Exigir Conjugation in Spanish, Translation, and Examples

The Spanish verb exigir means to demand or to require. It is a regular -ir verb, so it is conjugated like the verbs vivir and subir. In this article you can find exigir conjugations in the present, past and future indicative, the present and past subjunctive, the imperative, and other verb forms. When conjugating exigir, you must be careful with a spelling change that occurs whenever the g would be followed by an o or an a. The g in exigir makes a soft sound (like the English h sound), but in front of the vowels o, a or u it makes a hard g sound (like the English g in gate). Therefore, to maintain the soft g sound, the letter g changes to a j. For example, conjugations like exijo and exija use the letter j instead of g. Exigir Present Indicative In the present indicative tense, the spelling change g to j occurs only in the first person singular conjugation (yo). Yo exijo I demand Yo exijo respeto. Tà º exiges You demand Tà º exiges mucho de tus hijos. Usted/à ©l/ella exige You/he/she demands Ella exige muchas cosas de su novio. Nosotros exigimos We demand Nosotros exigimos libertad de expresià ³n. Vosotros exigà ­s You demand Vosotros exigà ­s muchas horas de trabajo. Ustedes/ellos/ellas exigen You/they demand Ellos exigen la renuncia del presidente. Exigir Preterite Indicative In the preterite tense there is no spelling change. The preterit is one of two past tenses in Spanish, and it is used to talk about completed actions in the past. Yo exigà ­ I demanded Yo exigà ­ respeto. Tà º exigiste You demanded Tà º exigiste mucho de tus hijos. Usted/à ©l/ella exigià ³ You/he/she demanded Ella exigià ³ muchas cosas de su novio. Nosotros exigimos We demanded Nosotros exigimos libertad de expresià ³n. Vosotros exigisteis You demanded Vosotros exigisteis muchas horas de trabajo. Ustedes/ellos/ellas exigieron You/they demanded Ellos exigieron la renuncia del presidente. Exigir Imperfect Indicative The imperfect tense is used to talk about habitual or ongoing actions in the past. It is conjugated using the stem exig- plus the imperfect ending for -er verbs (à ­a, à ­as, à ­a, à ­amos, à ­ais, à ­an). The imperfect can be translated as was demanding or used to demand. Yo exigà ­a I used to demand Yo exigà ­a respeto. Tà º exigà ­as You used to demand Tà º exigà ­as mucho de tus hijos. Usted/à ©l/ella exigà ­a You/he/she used to demand Ella exigà ­a muchas cosas de su novio. Nosotros exigà ­amos We used to demand Nosotros exigà ­amos libertad de expresià ³n. Vosotros exigà ­ais You used to demand Vosotros exigà ­ais muchas horas de trabajo. Ustedes/ellos/ellas exigà ­an You/they used to demand Ellos exigà ­an la renuncia del presidente. Exigir Future Indicative The future tense is conjugated by using the infinitive form exigir, and adding the future tense endings (à ©, à ¡s, à ¡, emos, à ©is, à ¡n). Yo exigirà © I will demand Yo exigirà ©respeto. Tà º exigirà ¡s You will demand Tà º exigirà ¡s mucho de tus hijos. Usted/à ©l/ella exigirà ¡ You/he/she will demand Ella exigirà ¡ muchas cosas de su novio. Nosotros exigiremos We will demand Nosotros exigiremoslibertad de expresià ³n. Vosotros exigirà ©is You will demand Vosotros exigirà ©is muchas horas de trabajo. Ustedes/ellos/ellas exigirà ¡n You/they will demand Ellos exigirà ¡n la renuncia del presidente. Exigir PeriphrasticFuture Indicative To form the periphrastic future you need the present indicative conjugation of the verb ir (to go), the preposition a, and the infinitive exigir. Yo voy a exigir I am going to demand Yo voya exigir respeto. Tà º vasa exigir You aregoing todemand Tà º vasa exigir mucho de tus hijos. Usted/à ©l/ella vaa exigir You/he/she isgoing todemand Ella vaa exigir muchas cosas de su novio. Nosotros vamosa exigir We aregoing todemand Nosotros vamosa exigir libertad de expresià ³n. Vosotros vaisa exigir You aregoing todemand Vosotros vaisa exigir muchas horas de trabajo. Ustedes/ellos/ellas vana exigir You/they aregoing todemand Ellos vana exigir la renuncia del presidente. Exigir Present Progressive/Gerund Form The gerund or present participle in Spanish is formed with the ending -ando (for -ar verbs) or -iendo (for -er and -ir verbs). Present Progressive of Exigir està ¡ exigiendo Is demanding Ella està ¡ exigiendo muchas cosas de su novio. Exigir Past Participle The past participle for regular ir verbs is formed with the ending -ido. It can be used as an adjective or to form compound tenses such as the present perfect. Present Perfect of Exigir ha exigido Has demanded Ella ha exigido muchas cosas de su novio. Exigir Conditional Indicative The conditional tense is used to talk about possibilities. In English it is usually expressed as would verb. Yo exigirà ­a I would demand Yo exigirà ­arespeto. Tà º exigirà ­as You would demand Tà º exigirà ­as mucho de tus hijos. Usted/à ©l/ella exigirà ­a You/he/she would demand Ella exigirà ­a muchas cosas de su novio. Nosotros exigirà ­amos We would demand Nosotros exigirà ­amoslibertad de expresià ³n. Vosotros exigirà ­ais You would demand Vosotros exigirà ­ais muchas horas de trabajo. Ustedes/ellos/ellas exigirà ­an You/they would demand Ellos exigirà ­an la renuncia del presidente. Exigir Present Subjunctive In the present subjunctive, the spelling change g to j occurs in all of the conjugations, since the endings of this verb tense contain the vowel a. Que yo exija That I demand Mamà ¡ espera que yo exija respeto. Que tà º exijas That you demand El abuelo quiere que tà º exijas mucho de tus hijos. Que usted/à ©l/ella exija That you/he/she demand La amiga sugiere que ella exija muchas cosas de su novio. Que nosotros exijamos That we demand El periodista pide que nosotros exijamos libertad de expresià ³n. Que vosotros exijà ¡is That you demand El jefe sugiere que vosotros exijà ¡is muchas horas de trabajo. Que ustedes/ellos/ellas exijan That you/they demand La gente espera que ellos exijan la renuncia del presidente. Exigir Imperfect Subjunctive To conjugate the imperfect subjunctive you need to start with the third person plural conjugation (ellos, ellas, ustedes), in the preterite tense (exigieron) remove the on, and then add the appropriate ending (a, as, a, amos, ais, an). There are two options for conjugating the imperfect subjunctive. Option 1 Que yo exigiera That I demanded Mamà ¡ esperaba que yo exigiera respeto. Que tà º exigieras That you demanded El abuelo querà ­a que tà º exigieras mucho de tus hijos. Que usted/à ©l/ella exigiera That you/he/she demanded La amiga sugerà ­a que ella exigiera muchas cosas de su novio. Que nosotros exigià ©ramos That we demanded El periodista pedà ­a que nosotros exigià ©ramos libertad de expresià ³n. Que vosotros exigierais That you demanded El jefe sugerà ­a que vosotros exigierais muchas horas de trabajo. Que ustedes/ellos/ellas exigieran That you/they demanded La gente esperaba que ellos exigieran la renuncia del presidente. Option 2 Que yo exigiese That I demanded Mamà ¡ esperaba que yo exigiese respeto. Que tà º exigieses That you demanded El abuelo querà ­a que tà º exigieses mucho de tus hijos. Que usted/à ©l/ella exigiese That you/he/she demanded La amiga sugerà ­a que ella exigiese muchas cosas de su novio. Que nosotros exigià ©semos That we demanded El periodista pedà ­a que nosotros exigià ©semos libertad de expresià ³n. Que vosotros exigieseis That you demanded El jefe sugerà ­a que vosotros exigieseis muchas horas de trabajo. Que ustedes/ellos/ellas exigiesen That you/they demanded La gente esperaba que ellos exigiesen la renuncia del presidente. Exigir Imperative The imperative mood is used to give direct orders or commands. In the tables below you can see both the positive and negative commands. Several of these conjugations have the spelling change g to j. Positive Commands Tà º exige Demand!  ¡Exige mucho de tus hijos! Usted exija Demand!  ¡Exija muchas cosas de su novio! Nosotros exijamos Let's demand!  ¡Exijamos libertad de expresià ³n! Vosotros exigid Demand!  ¡Exigid muchas horas de trabajo! Ustedes exijan Demand!  ¡Exijan la renuncia del presidente! Negative Commands Tà º no exijas Don't demand!  ¡No exijas mucho de tus hijos! Usted no exija Don't demand!  ¡No exija muchas cosas de su novio! Nosotros no exijamos Let's not demand!  ¡No exijamos libertad de expresià ³n! Vosotros no exijà ¡is Don't demand!  ¡No exijà ¡is muchas horas de trabajo! Ustedes no exijan Don't demand!  ¡No exijan la renuncia del presidente!

Teaching Abstinence and Abortion in Junior High Sex Education

Teaching Abstinence and Abortion in Junior High Sex Education 1. The two most important topics for a junior high sex education curriculum I think would be abstinence and abortion. Teens these days are struggling in a world that tells us sex is necessary for people who are dating. As a result, many teens give in to their desires and the pressures and engage in sexual relationships. This occurs from early to late adolescence and beyond. Supporting teens choices, schools teach safe sex. In my school a group was brought in to demonstrate for the entire school how to put on a condom, using a microphone. This turned into a joke, no one taking it seriously. Even so, it is a horrible example. We have to teach the kids abstinence†¦show more content†¦In the curriculum I would inform students about centers that counsel pregnant women and men on how to deal with the birth of the child. Adoption is a good option, as well as keeping the child, considering living standards are comfortable. However, the best way to prevent abortion is to ab stain from sex in a relationship where a baby is not desired; largely, marriage. 2. The basic organizer of sex-role attitudes a person meets in adolescence is the self-categorization as a boy or a girl. The child who recognizes that he is a male begins to value maleness and to act consistently with gender expectations. He begins to structure his own experiences according to his accepted gender and to act out appropriate sex roles. He reflects sex-role differences and he fantasizes himself as a daddy with a wife and children. The same holds true for the girl, who pretends she is a grown-up woman with a husband and children. Sex differentiation takes place gradually as children learn to be male and female according to culturally established sex-role expectations and their interpretations of them. 3. As they become oriented to the adult world, adolescents powers of reflective thinking enable them to evaluate what they learn. They become more capable of moral reasoning. Furthermore, their ability to differentiate the possible from the real enables them to distinguish not only what the adult world is but also what it mightShow MoreRelatedIs Abortion Really A Problem?1444 Words   |  6 Pagespregnancies end in abortion. This means that every four in ten women who find out they are pregnant unintentionally decide to not continue with the pregnancy. This equates to four out of every ten unplanned child being killed and not having a chance at a precious life(Abortions). Although many abortions occur each day, not everyone believes that abortion is really a problem. Truthfully, it is just like murder. Just as the country needs to limit the crime rate, the rate at which abortions take place needRead MoreThe Effects Of Sexual Education On Public Schools908 Words   |  4 Pages How Sexual Education In Public Schools Benefit By: Michelle Schlichting English Composition I November 2015 Sexual education in public schools has many benefits. This is of course if every public school would be willing to provide their students with the opportunity to be able to take this class. Therefore I believe that some sort of sexual education class should be provided to all high school students in public schools. Sexual Education should be taught in high schools for quite a numberRead MoreTeen Pregnancy and Sex Education Essay1794 Words   |  8 Pageswoman and her family, but it also has a consequential impact on society. Reducing the number of adolescent pregnancies would promote child well-being and decrease child poverty statistics (â€Å"National Data†). Increasing the number of comprehensive sex education courses would help young people learn how to better protect themselves against premature pregnancy and STDs, and in turn curtail the number of teenage pregnancies. The Facts Among industrialized countries, the United States has the highest ratesRead More Teaching Morality More Important than Sex Education in Public Schools2259 Words   |  10 Pagesabout the nature of sex education in the nations high schools.   Studies show that 81 percent of American adults support a joint program teaching abstinence and contraception as opposed to an abstinence-only program (Roper 0316946), and 79 percent support contraception education regardless of the level of sexual activity in teenagers (Roper 0340807). The sad fact is, contraception is societys attempt at a quick fix for a problem that runs far deeper than the issue of teen sex.   In the debate overRead MoreEssay on Sexual Education in Public Schools1915 Words   |  8 PagesSex education has been an ongoing debate for decades. In the early 1970’s, twenty states voted restricting sex education from the school curriculum, leaving the District of Columbia and only three states (Maryland, Kentucky, N ew Jersey), requiring schools to teach sex education. By the mid 1980’s, a deadly disease permitted through sexual intercourse was recognized; the fear of catching a disease sex education quickly became accepted. In 1986, Surgeon General C. Everett Koop felt sex education shouldRead More Distribution of Condoms in Public High Schools Essay2464 Words   |  10 PagesDistribution of Condoms in Public High Schools Coinciding with the onslaught of the new millennium, schools are beginning to realize that the parents are not doing their job when it comes to sexual education. The school system already has classes on sexual education; these classes are based mainly on human anatomy. Most schools do not teach their students about relationships, morals, respect, self-discipline, self-respect, and most importantly contraceptives. Everyday students engageRead MoreUnsafe Sex Practice3833 Words   |  16 PagesUnsafe sex or unprotected sex describes sexual contact of any form that takes place in the absence of a condom normally used in preventing the risk of sexually transmitted infections and HIV (Chambers, 2010). The World Health Organisation (2004) considers all the consequences that can arise from unsafe sex practice and came up with a broader definition stating that any sexual contact that can lead to unwanted pregnancy, abortion, infertility, unstable mental conditions and cancer arising from certainRead MoreCxc Mutipe Choice Questions13016 Words   |  53 PagesCooperation d) The coordination of foreign policy among the independent countries. The supreme policy-making body of Caricom is: a) The Heads of Government Conference b) The foreign affairs ministers c) The Standing Committee of Ministers of Education d) The Common Market of Ministers. One of these is not a common service of Caricom: a) British West Indian Airways b) University of the West Indies c) The West Indies Shipping Service d) Caribbean Examination Council The Caribbean Community isRead More_x000C_Introduction to Statistics and Data Analysis355457 Words   |  1422 PagesIntroduction to Statistics and Data Analysis This page intentionally left blank Introduction to Statistics and Data Analysis Third Edition Roxy Peck California Polytechnic State University, San Luis Obispo Chris Olsen George Washington High School, Cedar Rapids, IA Jay Devore California Polytechnic State University, San Luis Obispo Australia †¢ Brazil †¢ Canada †¢ Mexico †¢ Singapore †¢ Spain †¢ United Kingdom †¢ United States Introduction to Statistics and Data Analysis, Third Edition

Advertising Impact Free Essays

string(151) " both advertising expenditures and the stocks of perceived quality and awareness may lead to spurious positive estimates of the effect of advertising\." 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. We will write a custom essay sample on Advertising Impact or any similar topic only for you Order Now 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. clark@hec. ca U. Doraszelski Department of Economics, Harvard Universit y, 1805 Cambridge Street, Cambridge, MA 02138, USA e-mail: doraszelski@harvard. edu ) M. Draganska (B Graduate School of Business, Stanford University, Stanford, CA 94305-5015, USA e-mail: draganska_michaela@gsb. 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 investing 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 across 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 either 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 model, 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. You read "Advertising Impact" in category "Essay examples" 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 brands 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, i f 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 instru ments. This is an appealing methodology that has been widely applied (e. g. , Acemoglu and Robinson 2001; Durlauf et al. 2005; Zhang and Li 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 consumers’ 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 products 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 th e 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 course 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 over 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 the 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. Similarly, 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 competitors’ 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 effect† 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 f or 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 period 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, thi s 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 advertising 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 terms. 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 accurately 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 about 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 Web We 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 with 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 i n 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 Consumer 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 deviation 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 Overall 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. 059 159. 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 â €“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-meaned 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. 000007 45) (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 expenditures 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 t he 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 -within 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. 223 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 advertising expenditure s 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 instrumenting 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 lower 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 perceived 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 by Effect 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 advertising 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 f ourth 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 al ternative 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 (appliances, 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 Aqua? 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 from 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-section al 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, win e, 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 Difference-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 cou ld 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 advertising 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 i s 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 How to cite Advertising Impact, Essay examples

Criminal Technology free essay sample

Running Head: CRIMINAL TECHNOLOGY Criminal Technology from the Past into the Future CJ216: Computers, Technology and Criminal Justice Information Systems Professor Lally July 19, 2011 In the past, technology was not very advanced; there were not very much communication devices. But the police managed to respond the fastest way to emergency calls. As time has passed, technology advanced, so all the technology that we have now 20 years ago people did not know of its existence. That is why we are going to analyze the changes that technology has had through the years and in what way has helped the police. Also we are going to see what positive changes technology will provide us in the future. One of the advances in technology that the police have is the in-car camera system. This system has been very important to evaluate the performance of officers and their professionalism. The ability of this system to record video footage from the patrol has been very helpful in traffic stops, arrests, criminal investigations, training and internal affairs. Since the in-car cameras were installed the officers could detect drunken people or even other criminals and have also helped to exonerate officers from false accusations. If we compare the technology from the past with the technology we have in the present we can see there is a huge difference. In the past, officers only depended on their radio and had to pass all the information they got in a case and then have to wait for the dispatchers reply to know if they could continue with the arrest or not. Now days, they still use the radio, but they also have another support system that is the laptop they have in their patrol, that way the officer can access more easily and quickly to the police database. Technology provides to the police many other things that are useful in their line of work like: * Photo Enforcement Systems * Thermal Imaging * Graffiti Cameras * Electronic White Boards * Lasers * Radios * Cameras for K-9 Units * Automatic License Plate Recognition * Global Positioning System (Shultz 2008) There are also two very important database systems that law enforcement agencies use. The NCIC (National Crime Information Center); this system has a very large information of offender’s fingerprints and has led way to the system that today is known as AFIS of Automated Fingerprint Identification System. Foster, 2004) The AFIS provide all the law enforcement agencies around the United States a huge amount of criminal information just by run a simple fingerprint. In 1999 the FBI developed another database that is called IAFIS (Integrated Automated Fingerprint Identification System). This system not only include fingerprints, it includes mug shots, scars, criminal histories a nd tattoo photos; also include physical characteristics like hair color, eye color, height, weight and aliases. The system also has civil fingerprints from individuals who work or worked for the government. We have discussed the technology we had in the past and the technology we have now and compared both. But what does technology has prepared for us in the future? There are several prototypes of new technology that the police can use to help them; there is a system that is called RCIS (Remote Control Information System) â€Å"Is a highly compact communication system that provides video feed, two-way communication and vital signs monitoring and has a GPS feature† (Foster, 2004). The Stolen Item Database is another system that can be use by the law enforcement agencies. This will work like a scanner and if a store is robbed the officer could scan the item that the suspect that was arrested has and that way the officer can see if the merchandise is stolen or not. For future technology the Biometrics science which is a science that helps law enforcement to determine who is the correct criminal. Biometrics will lead the Criminal Justice System into the future. There are different items that have to collect and analyze using biometrics which are: Deoxybunucleic Acid (DNA) from blood samples, facial recognition, iris and retina recognition, fingerprints, palm and handprints. One of the most important examples of biometrics is DNA. The DNA is very helpful in the Criminal Justice System because some times thanks for a blood example that was found in a crime scene, it could be determine who was the person who commit the crime or who was that victim. Forensic scientist can use DNA in semen, hair, saliva, blood or skin that was found in a crime scene. This process is called DNA profiling. â€Å"In DNA profiling of variable sections of repetitive DNA, such as short tandem repeats and minisatellites, are compared between people†. Collins, 1994) This technique is very reliable when identifying matching DNA. But when the scene is contaminated the identification can be difficult. (Balding 2005) There is also technology that is developing for police tactical communications like: TEA’s LASH headset that was designed especially for Los Angeles police special weapons and tactics (SWAT) division. The TEA is a strap that goes around the neck and inside the strap is a nozzle module near the voice box, so you can talk even if there is a lot of noise and the person who has the other set can hear you but the other people can not. We can say that over the past 70 years technology has taken a huge step forward, from the radio communication to the cameras that are above the traffic lights, panic buttons and even advance technology that the new patrols has, like video cameras, computer and more. There was also advance in the security systems, in the past every worker had to check the time at they got to their work and the time they left in paper sheets, but now they use electronic cards or even chips. For the military there are several new equipment that can be use by sending them to investigate without jeopardize the life of one of the soldiers. So, there has been a breakthrough in technology that has helped us in every way. What progress can we expect for the next 70 years? REFERENCES Foster, R. E. (2004) Police Technology, New York, NY Prentice Hall Moriarty, L. J. (2005) Criminal Justice Technology in the 21st Century, Springfield, Illinois, Charles C. Thomas Publisher, LTD Schultz, P. D. (2008) The Future is here: Technology in police departments. From the police chief, Vol. LXXV, no. 6 Balding, D. J. (2005) Weight of evidence for forensic DNA profiles. London, UK John Wiley son LTD. Collins A. (1994) Likelihood ratios for DNA identification Great Britain

American Literature CLEP Flashcard

American Literature CLEP

A quick simple guide to becoming healthier and happier at work

A quick simple guide to becoming healthier and happier at work We spend almost 8 hours at work every day  and about 6 of those hours we are sitting at a desk with little to no activity. Sitting at a desk for long periods of time is not healthy for our bodies and it is not recommended. We need to get our blood flowing and while sitting in a bad posture and eating unhealthy foods does not help; we do have healthy alternatives. With little changes such as standing desks, 5 minutes of activity, and swapping sugary drinks for more water, we can help our bodies stay healthy. Being happy and healthy at work increases productivity and your overall mood at work. Healthy employees are happy employees! Here are some great tips for becoming healthier and happier at work.Source [ Ultimate Mats ]

Strategic Leadership on Alliance or Vertical Integration Case Study on Essay

Strategic Leadership on Alliance or Vertical Integration Case Study on Cisco Systems - Essay Example It also wanted to use the expertise of employees of acquired companies. Cisco's employee friendly policies was instrumental to its success. This acquisition policy has played a key role in the swift development of hardware components used in the Internet field. As all the acquired companies had their own infrastructure and clientele base, it helped Cisco in development and expansion. Although it maintained its leadership role in the market, yet this did not deter it from entering into partnerships with other manufacturing and software designing companies. Morgridge's philosophy proved very successful. The strategy of integration with competitors and other associated companies have made Cisco a world leader in the field of Internet and IT. 2. Hi Writer, I think this framework will help you better. Forget about the write up. Please help to identify all the factors and analyze why it is important from the perspective of customer, employer and writer. I am very sorry, but when reading your paragraph, I am not clear even after reading a few times how to pluck the factors into the following table. This is exactly what the question is asking for. Thanks. 3) Many factors favor the purchase of INS. INS is a leading network consulting company with about 2000 employees. Most of them are senior professionals in their field of expertise. It is a rapidly growing company with very good market share.

Final Exam Essay Example | Topics and Well Written Essays - 750 words - 11

Final Exam - Essay Example Pain is one such emotion that will serve as a permanent reminder for the culprit thereby compelling an intuitive behavior change. In their own unique explanations, the two authors explain the need for reforming a behavior before reintroducing an individual into the society. According to the two, effective punishments further serve examples to the rest of the population in the society thus deterring any similar undesired behaviors. In chapter 20, Joshua Green and Jonathan Cohen explain the relationship between punishments and neuroscience. Criminal offenders require effective mental evaluation before recommending appropriate punishments. Through effective psychiatric evaluation, the jurists and correctional facilities will understand the unique behavior patterns of every individual criminal thus designing an appropriate corrective measure. The two contend that the primary objective of punishment is to reform behavior. This requires the concerted effort of effective punishments coupled with appropriate psychological counselling in order to develop a coherent individual who will appreciate the societal values. Neuroscience thus helps devise effective punishments that will not only make the offenders acknowledge their mistakes but also reform their behaviors in case of their reintroduction into the society â€Å"effective punishments result in permanent reformation of behaviors† (Tony 243) In explaining the role of punishment, Lode Walgrave appreciates reintroduction of the criminals into the society. He therefore vouches for restorative justice, which he explains will provide justice to the aggrieved parties by punishing the culpable individuals but also reforms the individuals thereby creating a cohesive society in which people take responsibility of their actions. Restorative justice is thus a holistic approach to punishment since it infuses reformation and healing into punishment. The key

Management Communication Essay Example | Topics and Well Written Essays - 1750 words

Management Communication - Essay Example Good managers must also act as good leaders. The leadership role that is played by the manager is one of the most important functions that have to be performed by the management. In playing the role of a leader, the manager is involved in various activities that relate to lead the organization to function in a particular direction. The management works to provide direction for the organization on various issues. Great managers are also great leaders. Therefore they play the role of leading others who are under their influence. There have been raging debates on the issue of whether leaders are born or they are made. But one of the most important thing to realize here is that the role of leadership is based on some important skills that an individual posses and some which an individual learns in the process. This is because to be a good leader one does not require one attribute but is made up of specific array of attributes. A good leader must have the confidence to stand in front of other and provide them with direction. A good leader must be able to think in the sense that they must be able to gather, sort and structures information before passing it on to others. They must be able to develop a vision for the organization. ... However the most important aspect of any leader is that they must be able to communication effectively with others. This has been considered as the watershed capacity in leadership. This is because the leader plays the role of informing others, convincing others, uniting others, motivating others and directing others. These things require the leader to have effective communication skill in order to show others where the organization is heading. The effectiveness of a leader lies in their power to inform and persuade others which helps them to win battles for the hearts and the minds of the employees. (Baldoni, 2007) Good leaders are effecting because they have the power to convince others. They use a variety of strategies in order to convince others to follow them. Good managers ensure that they are good listeners and they other time to express themselves. They also ensure that they don't rush to make judgments. They will also ensure that there is an effective feedback mechanism in the organization. For example a good leader will ensure that they talk directly with their workers instead of using mediators. In this way they are able to learn the mood and response of the workers. Strategic organization communication Communication in an organization is very strategic in the sense that is one of the strategic factors that determine the viability of the performance of the organization. It is one of the components of organization strategy and it helps an organization to function even in difficult situations. It is strategic in the sense that it requires to be planned in advance as a part of the overall growth strategy of the organization. It is also strategic in the sense that it will have to be changed on the process of

Planning for Material Deliveries in Construction

Planning for Material Deliveries in Construction Construction  projects are becoming progressively larger and more complex in terms of physical size and cost, hence the risks and potential for losses require better control. Project management has evolved mainly because of the need to control costs and schedule (Chen  and Griffis at el, 2012). In the latest construction world a proper project management should give an overall success to the specific project within the constraints of cost, time, schedule, quality and the safety measurements. Project management plays a major role not only in the architectural and engineering industry but also the development of infrastructure of each and every country. (Edum-Fotwe and McCaffer, 2000). According to Risku and Karkkainen (2006) material delivery is one of the major parts of project management because materials are consuming huge amount of the construction cost. According to Asad (2005) Poor materials management can result in increased costs during construction. Efficient management of materials can result in substantial savings in project costs. Therefore Rivera (2004) stated Materials are major part of the construction project and the special concern should be provided from the planning stage of the project to end of the project. In the construction projects; amount of required materials cannot be reduced because it will affect the quality of the project. Meanwhile uncertainty is there in material supply due to the price fluctuations and availability of the certain materials. Therefore Sun, Liu and Lan (2011) suggested the material procurement planning (MPP) which is deals with the problem that purchasing the right quantity of material from the right supplier at the right time, a purchaser can reduce the cost for materials via a reasonable MPP model. Here the purchasing of material at the right time is one of the key elements of MPP. Risku and Karkkainen (2006) stated that the latest project management systems for construction projects facing new set of challenges in the delivery process of construction material. Mainly two requirements are expected for proper material delivery process. Those are transparency to material availability, and short response time in the material supply chain. Now a days the major challenge in the construction industry is delay in material delivery which is cause to the late completion of the project (Assaf and Al-Hejji 2005). A proper plan in material delivery and inventory management should be scheduled in the initial stage of the project plan and it can be lead to reduce the delay in material delivery in construction projects (Construction best management, 2008). Therefore this study will carry out on a delay in delivery of materials in BOI approved construction project. According to this study examine the delivery of material in projects under planning function and management. 2.2. Important of purchasing appropriate materials According to the definition provided by McConville (as cited in Hadikusumo et al., 2005, pp  48), purchasing is a fundamental function of material procurement that refers to the acquisition of goods and services and an establishment of mutually acceptable terms and conditions between a seller and a buyer. As far as the construction industry is concerned, purchasing can occur in all phases of a construction project. The purchasing function of a construction firm is central to materials management and specially includes the commitment of project funds for construction materials. Construction materials occupy a significant part of the constructions value contributing nearly 50%. Thus when selecting construction materials, it is very important that painstaking decisions should be made. Even though typically 10 to 15%, but up to 45% (WRAP, 2007) of the total materials ordered for construction projects are either unused or end-up as waste. Therefore purchasing the appropriate material is getting more important. Purchased materials and services typically represent the largest single element of cost in a company which stresses the importance of purchasing (Ibid and pooler et al, 2004 cited Otterheim, strand, 2007) The purchasing department may also contribute to a competitive position in more indirect ways. The indirect contributions may be in Reduction of quality costs Production standardization Stock reduction Increasing flexibility and fostering purchasing synergy The indirect contributions have often in practice saved more money than the indirect savings on purchasing prices (Van Weele, 2005 cited Otterheim, strand, 2007) 2.3. Significance of material procurement process According to Sun and Liu et al (2009) the process of obtaining raw materials from outside suppliers is considered as material procurement. This process consumes more cost of total operating capital. Now a day fast track approach is used to reduce the project schedule. The procurement process is very important and should be carried out in a possible manner to achieve the success of the project. According to Othman and Rahman (2010) five aspects can According to the analysis of interviews and surveys carried out during the study of the Procurement Process described in this paper (Rivas 1998), five features can expose the relevance of Procurement: Schedule pressures: Should finish the project within a less possible period, avoid or minimise financial and other indirect costs. Cooperation and coordination with construction: by following the construction schedule procurement. Improvement of the efficiency for procuring supplies will help to save the resources. High relative value: Supplies managed by procurement represents 50%, to 70% of the total cost for the project, it is imperious to have a strict and permanent control of the acquisitions, having in mind the financial approach being represented by such situation. Depends on the operation of the project needed equipments supply by the process of procurement. Potential critical of the supplies: due to important relationships and interrelation between various part of the project. Accurate situation diagnostics of the material purchase function in the construction sector, in relation with the proactive purchase implantation in material purchase functions. Therefore, the significance of this work is in analysing the validation of a purchase area with a new implementation of proactive purchasing. 2.4. Proactive purchasing The concept of proactive purchasing management is also addressed by Carr (1996), who defines proactive purchasing as purchasing willingness to take risks and to effectively use current knowledge to make decisions about the future. Purchasing pro action includes purchasing foresight and purchasing willingness to initiate change. According to Vrijhoef and Koskela (1999) the implementation of proactive purchasing in the construction industry is a challenge. The implementation success is strictly related to the strategies of the activities operation which involve the process of purchasing that guarantees the quality of the process. One tool that can be used to develop a continuous improvement process in the purchasing process is Demings PDCA cycle (1986). Demings PDCA cycle (Source:Wikipedia) According to Moen and Norman (2011), the steps in each successive PDCA cycle are Plan Create the aim and objectives and establish the process to achieve the aim and objectives with the anticipated outcome. Do Implement the plan, execute the process, and make the product. Collect needed data and information to check it in the next process. Check Compare the actual result got from Do stage with the anticipated results in planning stage. Find variations from this study. Charting the collected data may help to see trends over several PDCA cycles and in order to convert the collected data into information. Information is what you need for the next step Act. Act Take severe action on major variations between actual and planned results. Take a good study to identify the reasons for this variation. Find where the changes should be made to improve the process or product. Proactive purchasing starts in project conception, which is usually executed by the engineering or marketing areas. The responsible team for the project considers the enterprises goals and develops solutions for the product, subsystems, and components (Taylor, 2003). The quality assistance area analyses the projects and makes the proper contributions. The purchasing department participates in this process indicating new materials, rating prices quotations, and looking for new suppliers (Lawther and Martin, 2005). The next phase is characterized by the accomplishment of the programming phase of the execution of the project according to the organizational strategy. The purchasing team elaborates the purchase planning, which is based on the enterprises projects and specifications, on the production planning, and the detailed budget that reflects the organizational reality (Donk, 2004). The purchasing process must contain the procedures to put the activities that constitute its routine i nto practice, to avoid that each collaborator acts in a particular way (Andersson and Bernhardsson, 2011). This doesnt mean that the process must be set in stone but that policies should exist that orientate the elaboration of activities. With the application of the structuring of the proactive purchasing process, the team involved with purchasing used most of their time in planning activities, negotiations, and control, what makes the purchasing operational (solicitation, estimating, and purchasing) and faster (Cox et. al.,2005). When the purchase planning is done, it is necessary to effectuate its control and, if necessary, repeat the planning of the activities in order to guarantee that the production area is attended according to the negotiated conditions (Lawther, 2003). The purchasing process must be continuously analysed, so that the process bottlenecks are identified as well as the possibility of aggregating value to the process. As already described by Burt and Pinkerton (1996), the application of proactive purchasing procedures allows the material purchasing process to be focused on strategic actions, which are, the acquisitions planning realization, and also the relationship with the suppliers. Furthermore, the operational phase will likely be faster than in the traditional model, and it also meshes with the necessities of the final customer, that is, to deliver the material in the right quantities, at the right time, and under the best purchasing conditions. 2.5. Material requirement planning According to Acramin and Rahman (2011) the major purpose of material requirement planning is to ensure availability of materials in the future within the certain cost. This procedure includes the monitoring of stocks and, in particular, the automatic creation of procurement proposals for purchasing and production. (Sap, 2001) Material requirements planning to try to strike the best balance possible between Optimizing the service level and Minimizing costs and capital lockup. Four sorts of information use in material requirement planning to decide what material need to be ordered and when it will ordered (Mahbashi, 2007). Each and every product is scheduled to be manufactured. It is described in the master production schedule. Bill of materials, which lists exactly the parts or materials required to make each product. Production cycle times and material needs at each stage of the production cycle. Supplier lead times. The material purchasing process has the responsibility of supplying the customers buying necessities, it is also responsible for the planning in a quantitative and qualitative way. Moreover, it intends to guarantee that the customer will receive the material at the right time, with the right quantities, and within the desired specifications (Burt and, Pinkerton, 1996). In order to execute this important task, the material purchasing function is considered to have a fundamental role in the supply chain. This technical paper uses the proactive purchasing procedure as the purchasing management strategy, and presentation of the concept is very important. Proactive purchasing can be defined as purchasing which is focused on strategic activities. It puts emphasis on long range relationship negotiation activities, expanding the suppliers and materials total cost, instead of doing it in repeated demands and stock repositions (Burt and, Pinkerton, 1996). Making sure of purchasing continuity to keep effective relationships with existing sources, developing other supply alternatives, or attending the emergent or planned necessities, selecting the best suppliers. Keeping solid and cooperative relationships with the other organizational functions, supplying the necessary information, and advising to make sure of the effective operation of the entire organization. Developing the training of employees, and the adoption of procedures organization to make sure to reach the previous goals. Keeping a balance between quality and value, obtaining products and services in the necessary quantity and quality for the lowest cost. Surveying market tendencies. Developing methods to negotiate purchasing conditions to deal with suppliers that look for mutual benefit by means of superior economic performance. Developing and keeping good relationships with the suppliers, besides developing potential suppliers. Emitting and controlling purchasing solicitation. 2.6. Material scheduling Various types of resources are involved in construction projects, including manpower, equipment, materials, money, and space (Taghaddos and Hermann et al, 2010). Here materials are major part of the construction project. Effective scheduling of material is crucial for the success of construction projects (Lasry and Carter et al, 2008). This success implies accomplishing the project on time, in budget and with acceptable quality. Therefore, the concept of material scheduling is introduced to the construction industry as the process of improving the efficiency of the project. Providing such a material schedule is a complicated process, but has a key impact on the total cost and schedule of construction projects (Schwindt, 2005). According to Pinedo (2008) producing a realistic schedule for material in a construction project is a challenging task. It often happens that the construction process begins before enough detailed information is collected. Ensure the material availability without creating an unnecessary inventory is a major challenge to the delivery of material in the construction industry. But it can be done with the very good communication and good schedule with suppliers (Bertelsen and Nielsen 1997 cited Risku and Karkkainen et al, 2000). 2.7. Purchase planning According to USPS (2012), to obtain a best value in any purchase objectives and tactics to be established. Purchase planning is the process to help in this establishment. Effective  purchase  planning is essential to a successful construction project. As such, it needs the coordination and cooperation of a number of purchasing related parties often proves the crucial success of the project. Competing objectives of the construction industry, nature of purchase and its impacts on the project will decide the extent of the purchase planning. The success of large scale purchases, which are those with the potential to impact these objectives, need to be planned for by a purchase team that fully reflects the strategic importance of the purchase, and should involve the teams use of a wide range of supply chain business practices. The success of other purchases will not need the same level of investment, but may require some degree of planning. The good effective purchase plane will lead the project to success. According to Weele (2010), purchasing and supply function can make an important contribution to construction projects results. But many organizations handle both large and small purchases through the same standardized purchasing processes. But three purchasing methods are suggested by Ouhimmou and DAmours et. al, (2007) for different term purchasing. 2.7.1. Strategic planning According to Warszawski (1996), Strategic planning is an essential function in the construction industry and in this level the decisions taken are long ranged. In this, planning is defined as the target and the goals to be reached by the purchase area in the next five years. For Example, to institute no fail in activities that involves material purchases (Diabat and Richard et. al, 2011) 2.7.2. Tactical planning Tactical  production  planning  is a midterm  planning  process and it is concerned with shorter term decisions for purchasing (Aghezzaf and Sitompul et. al, 2009). In this, planning is defined by which and how many resources must be used to reach the goals defined by strategic planning, as well as its acquisition path and the organization of the work structuring. (Edmondson, 1999) 2.7.3. Operational Planning It selects, in a short range time period, the path for necessary operations to reach the goals (Seifert, 2003). These short range plans have a time frame of one year or less. These plans are greatly fallen in the middle and lower levels managers day to day activities. Petty cash purchasing is one of the operational planning methods. Some organizations permit the use of petty cash for small purchases. But because of frequent misuse and the lack of control in the purchasing process, most organizations discourage this practice (Parikh and Joshi, 2005). 2.8. Purchasing and delivery process Weele (2005 cited Otterheim and Strand, 2007) Define purchasing The management of the companys external resource in such a way that the supply of all capabilities, goods, services and knowledge which are essential for running, managing the companys primary and secondary activities is secured at the most favourable conditions Determining the specification of the goods and services that need to be bought Selecting the most suitable supplier and developing procedures and routines to select the best supplier from foreign countries or BOI approval supplier from Sri Lanka Preparing and making negotiations with the supplier to establish an agreement and to write up the contract through the email. Forward the Performa invoice to BOI and Get approval for that certain material. Placing the order with the selected supplier and or develop effected purchase order and handling system. Open the legal contract and delivery dates mentioned in the contract must observe. The supplier and the relevant department must agreed the correct details of the delivery schedule. Monitoring and control of the order and to secure supply (expediting) Clearing process in the port Follow up and evaluation (settling claims, keeping product and supplier files up to date, supplier rating and supplier ranking). 2.9 Construction material management 2.9.1 Material management cycle Construction materials vary from simple items purchased by direct POs to complex tasks that are purchased by sophisticated contract forms (Halpin and Woodhead, 1998). In all cases, several functions and steps comprise the material management process. Each of these functions can give rise to potential problems that need to be solved by the materials management department. Throughout the various sequential steps of materials management, several materials-oriented costs rise. Generally, those costs could be grouped into four major categories, namely, purchase costs, order cost, holding costs, and unavailability cost: The purchase cost The purchase cost of the material means the original unit price of an item added with transportation costs and freight expenses. In the construction industry many discounts are given by suppliers for the bulk orders (Hendrickson, 2000). The acquisition or order cost. The acquisition or ordered cost reflects the administrative expense associated with issuing a PO to an outside supplier. Four cost components typically make up the total acquisition cost; they are requisition, purchasing, receiving, and auditing costs (Zenz, 1994). Figure2. Sequential steps of materials management (Source: Parikh, M. A. and Joshi, K., 2005) The holding or carrying cost The holding or carrying costs are incurred because of the carried volume of inventory. Generally, they are subdivided into three sub-categories, which are capital costs, storage costs, and risk costs (Dobler et al., 1990). Capital costs are those costs or losses due to funds invested (tied-up) in the inventory that can be used for other productive purposes (Dobler et al., 1990). Storage costs are those of warehousing, handling, store workers, and equipment needed for different movements in the warehouse. Risk costs are those that could be incurred due to damage, obsolescence, deterioration, and theft. The unavailability cost. If required material unavailable in desire time then the unavailability cost will occur. Unavailability cost well known as stock out or depletion cost in manufacturing industries. Material shortages will lead the project to delay. Also it will cause to the waste of labour force (Hendrickson, 2000). 2.9.2 Material delivery and inventory control After a PO for a construction material is being submitted to the selected supplier, a period of time, usually called delivery lead time, elapses before the actual delivery of materials to the project warehouse takes place. According to Arnold and Chapman (2001) once materials are delivered, they represent an inventory used during the construction process. In this context, inventories can be regarded as materials stocked to cover upcoming future demand. Since, inventories cost the construction firm whenever the inventory level is more than zero, inventory control is applied to minimize such cost and the various other costs associated with construction materials. Figure 2.5 shows an inventory control chart, as the ones typically used in industrial and manufacturing practices. As noticed, inventoried materials are depleted to satisfy the existing project demands. Meanwhile, new material deliveries are made at specific points in time to compensate for such depletion. Furthermore, due to the uncertainty of lead times, safety stocks are commonly instated to counterbalance any late materials delivery and keep production non-stopped. There are several schemes for making material orders, such as, the cyclical or fixed order interval system, just-in-time (JIT) approach, material requirement planning (MRP) systems, fixed order quantity system (Dobler et al., 1990). And additional to that some construction industry using the software. The most popular software models use for Procurement in construction industries are, Build smart J D Edward Great Plan These software are best to monitor what you ordered and what has been delivered to site and what is the balance to be delivered to the site and when is going to happen. And also trade wise we can summarize the actual cost incurred for every single item in the BOQ by giving specific cost quotes to each trade and get the summary every month. This will go to the financial report of the moth.   Basically we can monitor the ordered materials from this software but it wont control any delays. The procurement basically from two parties, Local Suppliers. Overseas Suppliers. 2.10. Some delivery methods in the construction industry 2.10.1. Material requirement planning Material Requirements Planning (MRP) is the process that based on a software. The manufacturing process can manage by MRP inventory system. Although it is not common nowadays, MRP can conduct by hand as well. (www1.ximb.ac.in) According to ERP (2008) the aim of MRP system achieves three objectives simultaneously: Make sure the availability of the materials to the production and delivery it on time. Maintain the inventory level as low as possible. Plan manufacturing activities, delivery schedules and purchasing activities Logic of MRP In construction industry MRP mainly using for calculating the required materials and the time period (Slack, 2001). For the effective output there are three inputs are essential. Those are bill of material, inventory data and master production schedule. Here two main outputs namely planned order releases and reschedule notices (Lunn, 1992). As stated by Starr (1996), the MRP is suitable for products that do not satisfy the order point policy (OPP) models, which demand of the end product is independent or an end product orders may be placed periodically. Master production schedule According to Ong (2002) the Master Production Schedule (MPS) is the very essential thing to drive the MRP system. The main function of MPS identifies the required amount of material that should be manufactured. Bill of material Also Ong (2002) stated Bill of Material (BOM) is another major part of the MRP, which clarifies the structure of an independent demand item. A bill of material is: a listing of all of the sub assemblies, intermediates, parts, and raw materials that go into a parent assembly showing the quantity of each required to make an assembly (Starr, 1996). Inventory data Inventory data are the thing that helps to identify the inventory status to calculate the net requirement in MRP (Slack et al, 2001). MRP calculation MPS, BOM, Inventory data will use in MRP to establish the planned order release and reschedule notices (Lunn, 1992). The figure 2.5 shows generally how the MRP performs the calculations by using the logic (Slack et al, 2001). 2.10.2. Just-in-time In face of the challenges of global competition, business firms are concentrating more on the needs of customers and seeking ways to reduce costs, improve quality and meet the ever-rising expectation of their customers. To these ends, many of them have identified logistics as an area to build cost and service advantages. On the other hand, the Just-in-Time (JIT) management approach, which has long been proven effective in the manufacturing sector in increasing quality, productivity and efficiency, improving communication and decreasing costs and waste, might enhance the chances of firms to achieve cost and service advantages through logistics. (Lai and Cheng) Just in time (JIT) stimulates new directions of planning and performing activities in manufacturing systems: its effects are significant in improving the overall performance of whole organization. Conceptually, JIT is an approach that combines apparently conflicting objectives of low cost, high quality, manufacturing flexibility and delivery dependability. In short, JIT is a system that produces the required item at the time and in the quantities needed (Chung and Barkar, 2001cited Gunansekaran and Lyu, 1997) However, the potential of JIT has not been widely recognized in logistics as compared to in manufacturing. Similar to manufacturing, logistics employs processes that add value to the basic inputs used to create the end product. As the focus of JIT is on business processes, not products, the management principles of JIT can be replicated and applied in logistics. This book sets out to explore the possibilities of employing JIT to manage logistics activities, and provide an introduction to the application of JIT in the major areas of business logistics, which mainly deals with inter-organizational move-store activities (Lai and Cheng) Just-in-time principle JIT had many definitions, some of the common definitions are: (Chung and Barkar, 2001) A system that produces the required item at the time and in the quantities needed. A manufacturing system where the parts that are required to complete the finished products are produced or arrive at the assembly site as they are needed. A philosophy that centres on the elimination of waste in the manufacturing process. An inventory control philosophy whose goal is to maintain just enough materials in just the right place at just the right time to make just the right amount of product. The exact number of required units is brought to each successive stage of production at the appropriate time. Capital requirements reduced rework inventories of purchased parts, raw materials, work-in-progress and finished goods 2.10.3. Fixed re order point and fixed order quantity In this model describes the dependency of average expenses for goods holding, ordering and losses from deficit per time unit on two control parameters the order quantity and reorder point. (Kopytov and Greenglaz 2004 cited Muravjovs and Burakov, 2007) We consider a single-product stochastic inventory control model under the following conditions. The demand for goods is a Poisson process with intensity ÃŽÂ ». At the moment of time, when the stock level falls to certain level r, a new order is placed. The quantity R is called as reorder point. The order quantity Q is constant. We suppose that. The lead time L (time between placing an order and receiving it) has a normal distribution with a mean RQ†°Ã‚ ¥LÃŽÂ ¼ and a standard deviation Là Ã†â€™. There is the possible situation of deficit, when demand during lead time exceeds the value of reorder point R. We suppose that in case of deficit the last cannot be covered by expected order (Muravjovs and Burakov, 2007) Denote as Z the quantity of goods in stock at the time moment immediately after order receiving. We can determine this quantity of goods Z as a function of demand during lead time L: Expression (1) is basic. It allows expressing different economical indexes of considered process. Let T is the duration of a cycle. Length of the cycle consists of two parts: time T1 between receiving the goods and placing a new order and lead time L, i.e. 2.10.4. Cyclical or fixed order interval system In this model the order quantity is determined as the difference between the fixed stock level and quantity of goods at the moment of ordering. The analytical description of the second model has been considered by the authors in the work (Kopytov et al. 2006 cited Muravjovs and Burakov, 2007). Let us consider the model 2 with a fixed time T of the cycle, i.e. with fixed time between neighbouring moments of placing the orders (see Fig. 2). It is a single-product stochastic inventory control model under the following conditions. The demand for goods is a Poisson process with intensity ÃŽÂ ». The lead time L has a normal distribution with a mean LÃŽÂ ¼ and a standard deviation Là Ã†â€™. We suppose that lead time essentially less as time of the cycle (Muravjovs and Burakov, 2007) There is the possible situation of deficit, when the demand during the time between neighbouring moments of orders receiving exceeds the quantity of goods in stock Z at the time moment immediately after order receiving. Analogously model 1 we suppose that in case of deficit the last cannot be covered by expected order. We denote as S the goods quantity which is needed ideally for one period and it equals to the sum (Muravjovs and Burakov, 2007) Where TD is the average demand for cycle time; is the some safety stock. In the given sentence we suppose that ideally S gives us in the future the minimum of total ex