Henderson 等. - 1998 - Brand diagnostics Mapping branding effects using
- 格式:pdf
- 大小:493.33 KB
- 文档页数:22
porter波特五⼒模型详解Porter's Five ForcesA MODEL FOR INDUSTRY ANALYSISThe model of pure competition implies that risk-adjusted rates of return should be constant across firms and industries. However, numerous economic studies have affirmed that different industries can sustain different levels of profitability; part of this difference is explained by industry structure.Michael Porter provided a framework that models an industry as being influenced by five forces. The strategic business manager seeking to develop an edge over rival firms can use this model to better understand the industry context in which the firm operates.Diagram of Porter's 5 ForcesSUPPLIER POWERSupplier concentrationImportance of volume to supplierDifferentiation of inputsImpact of inputs on cost or differentiationSwitching costs of firms in the industryPresence of substitute inputsThreat of forward integrationCost relative to total purchases in industryBARRIERSTO ENTRYAbsolute cost advantages Proprietary learning curveAccess to inputsGovernment policyEconomies of scaleCapital requirementsBrand identitySwitching costs Access to distributionExpected retaliationProprietary productsTHREAT OFSUBSTITUTES-Switching costs-Buyer inclination tosubstitute-Price-performancetrade-off of substitutes BUYER POWERBargaining leverageBuyer volumeBuyer informationBrand identityPrice sensitivityThreat of backward integrationProduct differentiationBuyer concentration vs. industrySubstitutes availableBuyers' incentivesDEGREE OF RIVALRY-Exit barriers-Industry concentration-Fixed costs/Value added-Industry growth-Intermittent overcapacity-Product differences-Switching costs-Brand identity-Diversity of rivals-Corporate stakesI. RivalryIn the traditional economic model, competition among rival firms drives profits to zero. But competition is not perfect and firms are not unsophisticated passive price takers. Rather, firms strive for a competitive advantage over their rivals. The intensity of rivalry among firms varies across industries, and strategic analysts are interested in these differences.Economists measure rivalry by indicators of industry concentration. The Concentration Ratio (CR) is one such measure. The Bureau of Census periodically reports the CR for major Standard Industrial Classifications (SIC's). The CR indicates the percent of market share held by the four largest firms (CR's for the largest 8, 25, and 50 firms in an industry also are available). A high concentration ratio indicates that a high concentration of market share is held by the largest firms - the industry is concentrated. With only a few firms holding a large market share, the competitive landscape is less competitive (closer to a monopoly). A low concentration ratio indicates that the industry is characterized by many rivals, none of which has a significant market share. These fragmented markets are said to be competitive. The concentration ratio is not the only available measure; the trend is to define industries in terms that convey more information than distribution of market share.If rivalry among firms in an industry is low, the industry is considered to be disciplined. This discipline may result from the industry's history of competition, the role of a leading firm, or informal compliance with a generally understood code of conduct. Explicit collusion generally is illegal and not an option; in low-rivalry industries competitive moves must be constrained informally. However, a maverick firm seeking a competitive advantage can displace the otherwise disciplined market.When a rival acts in a way that elicits a counter-response by other firms, rivalry intensifies. The intensity of rivalry commonly is referred to as being cutthroat, intense, moderate, or weak, based on the firms' aggressiveness in attempting to gain an advantage.In pursuing an advantage over its rivals, a firm can choose from several competitive moves:Changing prices - raising or lowering prices to gain a temporary advantage.Improving product differentiation - improving features, implementing innovations in the manufacturing process and in the product itself.Creatively using channels of distribution - using vertical integration or using a distribution channel that is novel to the industry. For example, with high-end jewelry stores reluctant to carry its watches, Timex moved intodrugstores and other non-traditional outlets and cornered the low to mid-price watch market.Exploiting relationships with suppliers - for example, from the 1950's to the 1970's Sears, Roebuck and Co. dominated the retail household appliancemarket. Sears set high quality standards and required suppliers to meet its demands for product specifications and price. The intensity of rivalry is influenced by the following industry characteristics:1. A larger number of firms increases rivalry because more firms mustcompete for the same customers and resources. The rivalry intensifies ifthe firms have similar market share, leading to a struggle for marketleadership.2.Slow market growth causes firms to fight for market share. In a growingmarket, firms are able to improve revenues simply because of theexpanding market.3.High fixed costs result in an economy of scale effect that increasesrivalry. When total costs are mostly fixed costs, the firm must producenear capacity to attain the lowest unit costs. Since the firm must sell thislarge quantity of product, high levels of production lead to a fight formarket share and results in increased rivalry.4.High storage costs or highly perishable products cause a producer tosell goods as soon as possible. If other producers are attempting tounload at the same time, competition for customers intensifies.5.Low switching costs increases rivalry. When a customer can freelyswitch from one product to another there is a greater struggle to capturecustomers.6.Low levels of product differentiation is associated with higher levels ofrivalry. Brand identification, on the other hand, tends to constrain rivalry.7.Strategic stakes are high when a firm is losing market position or haspotential for great gains. This intensifies rivalry.8.High exit barriers place a high cost on abandoning the product. The firmmust compete. High exit barriers cause a firm to remain in an industry,even when the venture is not profitable. A common exit barrier is assetspecificity. When the plant and equipment required for manufacturing aproduct is highly specialized, these assets cannot easily be sold to otherbuyers in another industry. Litton Industries' acquisition of IngallsShipbuilding facilities illustrates this concept. Litton was successful in the1960's with its contracts to build Navy ships. But when the Vietnam warended, defense spending declined and Litton saw a sudden decline in itsearnings. As the firm restructured, divesting from the shipbuilding plantwas not feasible since such a large and highly specialized investmentcould not be sold easily, and Litton was forced to stay in a decliningshipbuilding market.9. A diversity of rivals with different cultures, histories, and philosophiesmake an industry unstable. There is greater possibility for mavericks andfor misjudging rival's moves. Rivalry is volatile and can be intense. Thehospital industry, for example, is populated by hospitals that historicallyare community or charitable institutions, by hospitals that are associatedwith religious organizations or universities, and by hospitals that are for-profit enterprises. This mix of philosophies about mission has leadoccasionally to fierce local struggles by hospitals over who will getexpensive diagnostic and therapeutic services. At other times, localhospitals are highly cooperative with one another on issues such ascommunity disaster planning.10.Industry Shakeout. A growing market and the potential for high profitsinduces new firms to enter a market and incumbent firms to increaseproduction. A point is reached where the industry becomes crowded withcompetitors, and demand cannot support the new entrants and theresulting increased supply. The industry may become crowded if itsgrowth rate slows and the market becomes saturated, creating a situation of excess capacity with too many goods chasing too few buyers. Ashakeout ensues, with intense competition, price wars, and companyfailures.BCG founder Bruce Henderson generalized this observation as the Ruleof Three and Four: a stable market will not have more than threesignificant competitors, and the largest competitor will have no more thanfour times the market share of the smallest. If this rule is true, it impliesthat:o If there is a larger number of competitors, a shakeout is inevitableo Surviving rivals will have to grow faster than the marketo Eventual losers will have a negative cash flow if they attempt to growo All except the two largest rivals will be loserso The definition of what constitutes the "market" is strategicallyimportant.Whatever the merits of this rule for stable markets, it is clear that marketstability and changes in supply and demand affect rivalry. Cyclical demand tends to create cutthroat competition. This is true in the disposable diaper industry in which demand fluctuates with birth rates, and in the greetingcard industry in which there are more predictable business cycles.II. Threat Of SubstitutesIn Porter's model, substitute products refer to products in other industries. To the economist, a threat of substitutes exists when a product's demand is affected by the price change of a substitute product. A product's price elasticity is affected by substitute products - as more substitutes become available, the demand becomes more elastic since customers have more alternatives. A close substitute product constrains the ability of firms in an industry to raise prices.The competition engendered by a Threat of Substitute comes from products outside the industry. The price of aluminum beverage cans is constrained by the price of glass bottles, steel cans, and plastic containers. These containers are substitutes, yet they are not rivals in the aluminum can industry. To the manufacturer of automobile tires, tire retreads are a substitute. Today, new tires are not so expensive that car owners give much consideration to retreading old tires. But in the trucking industry new tires are expensive and tires must be replaced often. In the truck tire market, retreading remains a viable substitute industry. In the disposable diaper industry, cloth diapers are a substitute and their prices constrain the price of disposables.While the treat of substitutes typically impacts an industry through price competition, there can be other concerns in assessing the threat of substitutes. Consider the substitutability of different types of TV transmission: local station transmission to home TV antennas via the airways versus transmission via cable, satellite, and telephone lines. The new technologies available and the changing structure of the entertainment media are contributing to competition among these substitute means of connecting the home to entertainment. Except in remote areas it is unlikely that cable TV could compete with free TV from an aerial without the greater diversity of entertainment that it affords the customer.III. Buyer PowerThe power of buyers is the impact that customers have on a producing industry. In general, when buyer power is strong, the relationship to the producing industry is near to what an economist terms a monopsony - a market in which there are many suppliers and one buyer. Under such market conditions, the buyer sets the price. In reality few pure monopsonies exist, but frequently there is some asymmetry between a producing industry and buyers. The following tablesIV. Supplier PowerA producing industry requires raw materials - labor, components, and other supplies. This requirement leads to buyer-supplier relationships between the industry and the firms that provide it the raw materials used to create products. Suppliers, if powerful, can exert an influence on the producing industry, such as selling raw materials at a high price to capture some of the industry's profits. The following tables outline some factors that determine supplier power.V. Barriers to Entry / Threat of EntryIt is not only incumbent rivals that pose a threat to firms in an industry; the possibility that new firms may enter the industry also affects competition. In theory, any firm should be able to enter and exit a market, and if free entry and exit exists, then profits always should be nominal. In reality, however, industriespossess characteristics that protect the high profit levels of firms in the market and inhibit additional rivals from entering the market. These are barriers to entry. Barriers to entry are more than the normal equilibrium adjustments that markets typicallymake. For example, when industry profits increase, we would expect additional firms to enter the market to take advantage of the high profit levels, over time driving down profits for all firms in the industry. When profits decrease, we would expect some firms to exit the market thus restoring a market equilibrium. Falling prices, or the expectation that future prices will fall, deters rivals from entering a market. Firms also may be reluctant to enter markets that are extremely uncertain, especially if entering involves expensive start-up costs. These are normal accommodations to market conditions. But if firms individually (collective action would be illegal collusion) keep prices artificially low as a strategy to prevent potential entrants from entering the market, such entry-deterring pricing establishes a barrier.Barriers to entry are unique industry characteristics that define the industry. Barriers reduce the rate of entry of new firms, thus maintaining a level of profits for those already in the industry. From a strategic perspective, barriers can be created or exploited to enhance a firm's competitive advantage. Barriers to entry arise from several sources:/doc/c3dc3578eefdc8d377ee3243.html ernment creates barriers. Although the principal role of the government in a market is to preserve competition through anti-trustactions, government also restricts competition through the granting ofmonopolies and through regulation. Industries such as utilities areconsidered natural monopolies because it has been more efficient to have one electric company provide power to a locality than to permit manyelectric companies to compete in a local market. To restrain utilities fromexploiting this advantage, government permits a monopoly, but regulatesthe industry. Illustrative of this kind of barrier to entry is the local cablecompany. The franchise to a cable provider may be granted bycompetitive bidding, but once the franchise is awarded by a community amonopoly is created. Local governments were not effective in monitoringprice gouging by cable operators, so the federal government has enacted legislation to review and restrict prices.The regulatory authority of the government in restricting competition ishistorically evident in the banking industry. Until the 1970's, the marketsthat banks could enter were limited by state governments. As a result,most banks were local commercial and retail banking facilities. Bankscompeted through strategies that emphasized simple marketing devicessuch as awarding toasters to new customers for opening a checkingaccount. When banks were deregulated, banks were permitted to crossstate boundaries and expand their markets. Deregulation of banksintensified rivalry and created uncertainty for banks as they attempted tomaintain market share. In the late 1970's, the strategy of banks shiftedfrom simple marketing tactics to mergers and geographic expansion asrivals attempted to expand markets.2.Patents and proprietary knowledge serve to restrict entry into anindustry. Ideas and knowledge that provide competitive advantages aretreated as private property when patented, preventing others from usingthe knowledge and thus creating a barrier to entry. Edwin Land introduced the Polaroid camera in 1947 and held a monopoly in the instantphotography industry. In 1975, Kodak attempted to enter the instantcamera market and sold a comparable camera. Polaroid sued for patentinfringement and won, keeping Kodak out of the instant camera industry.3.Asset specificity inhibits entry into an industry. Asset specificity is theextent to which the firm's assets can be utilized to produce a differentproduct. When an industry requires highly specialized technology or plants and equipment, potential entrants are reluctant to commit to acquiringspecialized assets that cannot be sold or converted into other uses if theventure fails. Asset specificity provides a barrier to entry for two reasons:First, when firms already hold specialized assets they fiercely resist efforts by others from taking their market share. New entrants can anticipateaggressive rivalry. For example, Kodak had much capital invested in itsphotographic equipment business and aggressively resisted efforts by Fuji to intrude in its market. These assets are both large and industry specific.The second reason is that potential entrants are reluctant to makeinvestments in highly specialized assets./doc/c3dc3578eefdc8d377ee3243.html anizational (Internal) Economies of Scale. The most cost efficientlevel of production is termed Minimum Efficient Scale (MES). This is the point at which unit costs for production are at minimum - i.e., the most cost efficient level of production. If MES for firms in an industry is known, thenwe can determine the amount of market share necessary for low costentry or cost parity with rivals. For example, in long distancecommunications roughly 10% of the market is necessary for MES. If sales for a long distance operator fail to reach 10% of the market, the firm is not competitive.The existence of such an economy of scale creates a barrier to entry. The greater the difference between industry MES and entry unit costs, thegreater the barrier to entry. So industries with high MES deter entry ofsmall, start-up businesses. To operate at less than MES there must be aconsideration that permits the firm to sell at a premium price - such asproduct differentiation or local monopoly.Barriers to exit work similarly to barriers to entry. Exit barriers limit the ability of a firm to leave the market and can exacerbate rivalry - unable to leave the industry, a firm must compete. Some of an industry's entry and exit barriers can be summarized as follows:DYNAMIC NATURE OF INDUSTRY RIVALRYOur descriptive and analytic models of industry tend to examine the industry at a given state. The nature and fascination of business is that it is not static. While we are prone to generalize, for example, list GM, Ford, and Chrysler as the "Big 3" and assume their dominance, we also have seen the automobile industry change. Currently, the entertainment and communications industries are in flux. Phone companies, computer firms, and entertainment are merging and forming strategic alliances that re-map the information terrain. Schumpeter and, more recently, Porter have attempted to move the understanding of industry competition from a static economic or industry organization model to an emphasis on the interdependence of forces as dynamic, or punctuated equilibrium, as Porter terms it.In Schumpeter's and Porter's view the dynamism of markets is driven by innovation. We can envision these forces at work as we examine the following changes:GENERIC STRATEGIES TO COUNTER THE FIVE FORCES Strategy can be formulated on three levels:corporate levelbusiness unit levelfunctional or departmental level.The business unit level is the primary context of industry rivalry. Michael Porter identified three generic strategies (cost leadership, differentiation, and focus) that can be implemented at the business unit level to create a competitive advantage. The proper generic strategy will position the firm to leverage its strengths and defend against the adverse effects of the five forces.Recommended ReadingPorter, Michael E., Competitive Strategy:Techniques for Analyzing Industries and Competitors Competitive Strategy is the basis for much of modern business strategy. In this classic work, Michael Porter presents his five forces and generic strategies, then discusses how to recognize and act on market signals and how to forecast the evolution of industry structure. He then discusses competitive strategy for emerging, mature, declining, and fragmented industries. The last part of the book covers strategic decisions related to vertical integration, capacity expansion, and entry into an industry. The book concludes with an appendix on how to conduct an industry analysis.QuickMBA / Strategy / Porters 5 ForcesHome | Site Map | About | Contact | Privacy | Reprints | User AgreementThe articles on this website are copyrighted material and may not be reproduced, stored on a computer disk, republished on another website, or distributed in any form without the prior express written permission of/doc/c3dc3578eefdc8d377ee3243.html .。
单位根检验的方法主要有以下几种:
1. ADF检验:即Augmented Dickey-Fuller检验,是对Dickey-Fuller检验的扩展,可以处理含有高阶滞后项的时间序列数据。
它通过在回归模型中加入差分滞后项来控制序列相关的干扰。
2. PP检验:即Phillips-Perron检验,与ADF检验类似,但使用非参数方法来修正序列相关的问题,对小样本性质有一定的改进。
3. KPSS检验:即Kwiatkowski-Phillips-Schmidt-Shin检验,是一种基于平稳序列的检验方法,原假设是序列是平稳的,而备择假设是序列存在单位根。
4. ERS检验:即Elliott-Rothenberg-Stock检验,是一种基于误差修正模型的单位根检验方法,适用于存在长期均衡关系的非平稳时间序列。
5. NP检验:即Nelson-Plosser检验,是一种专门用于检验宏观经济时间序列是否存在单位根的方法。
6. DF-GLS检验:即Dickey-Fuller Generalized Least Squares检验,是一种改进的Dickey-Fuller检验,使用广义最小二乘法来估计模型参数,以提高检验的功效。
7. 霍尔斯检验:即Hall测试,也是一种单位根检验方法,主要用于检测分数整合的存在。
8. 其他检验:还有一些其他的单位根检验方法,如Fisher类型的检验、Maddala-Wu检验等,它们在不同的情况下有各自的适用性和优势。
舍曲林对脑梗死急性期患者卒中后抑郁、神经功能及日常生活活动能力的影响张乾坤; 王昊亮; 宋景贵【期刊名称】《《河南医学研究》》【年(卷),期】2019(028)006【总页数】4页(P969-972)【关键词】舍曲林; 脑梗死急性期; 卒中后抑郁; 神经功能; 日常生活活动能力【作者】张乾坤; 王昊亮; 宋景贵【作者单位】新乡医学院第一附属医院神经内科河南新乡 453100【正文语种】中文【中图分类】R743.32卒中后抑郁(post stroke depression,PSD)是卒中后较为多见的并发症,极大影响患者神经功能恢复,并可能增加卒中后死亡率[1]。
研究报道,卒中早期预防性抗抑郁治疗可以明显改善患者精神状态,促进神经功能恢复[2-4]。
选择性5-羟色胺再摄取抑制剂(selective serotonin reuptake inhibitor,SSRI)类药物由于不良反应少、耐受性高在临床上被广泛应用。
本研究观察舍曲林对脑梗死急性期患者卒中后抑郁(PSD)、神经功能及日常生活活动能力的影响。
1 资料与方法1.1 一般资料选取2017年9—12月新乡医学院第一附属医院收治的180例脑梗死患者,按照随机数表法分为对照组和观察组,各90例。
对照组男39例,女53例;平均年龄(64.19±4.82)岁;合并高血压49例、糖尿病23例、心脏病17例、吸烟史57例、饮酒史42例。
观察组男43例,女47例;平均年龄(63.66±5.43)岁;合并高血压57例、糖尿病19例、心脏病21例、吸烟史63例、饮酒史38例。
两组患者一般资料比较,差异无统计学意义(P>0.05)。
患者及家属自愿参与本研究并签署知情同意书。
本研究经新乡医学院第一附属医院医学伦理委员会审批通过。
1.2 选取标准纳入标准:符合1995年全国第四届脑血管病学术会议制定的急性脑血管病诊断标准[5];头颅CT或MRI确诊为首次脑梗死;发病时间大于6 h且小于48 h;年龄小于85岁。
常见的有《南加利福尼亚大学测验》、《芝加哥大学创造力测验》、《沃利奇-凯根测验》等。
常见的创造力人格测量工具有《发现才能团体问卷》、《你属于哪一类人》、《探究兴趣问卷》。
《发现才能团体问卷》是瑞姆(S.Rimm)和戴维斯(G.Davis)分别于1976年和1980年研究出来的一种测试方法。
其使用世界上最早的创造力测试Posted by Ray | Posted in 创造力小故事| Posted on 18-10-2010标签:创造力, 故事, 测试X您好!如果您是第一次光临意享博客, 您可能需要订阅本站的内容以及更新.Power ed by WP Gr ee t B ox Word Pres s Pl ugin美国心理学家吉尔福特先生被很多人奉为现代创造力之父,这主要是源自50年前的一次心理学会议。
当时吉尔福特发表了一篇令人耳目一新的关于创造力的讲话,引发了人们对于创造力的极大兴趣,也让更多人开始思考这个话题。
其妙的是,在二战期间吉尔福特是一名心理学家,被指派设计一组性格测试,用来测试飞行员的性格,以便挑选出最适合作为轰炸机飞行员的人选。
于是,吉尔福特从智力方面入手,设计了一套评分系统和面试规则,用来挑选飞行员。
而另他难以接受的是,空军机构指派了一名没有任何心理学知识的人来帮助他筛选。
尽管这个人是一名退役的飞行员,但吉尔福特并不信任这位帮手。
最终,吉尔福特与退役飞行员从候选人中选择了截然不同的人选。
结果之后的统计评审结果显示,吉尔福特挑选的飞行员被击落毙命的人数,要比退役飞行员所挑选的飞行员多出很多。
吉尔福特非常沮丧,认为自己将如此之多的飞行员送上绝路,甚至一度想要自杀。
不过最终他再次振作起来,决心要找出退役飞行员挑选的人选比较出色的原因。
经过交流,吉尔福特得知,这位退役飞行员问了所有候选人这样一个问题:“在飞越德国领空时,你不幸遭遇德军的防空炮火攻击,这时你该怎么办?”随后,所有回答“我会上升飞到更高的高度”的候选人,都被退役飞行员淘汰,而那些违反飞行条例准则的人,例如回答“我可能我会俯冲”或“我会‘之’字形路线飞行”或“我会掉头避开火力”的人,却通过了面试。
肯德尔趋势检验实例-概述说明以及解释1.引言1.1 概述在肯德尔趋势检验实例这篇长文中,本文将对肯德尔趋势检验进行介绍和应用实例的探讨。
肯德尔趋势检验是一种用于分析非参数趋势的统计检验方法,它可以帮助我们判断两个变量是否存在趋势关系。
该检验方法是根据数据中的排序信息进行计算的,因此不需要对数据的分布做出任何假设。
本文的目的是通过具体的实例来展示和解释肯德尔趋势检验的原理和应用。
我们将首先介绍肯德尔趋势检验的基本概念和原理,包括其计算公式和统计量的含义。
然后,我们会通过一个实际案例来说明肯德尔趋势检验的具体应用过程,并对结果进行解读和讨论。
文章将从引言开始,介绍本文的结构和目的,明确读者可以从本文中获得的信息和知识点。
接着,我们将在正文部分详细介绍肯德尔趋势检验的概念和原理,包括其适用范围、计算方法和统计量的含义。
在应用实例部分,我们将选择一个具体的数据集,对其进行分析和检验,以展示肯德尔趋势检验的实际应用。
最后,我们将总结本文的内容,并对肯德尔趋势检验的重要性进行讨论和评价。
通过本文的阅读,读者将能够了解肯德尔趋势检验的基本概念和原理,理解其在实际问题中的应用方法,并掌握如何解读和解释检验结果。
同时,读者还可以通过本文对肯德尔趋势检验的重要性的讨论,深入思考该方法在科学研究和决策分析中的价值和作用。
1.2文章结构文章结构是指文章的整体组织框架,有助于读者更好地理解和阅读全文。
在这篇文章中,我们将按照以下结构展开讨论:第一部分是引言,主要包括概述、文章结构和目的。
在这一部分,我们将提供对肯德尔趋势检验实例文章的简要介绍,明确文章的目标和结构,使读者能够更好地理解文章内容和组织架构。
第二部分是正文,分为两个小节。
2.1小节将详细介绍肯德尔趋势检验的概念、原理和相关背景知识。
我们将解释肯德尔趋势检验的基本原理和假设,并提供其计算公式和相关统计量的解释。
此外,我们还将介绍肯德尔趋势检验的一般步骤,以使读者了解如何进行该检验。
呼吸末二氧化碳(EtCO2)在被动抬腿实验(PLRT)中评估容量反应性的价值何怀武刘大为北京协和医院重症医学科容量反应性的评估是重症患者中容量管理的重要环节之一。
从心脏静态前负荷指标(CVP、PAWP、CREDVI、GEDVI等)到心脏动态前负荷指标(SPV、△down、PPV、SVV等)人们一直在寻找简单可靠并且敏感特异的指标或方法来预测容量反应性,进而减少扩容治疗的盲目性,提高扩容治疗的有效性。
其中在预测容量反应性方法上,被动抬腿试验(Passive Leg Raising Test,PLRT)具有可逆性、可重复性、操作简单及不需要额外增加容量等优点,并且不受自主呼吸和心律失常等因素的影响,在临床上实用性强。
近来Cavallaro等荟萃分析也支持PLRT作为预测容量反应性的方法具有良好准确性和可靠性[1]。
但目前PLRT在具体临床应用推广时也面临一些制约因素,近来有学者通过观察被动抬腿实验(PLRT)中呼末二氧化碳(EtCO2)的变化来评估容量反应性,进一步拓展了PLRT的临床应用价值。
一、PLRT评估容量反应性时存在的问题人为被动抬高患者下肢可起到类似自体输血的作用,可以快速地增加回心血量300mL ~500mL左右,曾作为失血性休克早期的抢救手段之一。
抬高下肢,在重力作用下,静脉回流增加,可起到快速扩容的效果,同时监测循环系统的反应来判断是否存在容量反应性。
被动抬腿试验相当于自体模拟的容量负荷试验,但由于受到自身神经系统的调节,其作用一般可维持10min左右,并且这种前负荷的扩增效应多在抬腿早期2-3min内最为明显[2]。
但目前PLRT在临床实践中面临的最大的问题是:抬腿后观察什么指标的变化来判断容量反应性?在理论上,被动抬腿增加心脏前负荷来检验心脏的储备能力,在此期间如能进行心输出量的直接观察和监测则最为理想,在监测技术上则要求能够实时同步监测心输出量或其替代指标的变化。
大量研究证实在被动抬腿期间,通过简单地观察心率,血压的变化,不能预测容量反应性。
国际酒店品牌初探——对喜来登酒店的相关调查与研究中山大学旅游管理目录1.喜来登发展历史 (4)1.1 大事记简图 (4)1.2国际发展历程 (5)1.3中国发展历程 (6)2.喜来登在世界范围内的发展 (7)2.1发展现状 (7)2.1.1 分布情况 (7)2.1.2酒店数量 (8)2.1.3主要产品 (9)2.1.4 品牌指数 (9)2.1.5电子服务 (10)2.2宏观环境分析 (10)2.2.1优势 (10)2.2.2劣势 (10)2.2.3机会 (11)2.2.4威胁 (11)2.3未来发展趋势 (12)3.喜来登中国发展现状与分析 (13)3.1喜来登在中国发展情况 (13)3.1.1喜来登全国分布 (15)3.1.2喜来登扩张情况 (16)3.1.3喜来登酒店房价与客房总数及其比较 (17)3.2、喜来登在中国发展的五力模型分析 (19)——以广州粤海喜来登酒店为例 (19)3.2.1供应商议价能力 (19)3.2.2购买者议价能力 (19)3.2.3潜在竞争者进入的能力 (20)3.2.4行业竞争者的竞争 (20)3.2.5替代品的威胁 (21)3.3基于网络评价的国内喜来登顾客感知研究 (22)3.3.1调查背景 (22)3.3.2研究过程 (22)3.3.3评价分析 (23)3.3.4酒店对顾客评价的回应 (24)3.4国内典型案例分析 (25)3.4.1.三亚的标杆现象 (25)3.4.2 湖州喜来登温泉度假酒店的“天价” (26)4.喜来登酒店的经营特点 (28)4.1喜来登的核心价值观 (28)4.2致力于打造高端品牌 (28)4.3多种营销相结合 (29)4.3.1 整合营销 (29)4.3.2关系营销 (29)4.3.3多渠道营销 (30)4.3.4电视节目营销 (30)4.4“以浮动价格调节市场”的定价策略 (30)4.5高服务质量 (31)4.6“融入自然”的建筑理念与特点 (33)5.喜来登酒店管理特点 (33)5.1企业文化 (33)5.1.1对“关爱文化”的概述 (34)5.1.2喜来登“关爱文化”典型案例 (34)5.2人力资源管理 (35)5.2.1中国喜来登酒店人力资源现状 (35)5.2.2典型案例——呼和浩特喜来登酒店 (35)5.3 员工培训 (36)5.3.1员工培训理念与模式 (36)5.3.2员工培训典型案例 (37)5.4跨文化管理 (37)5.4.1中美企业文化差异 (38)5.4.2中美员工的文化行为差异 (38)5.4.3典型案例分析——东莞喜来登酒店的跨文化管理现状 (39)5.5酒店设施的管理 (39)5.5.1基础设施改造 (40)5.5.2基础设施报废 (40)5.5.3基础设施档案管理 (40)6.参考文献 (41)1.喜来登发展历史1.1 大事记简图•喜来登品牌诞生1937•成为在纽约证券交易所挂牌的第一家连锁酒店1947•走上国际化的扩展之路1949•推出了行业内第一个自动化的电子预订系统的“预订系统”1958•以色列特拉维夫喜来登酒店开业,标志着喜来登进入中东地区1961•拉丁美洲的第一家喜来登酒店在委内瑞拉的马库图开业1963•喜来登第100 家酒店——波士顿喜来登酒店开门营业1965•大型跨国企业ITT 收购了喜来登连锁酒店1968•喜来登旗下已拥有165 家酒店,服务宾客超过1200 万人次,并雇有员工近2 万名1969•喜来登是第一家使用免费电话(1-800-325-3535) 直接为顾客提供服务的连锁酒店1970•喜来登在澳大利亚开设了亚太地区首家喜来登酒店1973•香港喜来登酒店正式开业1974•喜来登成为第一家在中华人民共和国开始运营的国际连锁酒店1986•喜达屋酒店与度假村国际集团收购了ITT 喜来登1998•SPG俱乐部取代了喜来登国际俱乐部1999•喜来登引入甜梦之床(Sweet Sleeper™ Bed)2004•采用Link@SheratonSM微软网吧2006•引入由Core® Performance 设计的喜来登健身计划20081.2国际发展历程1. 1937年,当时公司创始人 Ernest Henderson 和 Robert Moore 在马萨诸塞州春田市收购了他们的第三家酒店,并就此成立了全球知名品牌——喜来登。
阿尔茨海默病的研究进展梁子涌;武雅静;邓远飞【摘要】阿尔茨海默病(AD)是老年人常见的慢性进行性神经系统变性病,临床表现主要为记忆力减退、进行性认知功能衰退,伴有各种精神行为异常和人格改变,严重影响患者的生活质量,2012年WHO和ADI发表的报告“痴呆:一项公共卫生重点”指出,AD的发病率为770万人/年,每4秒新发一例痴呆.AD的病因尚未阐明,目前的治疗方法尚不能阻止或逆转AD的疾病发展,且近年来在新药物研发、新治疗方法探讨等方面都遇到了挫折,但是,关于AD的研究继续在深入,本文就AD在病因机制、诊断、辅助检查技术、治疗、预防等方面的新近研究进展作一综述.【期刊名称】《中国医药科学》【年(卷),期】2018(008)016【总页数】4页(P42-45)【关键词】阿尔茨海默病;诊断;β淀粉样蛋白;综述【作者】梁子涌;武雅静;邓远飞【作者单位】北京大学深圳医院,广东深圳518036;北京大学深圳医院,广东深圳518036;北京大学深圳医院,广东深圳518036【正文语种】中文【中图分类】R749.16阿尔茨海默病(Alzheimer’s disease,AD)是一种中枢神经系统退行性疾病,起病隐袭,病程呈慢性进行性,是老年期痴呆最常见的一种类型,随着疾病的进展,将严重影响社交、职业与生活功能,2012年WHO和ADI发表的报告“痴呆:一项公共卫生重点”指出,AD的发病率为770万人/年,每4秒新发一例痴呆,2010年全球的患者数达3560万,预计2030年达6570万,2050年达11540万[1]。
AD的病因尚未阐明,目前的治疗方法尚不能阻止或逆转AD的疾病发展,详细的病史采集与体检和精神状况检查对诊断至关重要,分子神经影像学指标和家族性基因突变可作为重要的支持证据。
本文将就AD在病因机制、诊断、辅助检查技术、治疗、预防等方面的新近研究进展作一浅述。
1 病因机制1.1 基因遗传学说AD最常见的是21号染色体的淀粉样前体蛋白(APP)基因,14号染色体的早老素1(PS1)基因及1号染色体的早老素2(PS2)基因[2],散发性AD的易感基因主要是19号染色体的载脂蛋白E(ApoE)基因 [3]。
报告中的信度与效度分析方法1. 信度分析方法1.1. 内部一致性信度分析内部一致性是指问卷中各个测量项之间的一致性程度。
常用的内部一致性信度分析方法包括Cronbach's alpha、检验无重复性原则和Kuder-Richardson等。
Cronbach's alpha是一种基于项目的测量信度分析方法,它通过计算测量项之间的方差协方差矩阵来评估问卷的内部一致性。
检验无重复性原则是通过将问卷中的某个测量项删除后,观察剩余的测量项之间的相互关联情况,来评估该测量项对于问卷的内部一致性的贡献程度。
Kuder-Richardson是一种基于二元测量项的信度分析方法,适用于只有两种回答选项的测量项。
1.2. 测试-重测信度分析测试-重测信度分析用于评估同一受试者在不同时点上的测量结果之间的一致性。
常用的方法包括Pearson相关系数、Spearman相关系数和Intraclass correlation coefficient(ICC)等。
Pearson相关系数和Spearman相关系数适用于连续变量的信度分析,而ICC适用于定量变量的信度分析。
1.3. 分裂信度分析分裂信度分析用于评估问卷中不同测量项的可靠性。
常用的方法包括Spearman-Brown公式和Guttman-Split Half方法等。
Spearman-Brown公式可以根据问卷的半数测试长度和全长测试长度之间的比例来估计问卷的信度。
Guttman-Split Half方法则将问卷分成两个部分,计算两部分的分数之间的相关系数,通过比较来评估问卷的信度。
2. 效度分析方法2.1. 内容效度分析内容效度分析用于评估问卷测量项是否涵盖了研究领域全部或者大部分的内容。
常用的方法包括专家评审法和适应性检测法等。
专家评审法是将问卷交给相关领域的专家进行评审,通过专家的意见来评估问卷的内容效度。
适应性检测法是根据问卷回答者的反馈来评估问卷的内容效度,通过观察回答者对于各个测量项的理解程度和回答行为来确定问卷的内容效度。
Advances in Psychology 心理学进展, 2015, 5(12), 805-810Published Online December 2015 in Hans. /journal/ap/10.12677/ap.2015.512104Antisocial Personality Disorder Concept and Clinical Research ProgressYue DuanSoochow University, Suzhou JiangsuReceived: Dec. 9th, 2015; accepted: Dec. 21st, 2015; published: Dec. 28th, 2015Copyright © 2015 by author and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY)./licenses/by/4.0/AbstractAntisocial personality disorder is a great danger to social security, which has always been the fo-cus of scholars at home and abroad. This article, through summarizing the diagnosis of antisocial personality, clinical and biological research both at home and abroad in recent years, discusses the problems existing in the present study, and looks forward to the future research direction.KeywordsAntisocial Personality Disorder, Diagnostic Tools, Clinical Research反社会人格障碍概念及研究进展段越苏州大学,江苏苏州收稿日期:2015年12月9日;录用日期:2015年12月21日;发布日期:2015年12月28日摘要反社会人格障碍是一种对社会危害极大的人格障碍,因此也一直是国内外学者关注的焦点,本文通过对近几年来国内外反社会人格的诊断、影响因素研究、生物学研究进展的总结,对目前研究中存在的问题段越进行讨论,并展望了未来的研究方向。
Brand diagnostics:Mapping branding e ects using consumerassociative networksGeraldine R.Hendersona,*,Dawn Iacobucci b ,Bobby J.CalderbaFuqua School of Business,Duke University,Box 90120,Durham,NC 27708-0120,USAbKellogg Graduate School of Management,Northwestern University,2001Sheridan Road,Evanston,IL 60208-2001,USAReceived 1September 1997;revised 1February 1998AbstractUnderstanding consumer perceptions and associations is an important ®rst step to understanding brand preferences and choices.In this paper,we discuss how cognitive theorists would posit network representations of consumer brand associations.We rely upon several empirical examples of consumer associative networks,based on data from a variety of data collection techniques,in order to demonstrate the tools available to the brand manager using network analytic techniques.In addition to being grounded in theory,networks are shown to be quite important to mapping an extensive array of branding e ects,including:(1)branded features,(2)driver brands,(3)complements,(4)co-branding,(5)cannibalization,(6)brand parity,(7)brand dilution,(8)brand confusion,(9)counter-brands,and (10)segmentation.This list of 10issues is fairly ambitious but we desire this research to be truly useful to brand managers,and we believe we have made some progress in addressing all 10questions and in providing tools and a road map to the brand manager.Ó1998Elsevier Science B.V.All rights reserved.1.IntroductionDeveloping and maintaining high quality brand equity is of utmost importance to brand managers (cf.Aaker,1996).Brands create strategic positions and speci®c perceptual associations in the minds of consumers,they are important in product-line ex-tension development,and they signal quality and consistency critical in such moves as globalization.In this paper,we take the perspective of the brand manager who seeks answers to questions about co-branding opportunities (Spethmann and Benezra,1994),threats of cannibalization (Arnold,1992),assessments of brand parity (Aaker,1991),brand dilution (Loken and John,1993;Broniarczyk and Alba,1994;Jap,1993),brand confusion (Kap-ferer,1995;Zaichkowsky,1995),and the like.We will de®ne these terms more precisely shortly.We wish to address these questions and several others concerning branding phenomena.In this paper,we o er approaches to answering these strategic brand questions.We believe that consumer perceptions of brand properties andEuropean Journal of Operational Research 111(1998)306±327*Corresponding author.Fax:+1-919-681-6244;e-mail:gerri@0377-2217/98/$19.00Ó1998Elsevier Science B.V.All rights reserved.PII S 0377-2217(98)00151-9market structure are more important than a priori managerial statements of intended brand strate-gies,thus we argue for the empirical study of consumer perceptions and the associations they make to the focal brand.In addition,we believe that the theoretical research on mental models called``associative networks''is particularly well suited to studying these consumers'networks of perceived associations.The research on associative networks has developed primarily in psychology and has not yet been leveraged in marketing or in the study of consumer behavior to the extent it might.That is,associative networks have been discussed brie¯y,so that many marketers feel as though they have a level of familiarity with the basic tenets.However,few papers in the marketing ®eld have gone beyond the basic de®nitions of associative networks(Krishnan,1996).Further-more,we know of no research that has studied associative networks for the purpose of detecting branding e ects and strategies.We believe that the analytical approach called``network methods'' will be most suitable to study the structure of in-terconnections in these consumer associative net-works.Thus,in order to assist the brand manager in addressing branding questions in this paper,we focus on consumer perceptions,we model them as associative networks,and we analyze their struc-ture using network method techniques.The goal of this research is to investigate con-sumer brand associative networks and to demon-strate their utility to the brand manager.It is commonly held that consumers store information in memory in the form of networks(e.g.,Anderson and Bower,1973),so it would seem desirable to represent these brand associations as networks in order to allow qualitatively rich data to be mod-eled in a manner most consistent with such prev-alent views of consumer memory structure. Network representations will allow us to analyze the structure of brand associations,and these structural properties should a ord new insights into branding.In the section that follows,we®rst begin with some conceptual background on con-sumer brand associations and consumer associa-tive networks and then we proceed to more detailed discussions of how networks may be used to enhance our knowledge of branding issues.2.Consumer brand associationsConsumer brand associations are those percep-tions,preferences,and choices in memory linked to a brand(Aaker,1991).Brand associations or per-ceptions are critical components of brand equity, brand image,and brand knowledge(e.g.,Keller, 1993;Farquhar and Herr,1993;Schmitt et al., 1993).Because of such links to brand equity,brand image,preference,and choice,it is crucial for marketing managers to understand the nature and structure of associations for their brands. Brand associations can vary broadly,from physical product attributes to also include per-ceptions of people,places,and occasions that are evoked in conjunction with the brand.For exam-ple,the Pepsi brand may evoke attributes such as sweetness,cola products,competing brands(e.g., Coca-Cola),as well as associations to people(e.g., Michael Jackson),places(e.g.,a rock concert),and occasions(e.g.,thirst,a birthday party)thought by consumers to be related to Pepsi.Brand associations create value for the focal product in several ways(Aaker,1991).Associa-tions help consumers process and retrieve infor-mation(e.g.,Tybout et al.,1981),and hopefully they evoke positive a ect,as well as cognitive considerations of bene®ts that provide a speci®c reason to buy.For instance,the Gatorade brand is associated with a famous spokesperson,Michael Jordan,and it is also considered to be thirst re-plenishment after a tough workout.Brand asso-ciations can also provide a basis for new products and brand extensions;e.g.,Marriott Corporation has a reputation for(i.e.,associated with)service quality,and they created a distinct segmented service o ering when they launched Courtyard by Marriott,a chain of``no frills''hotels(Ulrich and Lake,1991).Now that we have begun to explore why brand associations are important to market-ers,we discuss how they are believed to be struc-tured in memory.3.Associative networksGiven that marketers are interested in the as-sociations that consumers hold for brands,it isG.R.Henderson et al./European Journal of Operational Research111(1998)306±327307important to determine what these associations are and how they are con®gured.A network approach to the representation of brand associations can help provide a clearer understanding of the per-ceptions consumers have of brands,as well as the perceptions they have of people,places,and oc-casions of usage for brands.Cognitive psycholo-gists have been studying associative networks for some time(e.g.,Anderson and Bower,1973;Col-lins and Loftus,1975;Ellis and Hunt,1992;Gen-tner and Stevens,1983).In general,these researchers contend that knowledge is represented as``associative networks''and that these basic structures are comprised of concept nodes(or units of information such as a person,place,or thing),and prepositional links(e.g.,like a person, of a place,or is a thing).Consider the cognitive structure depicted in Fig.1(from Aaker,1996). The nodes in this associative network include a ®rm name(i.e.,McDonald's),a product brand name(Big Mac),a generic product category (burger),features of the products(e.g.,quality, service),and people and occasions(family,social involvement).The links make various associations by connecting nodes together to form a network of ideas,or a knowledge structure.1Collins and Loftus(1975)developed an in¯u-ential network model using the concept of spread-ing activation:when a person is reminded of a stimulus(e.g.,McDonald's),activation of the node corresponding to that stimulus occurs.Activation then spreads to other nodes from the stimulus node, with the degree of spreading dependent upon the distance from the stimulus node;memory retrieval of one item produces a spread of activation to those other items that are closely related.Note that the network in Fig.1indicates that when McDonald's is activated,the consumer will make associations to ®rst family,social involvement,service,value,and meals;and next to service(again),quality,brands, products,and so on.It would appear that any of the ®rst associations would be equally likely(e.g.,Mc-Donald's as family or service or meals),but when we apply network methods shortly,we shall see how such tools can aid in the sophistication of the analysis for the brand manager beyond this naive eye-ball analysis of Fig.1.Consumer associative networks will often con-tain more than one®rm,or more than one brand. Aaker's network(1996)in Fig.1contains only one competitor,McDonald's.As we shall see,such a network can yield rich understandings of the focal brand or focal®rm.However,clearly the network in Fig.1yields no information about competition and market structure.In contrast,consider the network in Fig.2,presented by Peter and Olson (1993).This associative network contains three brands of athletic footwear(Brooks,Nike,and New Balance),all of which are clearly associated with running.Note the other qualities of the asso-ciations±Nike is connected to Brooks and to New Balance,but these two brands are not connected to each other.We might suspect therefore that these two brands are less likely to be perceived by con-sumers as similar in nature,or competitors when making purchase decisions.Note too,that only Nike is also associated with the property of``cush-ioning,''a presumably desirable attribute,one that is only indirectly linked to the other brands.In addition to cognitive psychologists,quanti-tative psychologists and some marketing research-ers have studied associative network models.The former have primarily concentrated on®nding ef-®cient network solutions as opposed to ultrametric or spatial representations(Hutchinson,1989; Klauer and Carroll,1989,1991,1995).The latter have agreed that such network models seem well suited to studying consumer memory(Bettman, 1971,1974;Calder and Gruder,1989;Schmitt et al.,1993;Krishnan,1996).However,while many cognitive theories of consumer behavior posit as-sociative network structures such as those above,1Associations in network representations of mental modelscan also be said to possess a certain strength.A strongerassociation or link will exist when it is based on manyexperiences or exposures to communications or when it issupported by a network of other links.In terms of the graphicdepiction,strength may be indicated by the thickness of thelink,the number of links between two nodes,or by a numericalindicator near the link.In addition,asymmetrical relations maybe represented if,say,Big Mac evokes McDonald's butMcDonald's evokes family and value.We are presentingsymmetric,binary ties for the purpose of simplicity,thoughwe note that all we will present can be extended to ties withstrength and direction using standard network methods(e.g.,Knoke and Kuklinski,1982;Wasserman and Faust,1994).308G.R.Henderson et al./European Journal of Operational Research111(1998)306±327rarely are they elicited empirically (e.g.,Hutch-inson,1989;Keller,1998,pp.46±49).Thus,we now turn to the elicitation of associative data,and their subsequent network representation.4.Associative networks:Elicitation and representa-tionThe process of using associative network models for the purposes of detecting branding e ects can be separated into three stages:data elicitation,representation of data as graph-theoretical or spatial structures,and network analytic techniques.The ®rst two steps are described elsewhere and have been approached in a variety of ways (Hutchinson,1989;Klauer and Carroll,1989,1991,1995).However,the use of network analytic techniques on these structures,once de-rived,has been primarily limited to social networks.In this paper,we present arguments and empirical support for analyzing networks of brand associations in similar fashion as these social net-works.We believe that such analysis yields rich information about the relationships amongst these various brands (or nodes).From the point of view of data collection,there are a variety of methods of elicitation of associations ranging from the most qualitative,such as free association and free re-sponse (Krishnan,1996;Boivin,1986;OlsonandFig.1.Aaker (1996)associative network.G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327309Muderrisoglu,1977),to more structured tech-niques such as the repertory grid,laddering (Rey-nolds and Gutman,1988),and pairwise similarity judgments (Hauser and Koppelman,1979).For the sake of demonstration,we use several di erent networks,generated in di erent ways,to show the richness and generality of the representational form.2For example,we study the networks in Figs.1and 2,those generated by Aaker (1996)and Peter and Olson (1993),we employ networks gen-erated by using the repertory grid procedure,and ®nally,we model some network proximities data.We have already seen,at least brie¯y,the networks in Figs.1and 2(which we analyze shortly),so we turn now to consider the repertory grid method.4.1.Repertory gridKelly's Repertory Grid (Kelly,1955)is a par-ticularly rich tool for data elicitation because it allows respondents to determine the set of stimulus brands as well as relevant dimensions along which to evaluate and di erentiate the brands (Kelly,1955;Sampson,1972;Zaltman and Coulter,1995).For this particular research,46individuals were recruited to participate in a study about sports cars from graduate management courses in consumer behavior and new products.Participants were asked to name seven sports car brands and to compare groups of three brands at a time using a procedure called triadic elicitation (Shaw,1981).Speci®cally,subjects were asked in what way two brands were alike,and how the third brand was di erent.For example,a respondent evaluating Porsche,Jaguar,and Camaro said that the ®rst two were similar and that the last of the three lacked mystique.The points of distinctions over multiple triads formed the dimensions upon which all brands were evaluated (e.g.,mystique-lack of mystique).The resulting data were placed in an n ´m matrix as seen in Table 1,where n equalstheFig.2.Peter and Olson (1993)network.2Although we acknowledge free association as a means for gathering associative network data,we also acknowledge that it is best employed in the elicitation of egocentric networks.Egocentric networks are those in which a focal node (e.g.,brand of car)has primary associations which are spawned from it (e.g.,fast,sporty,expensive).Since we analyze branding e ects,we have opted for more complex networks which contain both primary and secondary associations between all nodes (e.g.,brand±brand,dimension±dimension,and brand±dimension).310G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327number of dimensions(or rows)and m equals the number of brands(or columns).The subject whose data is depicted in Table1yielded seven brands(Porsche,Lamborghini,Nissan300ZX, Jaguar,Mercedes,Camaro,and Corvette)and the resultant triadic distinctions provided a total of®ve dimensions(lack of mystique,shape, classy,low price,and non-European).Those brands that were associated with the dimension at the left of the table are marked with``1's''.3The zeros indicate that the column brand is not as-sociated with the row property.For example, Nissan,Camaro,and Corvette are associated with the properties``no mystique''and``low price.''Porsches are seen as common in shape and classy,etc.The matrix X depicted in Table1indicates the associations between the brands and the dimen-sions(cf.,Breiger,1974).We might also wish to study the associations among the brands(vis- a-vis the dimensions)and those among the dimensions (with respect to the brands).To do so,we compute the raw sums of squares and cross products ma-trices,X'X to describe the brands,and XX'to describe the associations among the dimensions. To interpret the X'X matrix,note that the o -di-agonals of X'X represent the number of dimen-sions that characterize both brands i and j,and the diagonal entries represent the total number of di-mensions associated with brand i.The X'X matrix based on Table1is presented in Table2.Note that the greatest number of associations for any brand is two,and that Porsche,Lamborghini,and Jaguar share relatively many quantities in com-mon,whereas they have nothing in common with Nissan,Camaro,or Corvette for example.This matrix gives the brand manager a sense of con-sumer-perceived market structure. Analogously,XX'yields the associations among the dimensions.The o -diagonals represent the number of brands associated with both di-mensions i and j,and the diagonal entries repre-sent the total number of brands characterized by this dimension.The XX'matrix based on the data in Table1appears in Table3;note for example that four brands were described as classy,that no brands were perceived as both being classy and yet having no mystique,etc.This matrix gives the brand manager a picture of what qualities co-exist in the products in the marketplace.We can collect all these matrices and put them into a full associative matrix,A,as seen in Table4.4The upper-left submatrix in A is X'X, the lower-left portion of A contains X,the originalTable1Elicited associative matrix,XPorsche Lamborghini Nissan300ZX Jaguar Mercedes Camaro Corvette No mystique0010011 Common shape1101000 Classy1101100Low price0010011Non european00000103The researcher is free to collect data in any number of waysat this point.Data might simply be collected in a binary mannerby asking the respondent to make the judgment,``is thisstimulus associated with this property?Yes or no?''with theexpectation that the simplicity of a binary judgment is likely toyield more error-free data.Alternatively,rating scales may beused,which may be maintained in their continuous form,sothat subsequent analyses are e ectively weighted by strengths of associations,or these rating scales may be made binary(to simplify subsequent analyses)by some meaningful criterion. Similarly,these data might be frequencies;i.e.,numbers of respondents who believed brand X had some property Y,which also may be used as raw frequencies or in binary form for a simpli®ed view of the aggregate perception.We say more about aggregation later in the paper.4Note that because X'X and XX'are derived from X,clearly A will not be of full rank.However,this would only be a limitation if we were trying to apply stochastic distributions to the A matrix,rather than the X matrix.In this paper,we actually focus more on description,but more generally,should inferential statistical tests be conducted,they would necessarily be done on X,not A.G.R.Henderson et al./European Journal of Operational Research111(1998)306±327311data matrix.The upper-right contains X'for completion,and the lower-right contains XX'.5Note for simplicity,all entries in X'X or XX'that had been P 1have been set equal to 1to indicate simply the presence of an association (the zero entries were kept equal to 0).6Table 4contains all the information we need to proceed further,however sometimes it is easier to detect patterns when data are depicted diagrama-tically.For example,in Table 4,it is di cult to discern that the brands and dimensions actually split into two distinct groups,with some brands being described by some dimensions but not oth-ers,etc.Thus,the data in Table 4are presented as a network graph in Fig.3,and we see that indeed,a picture is worth a megabyte of words.The Eu-ropean brands are perceived as classy,but having acommon shape,whereas the US brands are seen as not mysterious,low-priced,and of course,non-European.At this point,we have three networks,depicted in Figs.1±3.The ®rst two came from the Aaker (1996)and Peter and Olson (1993)sources,and the third is the result of the repertory grid elicitation task.We had stated at the outset that we wished to investigate the breadth of applicability of network representations,and that we would do so over a variety of data sources.Thus,to these three net-works,we now add a fourth.Fig.4contains the network representations of perceived associations among seven brands of sports cars,with one network ®gure drawn for each of 10respondents.(The eleventh matrix is the aggregate view.)These data were obtained as pairwise similarities judgments on a 9-point scale,7Table 3Dimension matrix,XX'No mystiqueShape Classy Low price Non european No mystique 30031Shape 03300Classy 03400Low price30031Non European111Table 2Brand Matrix,X'XPorscheLamborghini Nissan 300ZX Jaguar Mercedes Camaro Corvette Porsche2202100Lamborghini 2202100Nissan 300ZX 0020022Jaguar 2202100Mercedes 1101100Camaro 0020022Corvette2225It should be noted that the matrices X'X and XX'measure proximity of a completely di erent scale than does X or X'.This is not an issue in the present paper since we dichotomize all data.However,the reader should be cautioned from trying this without dichotomizing.We would like to thank an anonymous reviewer for pointing this out.6Clearly the researcher interested in strengths of associations would retain the valued-entries,as found in Tables 2and 3.We simpli®ed the entries to indicate only the presence or absence of associations only for the purposes of ease of presentation in this paper.All the techniques we present may be pursued on the non-binary matrix elements as well.7Often in marketing,proximities data are modeled using multidimensional scaling (e.g.,Carroll and Green,1997;Kruskal and Wish,1991;Arabie et al.,1987;Carroll and Arabie,1980,1998)and in separate work,we are exploring the comparative strengths of networks and scaling.We would be happy to provide the interested reader with a copy of that manuscript.For the purposes of this paper,we are clearly focusing on how networks might be used to investigate branding phenomena to add to the diagnostic tools of the brand manager.312G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327and made binary for ease of comparison to the other networks,by the simple criterion of using the midpoint of the scale as a cuto .Thus,for exam-ple,Fig.4indicates that the subject called Allan considers the Acura NSX and Porsche to be sim-ilar,but both are di erent from Mercedes Benz. Figs.1±4provide us with a variety of networks in which to explore branding e ects.As mentioned previously,the data for these networks come from a variety of sources.Although Figs.1and2are based on hypothetical networks previously re-ported,they could have been elicited using means as qualitative as free associations or as quantita-tive as pairwise similarities judgments or any method in between.Figs.3and4,which represent real data,were derived from two very di erent methods:the Kelly Repertory Grid and pairwise similarities judgments,respectively.Although pairwise similarities judgments are well known to marketers,and are relatively easy to collect from subjects,they lack information regarding the bases for the similarity judgments.Hence,most mar-keters must also collect attribute ratings,in a separate but related task,in order to get a better understanding of the underlying judgments ren-dered.In addition,pairwise similarities judgments (as well as attribute ratings)are based on pre-de-termined stimuli and attributes.Although there are other more qualitative means of data collection(Boivin,1986;Green et al.,1973;Steenkamp et al.,1994),we use the Kelly Repertory Grid primarily because of its ability to bridge the gap between qualitative data collection media and quantitative analysis techniques.In the repertory grid method,a consumer generates and completes a grid by providing not only the brands, but also the basis upon which those brands are compared,dimensions.In addition,the repertory grid captures data in a quanti®able form,a data matrix,that allows for extensive manipulation, evaluation,and even aggregation.We brie¯y mention the variety of data sources here because we do not want the brand manager to feel con-strained with respect to data collection.That is, despite the method by which the data have been collected,there is a manner in which it can be represented that can yield insight into consumer perceptions of brands above and beyond what has currently been discussed in the marketing litera-ture.In the remainder of the paper,we now inte-grate10driving managerial branding questions with the tools of network methods as applied to these various consumer associative brand net-works.5.Mapping branding e ectsWe now present network approaches to map-ping brand phenomena using the consumer brand associative networks in Figs.1±4as empirical demonstrations.We introduce each network technique brie¯y as needed,but we note that this paper is not intended to be a network tutorial.Table4Full associative matrix,``A''Brands DimensionsPORS LAMB ZX JAG BENZ CAM VETT NOMY SH CLAS LOPR NOEU Porsche(PORS)110110001100 Lamborghini(LAMB)110110001100 Nissan300ZX(ZX)001001110010 Jaguar(JAG)110110001100 Mercedes(BENZ)110110000100 Camaro(CAM)001001110011 Corvette(VETT)001001110010 Nomystique(NOMY)001001110011 Shape(SH)110100001100 Classy(CLAS)110110001100 Low price(LOPR)001001110011 Non-European(NOEU)000001010011G.R.Henderson et al./European Journal of Operational Research111(1998)306±327313However,since we would like for this paper to be self-contained,we provide de®nitions for each network technique explored.For a more compre-hensive introduction to network methods,see Knoke and Kuklinski (1982)or Scott (1991)or for a focus on networks in marketing,see Iacobucci and Hopkins (1992)or Ward and Reingen (1990).We seek to study an ambitious agenda of branding e ects including branded features,co-branding,brand parity as well as many others.Fig.5pre-sents an overview of the many classes of branding phenomena we will explore.The 10branding questions are presented in the two right-most col-umns of the ®gure.We will describe the compo-nents in this ®gure in greater detail throughout the presentation that follows,but for now we note that the 10branding questions are organized by whether the brand manager needs to look within a single network or across several networks (i.e.,``intra-network''vs.``inter-network''analyses),Fig.3.Full associative network.This network shown is an unconnected graph comprising two disjoint cliques except for a very few missing edges to complete these two cliques,which may be due to measurement error or which could have been included had a somewhat di erent analytic method (Hutchinson,1989;Klauer and Carroll,1989,1991,1994,1995)been used.314G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327。