perceived quality. 5. 4 Competitive effects Advertising takes place in a competitive environment. Most of the industries being studied here are indeed oligopolies, which suggests that strategic considerations may in? uence advertising decisions.We next allow a brand’s stocks of awareness and perceived quality to be affected by the advertising of its competitors as discussed in Section 2. 9 Competitors’ advertising, in turn, can enter our estimation Eqs. 1 and 2 either relative in the share-of-voice speci? cation or absolute in the total-advertising speci? cation. We report the resulting estimates in Table 6. Somewhat surprisingly, the share-of-voice speci? cation yields an insignificant effect of own advertising. We conclude that the share-of-voice speci? cation is simply not an appropriate functional form in our application. The total-advertising speci? ation readily con? rms our main ? ndings presented above that own advertising affects brand awareness but not perceived quality. This is true even if we allow competitors’ advertising to enter quadratically in 9 For this analysis we take the subcategory rather than the category as the relevant competitive environment. Consider for instance the beer, wine, liquor category. There is no reason to expect the advertising expenditures of beer brands to affect the perceived quality or awareness of liquor brands. We drop any subcategory in any year where there is just one brand due to the lack of competitors.Table 6 Competitive effects Perceived quality 0. 845*** (0. 0217) 0. 356** (0. 145) Total advertising Brand awareness Perceived quality 228 Share of voice Brand awareness Lagged awareness/quality Relative advertising (Relative advertising)2 0. 872*** (0. 0348) 0. 236 (0. 170) ? 0. 00912 (0. 0104) 1. 068*** (0. 0406) 0. 0168 (0. 0164) ? 0. 00102 (0. 00132) Advertising Advertising2 Competitors’ advertising 0. 00892** (0. 00387) ? 0. 00000602** (0. 00000248) ? 0. 00609* (0. 00363) ?0. 0000180 (0. 000592) ? 0. 0000000303 (0. 000000535) 0. 00128** (0. 000515) Marginal effect of advertising at: Mean 5th pctl. 50th pctl. 75th pctl. 0. 00333 (0. 00239) 0. 0164 (0. 01218) 0. 00624 (0. 00448) 0. 00264 (0. 00190) Do not reject Reject* Do not reject Reject*** Do not reject 1,147 317 0. 000225 (0. 000218) 0. 00113 (0. 00110) 0. 00429 (0. 000416) 0. 000179 (0. 000173) 0. 00812** (0. 00355) 0. 00881** (0. 00382) 0. 00861** (0. 00375) 0. 00797** (0. 00349) Reject** Do not reject Do not reject Reject** Do not reject 1,147 317 ?0. 000140 (0. 000524) ? 0. 0000174 (0. 000582) ? 0. 0000164 (0. 000565) ? 0. 0000132 (0. 000510) Do not reject Do not reject Reject*** Do not reject 1,147 317 C. R. Clark et al.Advertising test: ? 1 = ? 2 = 0 Speci? cation tests: Hansen J Differ ence-in-Hansen J Arellano & Bond AR(2) Arellano & Bond AR(3) # obs # brands Do not reject Do not reject Do not reject Reject** Do not reject 1,147 317 Standard errors in parenthesis. DGMM estimates in column labeled â€Å"Total advertising/perceived quality† and SGMM estimates otherwise * p = 0. 10; ** p = 0. 05; *** p = 0. 01 Effect of advertising on brand awareness and perceived quality 229 addition to linearly. Competitors’ advertising has a signi? cant negative effect on brand awareness and a signi? cant positive effect on perceived quality.Repeating the analysis using the sum instead of the average of competitors’ advertising yields largely similar results except that the share-of-voice speci? cation yields a signi? cant negative effect of advertising on brand awareness, thereby reinforcing our conclusion that this is not an appropriate functional form. 10 Overall, the inclusion of competitors’ advertising does not seem to in? uence our results about the role of own advertising on brand awareness and perceived quality. This justi? es our focus on the simple model without competition. Moreover, it suggests that the following alternative explanation for our main ? dings presented above is unlikely. Suppose awareness depended positively on the total amount of advertising in the brand’s subcategory or category while perceived quality depended positively on the brand’s own advertising but negatively on competitors’ advertising. Then the results from the simple model without competition could be driven by an omitted variables problem: If the brand’s own advertising is highly correlated with competitors’ advertising, then we would overstate the impact of advertising on awareness and understate the impact on perceived quality.In fact, we might ? nd no impact of advertising on perceived quality at all if the brand’s own advertising and competitors’ advertising cancel each other out. 5. 5 Category-speci? c effects Perhaps the ideal data for analyzing the effect of advertising are time series of advertising expenditures, brand awareness, and perceived quality for the brands being studied. With long enough time series we could then try to identify for each brand in isolation the effect of advertising expenditures on brand awareness and perceived quality.Since such time series are unfortunately not available, we have focused so far on the aggregate effect of advertising expenditures on brand awareness and perceived quality, i. e. , we have constrained the slope parameters in Eqs. 1 and 2 that determine the effect of advertising to be the same across brands. Similarly, we have constrained the carryover parameters in Eqs. 1 and 2 that determine the effect of lagged perceived quality and brand awareness respectively to be the same across brands. As a compromise between the two extremes of brands in isolation versus all brands aggregated, we ? st examine the effect of adver tising in different categories. This adds some cross-sectional variation across the brands within a 10 We caution the reader against reading too much into these results: The number and identity of the brands within a subcategory or category varies sometimes widely from year to year in the Brandweek Superbrands surveys. Thus, the sum of competitors’ advertising is an extremely volatile measure of the competitive environment. Moreover, the number of brands varies from 3 for some subcategories to 10 for others, thus making the sum of competitors’ advertising dif? ult to compare across subcategories. 230 Table 7 Category-speci? c effects Brand awareness Marginal effect Carryover rate Appliances Automobiles Beer, wine, liquor Beverages Computers Consumer electronics Cosmetics and fragrances Credit cards Fast food Food Footwear Health and beauty Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 0. 0233 (0. 0167) 0. 00526 (0. 0154) ? 0. 0264 (0. 0423) ? 0. 0245 (0. 0554) 0. 0193** (0. 00777) 0. 0210** (0. 0

Samuel Clemens Essays - Mark Twain, Redding, Connecticut, Lecturers

Samuel Clemens Essays - Mark Twain, Redding, Connecticut, Lecturers Samuel Clemens Samuel Clemens was born and grew up in Hannibal, Missouri. This was the home of his later characters Tom Sawer and Huck Finn. In these books he incorporated such features that really existed in Hannibal; features such as Holidays Hill, Bear Creek and Lover?s Leap. Clemens described the residents of Hannibal as happy and content with the lives they led in their small town. In his late teens, Clemens left Hannibal on a riverboat to become a printer in St. Louis. He moved up in the ranks of printing and moved to New York and eventually to Washington D.C. Clemens remembered how much fun he had had on the riverboat and how glorious it must have been to be a pilot. He soon decided to move to New Orleans to become a pilot. On the boat, he often heard things like ?Mark the twain, two fathoms deep?. He liked how the words ?Mark Twain? sounded and in one of his first books, ?Life on the Mississippi? about his four years piloting the Spread Eagle along the twisting river, he decided to use the name Mark Twain. Mark Twain stopped piloting the riverboat in 1861, at the start of the Civil War, to join the Union. He went to war for two weeks and left immediately after being involved in the shooting of a civilian. He said he knew retreating better than it?s inventor did. He soon decided to travel 1,700 miles from the Missouri Territory , to the Nevada Territory. He passed through Overland City, Horseshoe City, and many large and small cities in between. Clemens commented that Salt Lake City was healthy. He said that the city had one doctor who was arrested once a week for lack of work. Virginia City was very lively from all of the gold and silver found near. He commented that the saloons, courts and prisons were busy and there was a whiskey mill every fifteen steps. Inspired by the vein of silver as wide as a New York City street under Virginia City, Twain decided to go prospecting. Many people went prospecting crazy but Twain thought it must have skipped over him. After not finding any silver, he wrote a book called Roughing It. Clemens soon went to San Francisco and took a job at the San Francisco Times. From them he got the title of ?The Most Wild Humorist of the Pacific Slope?. He wanted to travel, so he boarded a ship to Hawaii, also known as the Sandwich Islands. From there, he traveled around the South Pacific and eventually made his way to Egypt where he was surprised by the large number of American tourists. He called many of them lost tribes of America. Twain soon felt he was in a strange world that had developed so much from his small town of Hannibal. ?My heart is in my own century,? Twain said,?but I wish the twentieth well.? There were other great phrases that he said, such as: ?I was young and foolish and now I?m old and foolish.? Twain churned out quotable phrases like a cigar churns out smoke. Clemens eventually bought a house on Long Island which he named Stormfield and stayed there through his final days. Samuel Clemens was born in 1835, the night of the Haley?s Comet. He always said that he thought he would go out with the comet just as he came in with it. Well, he got his wish ; Clemens died in 1910 at age seventy five, the night of the Haley?s Comet.

The Truth of Writing

The Truth of Writing The Truth of Writing The Truth of Writing By Guest Author This is a guest post by Shelley M. DuPont. If you want to write for Daily Writing Tips check the guidelines here. Every time I write, I discover something more about myself. I dont always see it immediately; but I begin to notice a pattern developing. Recently, I wrote a feature article and realized that I overuse the word that. Grammatically, it was not wrong; it was just too much. It visually detracted from the overall appearance of the piece. Maybe no one else would have noticed, but it bothered me. Every that was like an unsightly wad of gum stuck under a desk. I couldnt wait to pry them out. The next thing I became aware of was a tendency to edit my work as I write. This should be a separate process, and I really have to fight against doing it. Its almost like a default mode that subconsciously takes over as I write. As you can see, we all struggle with the writing process. It reveals more than we realize. To strengthen the weak spots, here are some things that may be of help to you. Avoid editing as you write-it slows down the writing process Read your piece out loud-you will hear your mistakes before you will see them Have someone read it back to you you will better determine if you clearly communicated your thought Vary your sentence structure-avoid starting every sentence with a subject, turn some sentences into questions, use introductory clauses Simplify-delete unnecessary words and phrases, avoid repetition Ive always told my students that writing is like an art form. It is the true you being unveiled. It cannot be completed in one sitting. You build it, tear it down, add more, take away, and rebuild. One day you may like it, the next you may not. Remember, Rome was not built in a day. Take your time, be thorough, have someone help you, and dont be afraid to throw your words away. Those that matter will stand. You can read more from Shelley on WriteSideUp.org. Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Writing Basics category, check our popular posts, or choose a related post below:100 Words for Facial ExpressionsThe Six Spellings of "Long E"Trooper or Trouper?

Systems of Inquiry Essay Example

Systems of Inquiry Essay Example Systems of Inquiry Essay Systems of Inquiry Essay The first three of these activitiesfixing agendas, setting goals, and designing actionsare usually called problem solving; the last, evaluating and choosing, is usually called decision making. This system of inquiry should be performed effectively (Simon, et al., 1986).The basic framework to be used is the determination of the quality of our decisions and problem solutions through the abilities and skills of the human resource in the organization and the tools and machines available like computers. Maximization of the human resource and the use of tools and machine may reach remarkable levels of economic productivity. The targets for this system of inquiry is understanding how human minds, with and without the help of computers, solve problems and make decisions effectively, and improving problem-solving and decision-making capabilities. Some of the knowledge and data that will be gained through this research describes the ways in which people in the organization actually go about making decisions and solving problems, adopt better methods and offer advice for the improvement of the process (A Roundtable Discussion: Knowledge and the New Organization, 2006).Central to the body of prescriptive knowledge about decision making has been the theory of subjective expected utility (SEU), a sophisticated mathematical model of choice that lies at the foundation of most contemporary economics, theoretical statistics, and operations research. subjective expected utility theory defines the conditions of perfect utility-maximizing rationality in a world of certainty or in a world in which the probability distributions of all relevant variables can be provided by the decision makers. In spirit, it might be compared with a theory of ideal gases or of frictionless bodies sliding down inclined planes in a vacuum. subjective expected utility theory deals only with decision making; it has nothing to say about how to frame problems, set goals, or develop new alternatives (Simon, et al., 1986).Prescriptive theories of choice such as subjective expected utility are complemented by empirical research that shows how people actually make decisions (purchasing insurance, voting for political candidates, or investing in securities), and research on the processes people use to solve problems (designing switchgear or finding chemical reaction pathways). This research demonstrates that people solve problems by selective, heuristic search through large problem spaces and large data bases, using means-ends analysis as a principal technique for guiding the search. The expert systems that are now being produced by research on artificial intelligence and applied to such tasks as interpreting oil-well drilling logs or making medical diagnoses are outgrowths of these research findings on human problem solving (Buchanan and Smith, 1988).What chiefly distinguishes the empirical research on decision making and problem solving from the prescriptive approaches derived from subjective expected utility theory is the attention that the former gives to the limits on human rationality. These limits are imposed by the complexity of the world in which we live, the incompleteness and inadequacy of human knowledge, the inconsistencies of individual preference and belief, the conflicts of value among people and groups of people, and the inadequacy of the computations we can carry out, even with the aid of the most powerful computers. The real world of human decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it within the scope of human thinking powers, we must simplify our problem formulations drastically, even leaving out much or most of what is potentially relevant (Simon, et al., 1986).The descriptive theory of problem solving and decision making is centrally concerned with how people cut problems down to size: how they apply approximate, heuristic techniques to handle complexity that cannot be handled exactly. Out of this descriptive theory is emerging an augmented and amended prescriptive theory, one that takes account of the gaps and elements of unrealism in SEU theory by encompassing problem solving as well as choice and demanding only the kinds of knowledge, consistency, and computational power that are attainable in the real world (Nicholas, 1998).The growing realization that coping with comp lexity is central to human decision making strongly influences the directions of research in this domain. Operations research and artificial intelligence are forging powerful new computational tools; at the same time, a new body of mathematical theory is evolving around the topic of computational complexity. Economics, which has traditionally derived both its descriptive and prescriptive approaches from SEU theory, is now paying a great deal of attention to uncertainty and incomplete information; to so-called agency theory, which takes account of the institutional framework within which decisions are made; and to game theory, which seeks to deal with interindividual and intergroup processes in which there is partial conflict of interest. Economists and political scientists are also increasingly buttressing the empirical foundations of their field by studying individual choice behavior directly and by studying behavior in experimentally constructed markets and simulated political str uctures (Simon, et al., 1986).This system will be adopted since in this system all the alternatives among which choice could be made will be known, and that the consequences of choosing each alternative could be ascertained. It is assumed that a subjective or objective probability distribution of consequences was associated with each alternative. It will make use of the subjective expected utility theory. By admitting subjectively assigned probabilities, subjective expected utility theory opened the way to fusing subjective opinions with objective data, an approach that can also be used in man-machine decision-making systems. In the probabilistic version of the theory, Bayess rule prescribes how people should take account of new information and how they should respond to incomplete information.Through this sytem, strong inferences can be made. Although the assumptions cannot be satisfied even remotely for most complex situations in the real world, they may be satisfied approximately in some microcosmsproblem situations that can be isolated from the worlds complexity and dealt with independently. For example, the manager of a commercial cattle-feeding operation might isolate the problem of finding the least expensive mix of feeds available in the market that would meet all the nutritional requirements of his cattle. The computational tool of linear programming, which is a powerful method for maximizing goal achievement or minimizing costs while satisfying all kinds of side conditions (in this case, the nutritional requirements), can provide the manager with an optimal feed mixoptimal within the limits of approximation of his model to real world conditions. Linear programming and related operations research techniques can be used to make decisions whenever a situation that reasonably fits their assumptions can be carved out of its complex surround. These techniques have been especially valuable aids to middle management in dealing with relatively well-structured decision problems (Simon, et al., 1986).Other tools of modern operations research that can be used adide from linear programming, are integer programming, queuing theory, decision trees, and other widely used techniques. They assume that what is desired is to maximize the achievement of some goal, under specified constraints and assuming that all alternatives and consequences or their probability distributions are known. These tools have proven their usefulness in a wide variety of applications (Simon, et al., 1986).Decision-making and human problem solving is usually studied in laboratory settings, using problems that can be solved in relatively short periods of time seldom more than an hour, and often seeking a maximum density of data about the solution process by asking subjects to think aloud while they work. The thinking-aloud technique can be used dependably to obtain data about subjects behaviors in a wide range of settings. The laboratory study of decision-making and proble m solving has been supplemented by field studies of professionals solving real-world problems. Currently, historical records, including laboratory notebooks of scientists, are also being used to study decision-making and problem-solving processes in scientific discovery (Simon, et al., 1986).These systems can be used by the students or management people in the company. They may question respondents about specific situations, rather than asking for generalizations. They ones conducting this system should be sensitive to the dependence of answers on the exact forms of the questions. They should be aware that behavior in an experimental situation may be different from behavior in real life. They may also attempt to provide experimental settings and motivations that are as realistic as possible. Using thinking-aloud protocols and other approaches, they can try to track the choice behavior step by step, instead of relying just on information about outcomes or querying respondents retrosp ectively about their choice processes (Hofer, 2004).The code will be implemented through finding the underlying bases of human choice behavior. Although not always easy, try to provide veridical accounts of how decision-makers make up their minds, especially when there is uncertainty. In many cases, predict how they will behave but the reasons people give for their choices can often be shown to be rationalizations and not closely related to their real motives (Simon, et al., 1986).Possible reaction that will be generated from the code from employees is that the employees may find that present and prospective computers are not even powerful enough to provide exact solutions for the problems of optimal scheduling and routing of jobs through a typical factory that manufactures a variety of products using many different tools and machines. And the mere thought of using these computational techniques to determine an optimal national policy for energy production or an optimal economic pol icy reveals their limits (Currently skimming chapter: Report of the Research Briefing Panel on Decision Making and Problem Solving, 1986).This system may also make enormous demands on information. For the utility function, the range of available alternatives and the consequences following from each alternative must all be known. The employees may find this system as not fitting real-world problems aside from the informational and computational limits of people and computers and the inconsistencies in their values and perceptions (Simon, et al., 1986).The effect that the code would have on the organization is that the code would provide explanations for the many forms of decisions that has to be made in the business. Incompleteness and asymmetry of information have been shown to be essential for explaining how individuals and business firms decide when to face uncertainty by insuring, when by hedging, and when by assuming the risk. It assumes that economic agents seek to maximize uti lity, but within limits posed by the incompleteness and uncertainty of the information available to them (Currently skimming chapter: Report of the Research Briefing Panel on Decision Making and Problem Solving, 1986).Decision-making and problem-solving relies on large amounts of information that are stored in memory and that are retrievable whenever the maker / solver recognizes cues signaling its relevance. Thus, the expert knowledge of a diagnostician is evoked by the symptoms presented by the patient; this knowledge leads to the recollection of what additional information is needed to discriminate among alternative diseases and, finally, to the diagnosis. In a few cases, it has been possible to estimate how many patterns an expert must be able to recognize in order to gain access to the relevant knowledge stored in memory. In applying knowledge of decision making and problem solving to society-wide, or even organization-wide, phenomena, the problem of aggregation must be solved. Methodologies must be found to extrapolate from theories of individual decision processes to the net effects as a whole. Because of the wide variety of ways in which any given decision task can be approached, it is unrealistic to postulate a representative firm or an economic man, and to simply lump together the behaviors of large numbers of supposedly identical individuals. Solving the aggregation problem becomes more important (Simon, et al., 1986).Organizations sometimes display sophisticated capabilities far beyond the understanding of single individuals. They sometimes make enormous blunders or find themselves incapable of acting. Organizational performance is highly sensitive to the quality of the routines or performance programs that govern behavior and to the adaptability of these routines in the face of a changing environment. In particular, the peripheral vision of a complex organization is limited, so that responses to novelty in the environment may be made in inappropri ate and quasi-automatic ways that cause major failure (Simon, et al., 1986).

Hewlett Packard (HP) - Introduction to Business Organization Coursework

Hewlett Packard (HP) - Introduction to Business Organization - Coursework Example It deals in manufacture and supply of laptops, printers, PC’s and variety of range of computers. HP operates a large network for the manufacture and supply of technological products; it operates in 170 countries of the world fulfilling the technological needs of millions. (HEWLETT PACKARD. 2012) HP was founded in 1939 by two of the classmates of Stanford University- Bill Hewlett and Dave Packard. It’s first ever product was an electronic test instrument and one of its early customers who boosted their sales initially was Walt Disney. (HEWLETT PACKARD. 2012) HP operates in 170 countries of the world which makes it operative in America, Asia Pacific, Europe, Middle East and Africa (EMEA). Its head office is located in Palo Alto, California –USA. Further it has installed HP Solution Centers in over 80 locations all around the world which provide technical support regarding the products to million of customers. (HEWLETT PACKARD. 2012) HP operates as a public listed c orporation and is listed on New York Stock Exchange. Financially, HP is a very sound company with an annual turnover of $127,245 million, generating a profit of $7,074 million. HP has a diversified portfolio of products and operates many different segments. Region wise it mostly generates its sales- 45% of it from America (which includes US, Canada and Latin America) the rest is generated from EMEA and Asia Pacific. Its most revenue generating segment is that of personal computers products and services which generates around 60% of the revenue. Other segments which are operated are imaging and printing group, enterprise server, storage and networking, HP software and HP financial services. The company is currently headed by Meg Whitman, the CEO and President of HP who was appointed on the posts recently in late 2011. (HEWLETT PACKARD. 2012) Organizational Structure: An organization’s structure plays a great role in executing its strategies. HP’s organizational structur e has grasped international attention from past decade. In 2000 Carly Fiorina was appointed as the Chief Executive Officer at HP, at that point Fiorina changed the organizational structure of HP which had been there for last 64 years. She dismantled the decentralized structure of HP and introduced a more modern concept of structuring the organization in which HP was to operate with a front-back approach in which back-end unit was to deal with manufacturing while the front-end dealt with sales, marketing and customers. That was the first time a large company with numerous production lines adopted this structure which requires high level of coordination. (PEARCE & ROBINSON. 2000) However this structural strategy was a fail and in 2005Mark Hurd was appointed as the CEO and he changed the structure back to it what it was before Fiorina, that is, a decentralized structure with independently run smaller units with a narrow product focus. The current organizational structure at HP allows f or greater accountability, high sense of responsibility, aids in cost reduction and accountability of spending and better control on production to sale activities. The decentralized structure at HP is basically made up of seven divisions/segments which are either organized on the basis of products or functions. Such a structure enables HP to have greater insight about the environment in which it operates as a technological business is highly dynamic, the divisions and units have to be adaptable and agile to the changes which is done by resting the power to plan day-to-day activities to the segments. However major strategic decisions are held with the higher level. Such a structure also supports a giant like HP which serves a market of trillions all around the world to execute more effectively. HP has