Brand diagnostics:Mapping branding e ects using consumer
associative networks
Geraldine R.Henderson
a,*
,Dawn Iacobucci b ,Bobby J.Calder
b
a
Fuqua School of Business,Duke University,Box 90120,Durham,NC 27708-0120,USA
b
Kellogg Graduate School of Management,Northwestern University,2001Sheridan Road,Evanston,IL 60208-2001,USA
Received 1September 1997;revised 1February 1998
Abstract
Understanding 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.Introduction
Developing 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 and
European Journal of Operational Research 111(1998)
306±327
*
Corresponding author.Fax:+1-919-681-6244;e-mail:gerri@https://www.doczj.com/doc/1911226667.html,
0377-2217/98/$19.00ó1998Elsevier Science B.V.All rights reserved.PII S 0377-2217(98)00151-9
market 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 brieˉy,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 associations
Consumer 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 networks
Given that marketers are interested in the as-sociations that consumers hold for brands,it is
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327307
important 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.1
Collins and Loftus(1975)developed an inˉu-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 models
can also be said to possess a certain strength.A stronger
association or link will exist when it is based on many
experiences or exposures to communications or when it is
supported by a network of other links.In terms of the graphic
depiction,strength may be indicated by the thickness of the
link,the number of links between two nodes,or by a numerical
indicator near the link.In addition,asymmetrical relations may
be represented if,say,Big Mac evokes McDonald's but
McDonald's evokes family and value.We are presenting
symmetric,binary ties for the purpose of simplicity,though
we note that all we will present can be extended to ties with
strength 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±327
rarely 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-tion
The 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;Olson
and
Fig.1.Aaker (1996)associative network.
G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327309
Muderrisoglu,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 brieˉy,the networks in Figs.1and 2(which we analyze shortly),so we turn now to consider the repertory grid method.
4.1.Repertory grid
Kelly'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 equals
the
Fig.2.Peter and Olson (1993)network.
2
Although 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±327
number 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 original
Table1
Elicited associative matrix,X
Porsche Lamborghini Nissan300ZX Jaguar Mercedes Camaro Corvette No mystique0010011 Common shape1101000 Classy1101100
Low price0010011
Non european0000010
3The researcher is free to collect data in any number of ways
at this point.Data might simply be collected in a binary manner
by asking the respondent to make the judgment,``is this
stimulus associated with this property?Yes or no?''with the
expectation that the simplicity of a binary judgment is likely to
yield more error-free data.Alternatively,rating scales may be
used,which may be maintained in their continuous form,so
that 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±327311
data 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).6
Table 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 a
common 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,7
Table 3
Dimension matrix,XX'
No mystique
Shape Classy Low price Non european No mystique 30031Shape 03300Classy 03400Low price
30031Non European
1
1
1
Table 2
Brand Matrix,X'X
Porsche
Lamborghini Nissan 300ZX Jaguar Mercedes Camaro Corvette Porsche
2202100Lamborghini 2202100Nissan 300ZX 0020022Jaguar 2202100Mercedes 1101100Camaro 0020022Corvette
2
2
2
5
It 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.6
Clearly 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.
7
Often 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.
312
G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327
and 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 brieˉy 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 ects
We 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 brieˉy as needed,but we note that this paper is not intended to be a network tutorial.
Table4
Full associative matrix,``A''
Brands Dimensions
PORS 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)000001010011
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327313
However,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
which network property needs to be modeled (centrality,cohesion,etc.,terms that will be de-?ned shortly),and whether the brand manager is concerned about his or her focal brand or is looking to compare across brands (i.e.,the head-ings of the last two columns in the ?gure).
For the moment,let us focus on the 10primary branding e ects presented in Fig.5.They corres-pond to the following 10questions for the brand manager: 1.Branded feature ±What features of my brand are perceived by consumers to be the most im-portant?
2.Driver brand ±Is there some brand in my company portfolio that also attracts customers and drives them to purchase our other brands?
https://www.doczj.com/doc/1911226667.html,plements ±What complementary com-bination of features might be leveraged best for the ultimate success of the brand and
?rm?
https://www.doczj.com/doc/1911226667.html,works from pairwise sports car data:(P)Porsche;(M)Mazda;(F)Ferrari;(V)Corvette;(B)Mercedes Benz;(Z)Nissan 300ZX;(A)Acura NSX.
G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327315
4.Co-branding ±What other brands exist that might be a good candidate for co-branding?
5.Cannibalization ±How can we minimize can-nibalization in our product portfolio?
6.Brand parity ±How can I assess consumers'perceived parity between my brand and its competition?
7.Brand dilution ±Is my brand's equity in jeop-ardy of being diluted if we were to introduce a brand-or line-extension that is not congruent with my existing brand image and positioning?
8.Brand confusion ±To what extent is there brand confusion in the consumers'perception of the competitive
?eld?
https://www.doczj.com/doc/1911226667.html,work properties and branding e ects.
316G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327
9.Counter-brand±What are the brands con-
sumers are most likely to choose as alterna-tives to the market leader brand?and
10.Segmentation±How can the market be seg-
mented to take advantage of the existing per-ceptions of consumers with respect to my brand relative to other brands?
Each branding question,and its appropriate type of analysis,will be discussed.
5.1.Intra-network analyses
Branding e ects1±6share the property that they are best studied within a given network.Intra-network analyses are those which are conducted at the node level.With this type of analysis,we are primarily concerned with the properties of indi-vidual nodes(brands,or related concepts)by themselves,or relative to other nodes within the same network.These properties include measures of centrality,cohesion,and position.Measures such as these can be calculated by brute force;e.g., by using spreadsheets or other software packages which perform matrix algebra.These network properties,and the corresponding branding e ects which they suggest,are discussed below.
5.1.1.Intra-network centrality
Centrality measures are indices of importance which are based on the location of a node within a network relative to other nodes.There are several di erent types of centrality that can be measured, including degree,betweenness,and closeness cen-trality(Freeman,1979;Knoke and Kuklinski, 1982;Wasserman and Faust,1994).Perhaps the most commonly used measure of centrality is called degree centrality,C D.The degree of a node, C D(sometimes called a point,p),is de?ned to be the number of other points that have a direct tie to that node(Freeman,1979;Czepiel,1974).Degree centrality is computed to be:
g D p k
n
i 1
p i Y p k Y
where n is the number of nodes in the network and p i Y p k 1,if and only if p i and p k are connected by a link n 0,otherwise.In essence,degree cen-trality measures network activity.For instance,in terms of the Peter and Olson(1993)network in Fig.2,the node Nike has the highest degree cen-trality,four,because it has more direct associa-tions than do any of the other nodes.Conversely, the node avoid-sore-knees has a degree of only one because of its single connection to the node feels-soft-to-run-in.
A second measure of centrality is based on betweenness,C B,which is often thought of as a measure of control within a network.The be-tweenness measure is de?ned in terms of proba-bilities;since there is more than one possible path, it considers the probability of using a particular path(Freeman,1979).The formal equation for betweenness centrality is:
g B p k
n
i
n
j
ij p k
for all(i`j)1k,and where
ij p k
g ij p k
g ij
Y
where g ij represents the number of geodesic paths from point i to point j and g ij p k represents the number of geodesic paths from point i to point j that contain p k.A geodesic is de?ned to be the shortest path(s)between two pairs of nodes. Therefore, ij p k represents the probability that p k falls on a randomly selected geodesic connecting i and j.Betweenness centrality reˉects the likelihood that some node will be activated as associations spread throughout the network;if a node is on many paths between other pairs of nodes,then it is ``between''many nodes and it will have a high betweenness centrality index(Freeman,1979).For example,in Fig.2,the node avoid-sore-knees is not between any pair of nodes,and so has a be-tweenness centrality index of zero.Its adjacent node,feels-soft-to-run-in is higher on betweenness centrality than running(in the lower right of the ?gure)even though the latter has more degrees. This result is true because the only way that the node avoid-sore-knees is a part of the network is through its a liation to feels-soft-to-run-in. Therefore,feels-soft-to-run-in is high on
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327317
betweenness centrality because it controls the ac-cess of avoid-sore-knees to other nodes in the network.In contrast,while access to the node running allows direct access to the three brand nodes,those nodes each have alternate paths to the other nodes in the network.Thus,the betweenness status of running is not that critical for access to the entire network.
A third type of centrality,C C,which is based on closeness,measures exactly what its name suggests: how close a node is to other nodes(Sabidussi, 1966).The index of actor centrality based on closeness is de?ned to be:
g c p k
g
i 1d p i Y p k
45à1
Y
where d(p i,p k)is the number of lines in the geodesic linking nodes i and k.Theoretically,closeness centrality is typically thought to represent inde-pendence from the control of other nodes in a network.In Fig.2,the nodes feels-soft-to-run-in, New Balance,Brooks,and cushioning all have only two direct links in the network.However,the latter node is closer to the majority of nodes in the network because of the denseness of connections near that cushioning node.These three indices are slight variants on the construct of network cen-trality,but each is a helpful tool for the brand manager seeking to identify``central''nodes,those brands or concepts important in consumer asso-ciative networks.
Branded feature.Consumer brand associative networks can be constructed or studied at di erent levels,so there are di erent e ects,which may be indicated by centrality within a network.When information is elicited from consumers instructed to focus on a single brand,or when a network contains multiple brands but the brand manager focuses on the one in his or her charge,then cen-trality would uncover those features of the brand which are most pivotal to the overall image of the brand.Furthermore,central features might them-selves be good candidates for branding,i.e., branded features.For instance,in the Peter and Olson(1993)network in Fig.2,the concept of cushioning is a node high on betweenness and closeness centrality so it would make sense to consider it as a candidate for a branded feature.It is also most closely associated with Nike,and of course,as it happens,the branded-feature Nike-Air capitalizes at least indirectly on the centrality of cushioning to the overall image of Nike prod-ucts.
Driver brand.When the brand manager studies a network to compare across brands(e.g.,com-petitors in a product class or a portfolio of product o erings within a?rm),then centrality will reˉect those brands which are most core to the product category or?rm.For instance,in the sports car networks in Fig.4,the Corvette is found to be the most central across all subjects. That is,for this set of consumers,the Corvette can be said to be the most prototypical type of sports car because it is so core,or central,to the entire product category of sports cars.Measuring cen-trality is ideal for uncovering the presence of a ``driver brand''in a?rm's portfolio of products. Aaker(1996,p.243)de?nes a driver brand as``a brand that drives the purchase decision.Its iden-tity represents what the customer primarily ex-pects to receive from the purchase.This driver brand represents the value proposition that is central to the purchase decision and use experi-ence.''While these sports cars are made by dif-ferent automobile manufacturers,we can still see how centrality addresses Aaker's concerns.Imag-ine being the owner or manager of a car dealer-ship that sells sports cars trying to determine what automobile deserves the prime window location to ``drive''the consumer in to look and purchase±these data indicate clearly that the Corvette would be that car.This result is a nice example of the coming together of empirical consumer associa-tions with network method detection,because a priori,one might have been tempted to assume the prime driver brand might be the Porsche or the Ferrari.
It should be noted that degree,betweenness, and closeness centrality are three very distinct measures.Evidence of this can be seen in the above examples in which there are di erent centrality scores for the same nodes.Despite the distinc-tiveness of these measures,it is possible for a particular node to rank low on all three.Such is the case with the node avoid-sore-knees in the
318G.R.Henderson et al./European Journal of Operational Research111(1998)306±327
Peter and Olson(1993)network in Fig.2.To-gether,these three scores seem to indicate that ``avoid-sore-knees''is not a very``central''node to the overall network.However,it may still be per-ceived as being important to the consumer(s).For this determination,one may want to collect im-portance weights on individual attributes.
5.1.2.Intra-network cohesion
While centrality focuses on nodes within the network,researchers are also interested in methods that identify subgroups within networks.Sub-groups can be based on interlocking cohesion or the equivalence of structural position.We describe cohesion in this section and equivalence in the next.Cliques are the primary measures of cohesion that exist between nodes in a network(Luce and Perry,1949,Reingen et al.,1984).Wasserman and Faust(1994,p.254)indicate that cliques are sub-groups based on complete mutuality.A perfect clique is one which consists of three or more nodes and in which all nodes are connected with all other nodes.
For instance,in the Peter and Olson(1993) network in Fig.2,the nodes Nike,Brooks,and running are all mutually connected,thus indicating a clique or cohesion.A larger clique exists in the Aaker(1996)network depicted in Fig.1:Mc-Donald's,meals,value,and service are all directly interconnected.Many cliques also exist in the networks in Figs.3and4.We now turn to the branding e ects that cohesion detects when fo-cusing on one brand or when comparing across many.
Complements.In a consumer knowledge net-work,a clique represents those features or sub-brands with the strongest mutual associations. These features are considered complements,be-cause when consumers think of one,they almost automatically think of another.Consider the four-member clique in the Aaker data in Fig.1:Mc-Donald's,service,value,meals.Although the ``McDonald's value meal''was not explicitly de-scribed in this network,and we are not sure that it even existed when this network was created,one can see that such an entity is suggested by the occurrence of this clique.The complementarity which exists between the members of this sub-group suggests the intersection between``value'' and``meals''because the connection already exists in the consumers'minds.
Co-branding.In terms of branding phenomena that incorporate multiple brands,cohesion sug-gests the possibility of co-branding.Co-branding includes the usage of ingredient brands or com-posite brands.Ingredient brands can be used as a portion of some product(e.g.,an Intel Pentium Chip inside an IBM ThinkPad Notebook Com-puter),whereas composite brands are the``bun-dling of two brands to provide an enhanced consumer bene?t or reduced cost''(e.g.,Micro-soft's and General Electric's MSNBC Cable/In-ternet o ering;Aaker,1996,p.299).Cliques indicate co-branding candidates because of the natural complementary nature of the products which already exists in the consumers'minds. These naturally occurring groupings are simply elicited from consumers and leveraged by a man-ufacturer or groups of manufacturers for the bene?t of all involved.
5.1.3.Intra-network position
The primary measure of position within a net-work is structural equivalence.Two nodes are said to be structurally equivalent if they have identical linkages to other nodes within that network(cf., Hopkins et al.,1995;Knoke and Kuklinski,1982; Wasserman and Faust,1994).In essence,struc-turally equivalent nodes are substitutes.Substi-tutability can be diagnostic for branding e ects of cannibalization and brand parity,each of which is discussed in turn.
Cannibalization.Cannibalization occurs when one of a?rm's brands steals share away from an-other.In the Aaker network(Fig.1),the following groupings were uncovered based on structural equivalence:{service,value},{brand,product}, {family,social involvement}and{Big Mac, burger}.Consider the last group,that which identi?es the substitutability of the Big Mac and a burger.The structural equivalence of these two products indicates McDonald's should be con-cerned about the cannibalization that this substi-tutability suggests between two of their own brands±a consumer ordering one is as likely in-stead to order the other because of their perceived
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327319
similarity in the associative network structure (neither is particularly central,neither is a direct association to McDonald's,etc.)In particular,the ``Big Mac''brand manager should be working to form associations in the consumers'minds that distinguish it from McDonald's more generic hamburger o erings;e.g.,``the Big Mac,it's not just another burger.''
Brand parity.Brand parity is the consumer perception of sameness amongst brands.In the sports car network in Fig.3,the following sets of nodes were found to be structurally equivalent: {Porsche,Lamborghini,Jaguar,classy},{Merce-des,common shape},{Nissan300ZX,Corvette}, and{no mystique,low price,Camaro}.Consider the{Nissan300ZX,Corvette}subgroup,for ex-ample.Even though there are12nodes within this network,these two share the speci?c associations to no mystique,low price,and Camaro.In the mind of the consumer,Nissan300ZX and Cor-vette are interchangeable.Imagine that a customer already owned,say a Corvette.The perceived brand parity between the Corvette and the Nissan 300ZX would be of primary importance to the brand manager of the non-incumbent brand the next time the customer considers a car purchase. Such knowledge would certainly improve the e -ciency of marketing campaigns targeted towards drivers.The brand manager can more readily identify potential customers,and the brand man-ager gains more precise information regarding who his/her competition really is,not as perceived by management or``market structure''but by the voice of the customer.
5.2.Inter-network analyses
In contrast to intra-network analyses,inter-network indices include density and measures of network isomorphism.In general,these measures allow for the comparison and grouping of net-works across respondents,time,or other factors of interest.Here we could make inter-temporal comparisons on the same consumer or compari-sons between two or more consumers with respect to di erences in their brand associative structures. Multiple individuals may be directly compared based on the similarity of their networks of brand associations in terms of content or structure, which in turn allows for the identi?cation of market segments.We discuss each of the two inter-network tools,and the branding e ects they can uncover.
5.2.1.Inter-network density
Density,D,is the proportion of the number of links present in a network compared to the number of possible links(Scott,1991;Knoke and Kuk-linski,1982).Network density is measured to be:
h
l
n nà1 a2
Y
where l is the number of links present and,n is the number of nodes.For instance,if the networks in Fig.4were compared,Barbara's network (D BARBARA 0.67)would be found to be much more dense than Allan's(D A L L A N 0.19).8Density can be used to identify brand dilution and brand confusion.We de?ne both.9
Brand dilution.Once density is calculated,the resulting index can be used as a measure in a number of standard statistical models.For ex-ample,density might be tracked over time to yield a good indicator of brand dilution,because a network that is very dense could indicate an un-
8The networks in Fig.4represent real data elicited from subjects in an experimental setting.The names have been changed to pseudonyms to protect their anonymity.
9It should be noted that density is a measure taken at the level of an entire network.That is,it measures the number of ties present relative to all possible ties.If the focus is on a network which consists of one brand(e.g.,the McDonald's network in Fig.1),then a change in density of this network from one time to the next,would actually indicate a change in associations to this one brand.One may debate whether an increase in associations to a brand indicates brand dilution or brand strength.Nevertheless,there has been a change in the associations to that brand.When the density of a network containing more than one brand is measured,then associated changes in density may be attributed at some higher level than that of just one brand.That is,there seems to be some confusion about the features associated to the many brands in a category or class of study(e.g.,sports cars in general or non-European sports cars in particular.)
320G.R.Henderson et al./European Journal of Operational Research111(1998)306±327
clear positioning and therefore dilute a brand's equity(Loken and John,1993).With respect to the sports car example,a more dense network would have many more associations,e.g.,to other sports cars,or the descriptor dimensions.Imagine that an advertiser wished to clarify a brand's position.The advertisement could be tested in the laboratory to measure the extent to which the manipulation decreased the brand dilution.By eliciting networks of associations and calculating density,the researcher would obtain detailed ev-idence for dilution or clarity in brand positions and associations.If the advertisement interven-tion were run in a repeated measures design,the before-after network density indices become pro-cess-tracing measures of the dilution as,or if,it occurs.
Brand confusion.We have just demonstrated that when the brand manager focuses on a single brand in a network,high density reˉects brand dilution,which is a confusion in consumers'minds regarding the features associated with the brand. When we expand the brand manager's consider-ation of multiple brands within a network,high density reˉects confusion of another sort,confu-sion between brands.Issues of brand confusion have been raised recently in the marketing litera-ture,and they can be as serious as to require legal settlement(Zaichkowsky,1995;Kapferer,1995). Brand confusion is clearly not desirable for the market leader,but may well be an intended strat-egy for a me-too brand.When de?ning density,we had noted that the density for the respondent code-named``Allan''in Fig.4is0.19,whereas ``Barbara's''is0.67.These networks contain mul-tiple brands and are a good empirical illustration of confusion.Barbara perceives greater similarities amongst the sports cars than does Allan.This is an associative network pattern not unusual for nov-ices compared to experts who can make?ner dis-tinctions amongst brands,for example.
5.2.2.Inter-network equivalence
The?nal network technique that we examine is that which detects structural equivalence or simi-larity between two network structures.A network-level measurement of equivalence can be per-formed through the computation of the Quadratic Assignment Procedure(QAP)correlation coe -cient.QAP is a nonparametric means of compar-ing proximity measures such as the ones upon which our network representations are based (Hubert,1987).In essence,it is an element-wise correlation which takes dyadic interdependencies of di erent permutations and judges the strength of correlation between them.Such network-level comparisons allow for the detection of counter-brands,and the grouping of consumers based on the similarity of their associative networks.Each is discussed below.
Counter-brand.A counter-brand is a retailer product or line brand designed to capture cus-tomers from the market leader(Kapferer,1992). Presumably a consumer would not turn to an al-ternate brand unless it was perceived as highly similar to the category leader,the more desirable brand.Speci?cally,a consumer associative net-work focused on say,McDonald's,as in the net-work of Aaker(1996)(Fig.1)describes the consumers'perceptions of features associated with the fast-food leader,namely family,value,service, etc.A counter-brand fast-food outlet would be one that elicited a similarly structured network of as-sociations;direct links to family,value,service, etc.,their interconnections,and their further con-nections to nodes like quality and burger,etc.,as per Fig.1.Once again,this empirical network demonstration is useful because while a priori, brand managers might immediately assume Burger King or Wendy's to be counter-brands to Mc-Donald's,this network suggests that attributes like value and service are more closely linked to Mc-Donald's than hamburger.Clearly this?nding makes Taco Bell's push to be perceived as pro-viding value meals a sensible strategy.To be a successful counter-brand,Taco Bell's network would need to resemble that of McDonald's,at least with respect to the links closest to the?rm. Segmentation.Finally,inter-network assess-ments of structural equivalence should also be useful in identifying market segments.The simi-larities between the associative networks of a sample of consumers can be used to sort them into empirically derived groups based on their percep-tions of features thought to be descriptive of the brands in the competitive marketplace.We dem-
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327321
onstrate this use on the10consumer networks in Fig.4.
QAP correlations were run on all possible pairs of these10respondent networks.This10′10 correlation matrix was evaluated in terms of re-spondent equivalence.The resulting structurally equivalent subgroups were:{Allan,Jennifer}, {Barbara,Edward},{Greg,Chris},each providing a market segment which can be targeted according to their shared perceptions of brands.By exam-ining the structures in Fig.4,it is easy to see that in this particular example,the segments were formed for the following reasons.Allan and Jen-nifer were grouped based on their low densities and the connection between the two Japanese cars, the Acura NSX and the Nissan300ZX.That is, based on the sparseness of their networks,the links which do exist are made that much more mean-ingful in comparison.Barbara and Edward were grouped based on the existence of the{Corvette, Ferrari,Porsche},{Porsche,Ferrari,Nissan}and {Porsche,Acura,Nissan}cliques within their networks.In other words,out of the ten networks analyzed,theirs were the only two this highly,and similarly,structured.And the last segment,Greg and Chris,was formed because of the{Porsche, Corvette,and Ferrari}clique which they have in common.
Network researchers are also often interested in using each consumer's network in a QAP re-gression analysis to predict the overall network (Krackhardt,1988).This analysis would deter-mine if the cognitive network of one person is capable of predicting the aggregate network of the group,or segment.We conducted such an analysis and the predictions are indicated in Ta-ble5.Out of the ten subjects,there was one, Kevin,whose ability to predict the aggregate network was quite high(r K E V I N9-A G G R 0.908), relative to the others.Such a person might be an ``opinion leader,''and could be quite helpful to future research concerning associative networks for such a segment.
5.2.3.Aggregation
In our illustration incorporating pairwise simi-larities data,it was straightforward to develop an aggregate network based on individual subject responses.However,with more qualitatively cap-tured data,such as that elicited via the modi?ed repertory grid technique,the aggregation process is not as neat.Clearly it would be desirable to capture knowledge structures of groups of con-sumers,thus,we present a small example here of how such aggregation might be accomplished with repertory grid data.Consider again Fig.3,an as-sociative network for an individual consumer.If such data were available for several subjects,one could develop a superset grid which contained all brands and all dimensions of sports cars for all subjects,in order to aggregate.We elicited net-work data for9subjects(including the individual represented by Fig.3)and developed a superset grid.That is,if a subject provided an evaluation of
Table5
Pairwise sports car study network measures
Subject Density Degree centrality(%)Closeness centrality(%)Betweenness centrality(%)Prediction of average(r) Allan0.1943.3313.2213.330.560
Barbara0.6723.3332.4028.890.612
Chris0.3323.33 6.9413.330.205
Deborah0.4743.3354.4724.440.633
Edward0.5736.6743.2226.670.556
Fran0.3323.3310.040.000.205
Greg0.4333.3350.0453.890.221
Jennifer0.0516.67 4.370.000.259
Kevin0.4826.6713.970.000.908
Mary0.4826.6716.52 4.810.522
Average0.4333.3315.25 1.48
322G.R.Henderson et al./European Journal of Operational Research111(1998)306±327
a particular car on a particular dimension,that evaluation was placed in the corresponding cell.However,if a subject did not provide such an evaluation,that cell was ?lled with a zero.Each person's grid was then dichotomized (for simplic-ity and to eliminate totally idiosyncratic responses)and these cells were summed across all individuals.Those cells for which more than one person pro-vided high evaluations were set equal to one and are represented in the accompanying networks as ties,whereas those cells for which one person or no people responded are represented with a lack of a tie in the network (again,the strengths of valued ties could be retained;the dichotomization is for simplicity of presentation).The resulting network is shown in Fig.6and the corresponding aggregate full associative matrix,A ,is shown in Table 6.The ability to generalize across consumers or segment the market based on di ering consumer perceptions is the primary bene?t of such an ag-gregate-level analysis.As previously mentioned,once the full associative matrix,A ,is determined,it is possible to carry out the same network analytic steps taken on individual-level data.For instance,in terms of all three measures of centrality calcu-lated from A ,Corvette and Miata are found to be the most central.That is,even after aggregating the views of several subjects,Corvette continues to
be
Fig.6.Aggregate sports car network.
G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327323
T a b l e 6A g g r e g a t e f u l l a s s o c i a t i v e m a t r i x ,A
B r a n d s
D i m e n s i o n s
V E T T F E R R J A G L A M B M A S R B E N Z M I A T M U S T N I S S
P O R S P R O B R X 7V I P E
T R A D C O N V F R G N L O P R N O E U S P T Y
C o r v e t t e 1011011110010100110F e r r a r i 0000000000000000000J a g u a r 1011010000000100000L a m b o r g h i n i 1011010000000100000M a s e r a t i 0000000000000000000M e r c e d e s 1011011000000110000M i a t a 1000011111010010111M u s t a n g 1000001110010000110N i s s a n 1000001110010000110P o r s c h e 0000001001000001001P r o b e 0000000000000000000R X 71000001110010000110V i p e r 0000000000000000000T r a d i t i o n a l 1011010000000110110C o n v e r t i b l e 0000011000000110111F o r e i g n 0000000001000001001L o w p r i c e 1000001110010110111N o n -E u r o p e a n 1000001110010110111S p o r t y 000
000100
1000011111T a b l e 7B r a n d d i a g n o s t i c p r o ?l e
B r a n d i n g e e c t
N e t w o r k p r o p e r t y
B r a n d i n g s t r a t e g y
N e t w o r k p r o p e r t y
C o m p l e m e n t s C o h e s i o n S e g m e n t a t i o n S t r u c t u r a l i s o m o r p h i s m B r a n d d i l u t i o n
D e n s i t y C o -b r a n d i n g C o h e s i o n B r a n d c o n f u s i o n D e n s i t y B r a n d e d f e a t u r e C e n t r a l i t y B r a n d p a r i t y P o s i t i o n D r i v e r b r a n d C e n t r a l i t y C a n n i b a l i z a t i o n
P o s i t i o n C o u n t e r -b r a n d S t r u c t u r a l i s o m o r p h i s m
324
G.R.Henderson et al./European Journal of Operational Research 111(1998)306±327
the most central sports car even amongst a larger set of competitors(6at the individual level versus 12at the aggregate).A marketer going after this segment of consumers,who were primarily second-year MBA students,would be aware of these views which many may argue are in contrast to how others might perceive the?eld of sports cars.
In terms of cohesion,?ve three-member cliques, two four-member cliques,two?ve-member cli-ques,and one seven-member clique were found. The seven-member clique consisted of:{Corvette, Miata,Mustang,Nissan,RX7,Low Price,Non-European}.As in the individual subject case,there appears to be a large distinction between Euro-pean and Non-European vehicles given the nature of the large clique which exists of all Non-Eu-ropean cars and related associations.In terms of co-branding,one of the e ects highlighted by cohesion,one could imagine a US/Japanese man-ufacturer combination(e.g.,Chevrolet Geo)in the area of sports cars since there tend to be perceived similarities/complementarities between non-Euro-pean manufacturers.
5.3.Summary
We have now completed our presentation of the 10questions in Fig.5.The overarching framework in the?gure organizes these branding phenomena by whether the researcher focuses on a single as-sociative network or makes comparisons between networks,computes a variety of network indices and structural characteristics,and whether the manager is momentarily consumed with a focus on a single brand or wishes to understand the struc-ture of associations across multiple brands.The combinations of these factors allowed us to in-vestigate consumer-perceived branding e ects and strategies.These are presented in a comprehensive pro?le of brand e ects and strategies in Table7. The branding e ects(complements,brand dilu-tion,brand confusion,brand parity,and canni-balization)range from the benign to ones which can have devastating e ects on a brand's perfor-mance.The strategies(segmentation,co-branding, branded features,driver brands,and counter-brands)range from the more passive(segmenta-tion)to the more aggressive(counter-branding). We believe that we have not only illustrated how networks might help the brand manager in one or two instances,but we have provided a fairly tho-rough beginning of the mapping of network solu-tions to many of the brand manager's problems.
6.Discussion
Our goal was to demonstrate how consumer brand perceptions might be represented as asso-ciative networks and how those networks might be used to develop a pro?le of brand e ects and strategies.We began by discussing the nature of consumer brand associations:that associations can be features,people,places,and occasions that are evoked when consumers think about brands.We connected existing cognitive theories of associative structures to existing literature on structural net-works for the purposes of representing consumer brand associations.Several types of data(such as those elicited by the repertory grid method)were used for the purposes of demonstrating the mod-eling of the various associative networks.
In sum,we have presented a pro?le of brand e ects and strategies in which many signi?cant questions about brands might be answered.We have highlighted this pro?le in Table7.There are many questions that might be asked regarding potential branding e ects which can be answered via network analysis and we believe that we have begun to make substantial impact on this venture. In our view,the consumer brand associative net-work approach is powerful because it allows marketers to holistically diagnose a brand.That is, perhaps it is unknown a priori what branding ef-fect or strategy might be possible.By computing such measures as centrality,cohesion,position, density,and/or structural equivalence,on the same data,the marketer would be able to diagnose his or her brand for one or more of these e ects/ strategies.On the contrary,given existing meth-ods,the brand researcher/marketer would have to determine a priori which of these branding phe-nomena might be possible,and then structure a questionnaire that would answer speci?c questions about co-branding,or brand confusion,for in-
G.R.Henderson et al./European Journal of Operational Research111(1998)306±327325
万冲镇三柏小学学籍管理制度 为了全面贯彻党的教育方针，保证学校的正常教学秩序，按照上级部门的有关规定，特制定本校学籍管理办法。 一、入学 1、一年级新生的入学。 （1）审核入学资格。凡属我校招生范围的学龄儿童，由校领导把关，按有关部门的规定，依户口本及户口复印件和接种证严格审查入学资格。 （2）新学期开始，凡被我校录取的新生，必须按规定时间到校进行注册。如因故不能按时报到的由家长持有关证明，向学校申请，经学校领导批准后可延期注册。 2、新生入学后，一年级班主任在一个月内填写“学籍卡片”，并编制花名册，报送万冲镇中心学校建立学籍档案。 3、在校学生，每学期开学必须按学校规定的日期报到注册，因故不能按期报到者，应由家长持有关证明到学校请假。否则按旷课处理。 二、转学、借读 1、凡从外地随家长迁入我辖区的学生，必须持转学证明向学校提出申请，依班额情况，经校长批准，方可办理入学手续。 2、非本辖区来我校就读，经学校领导审核转学手续，合格可插入合适年级。 3、凡在校学生随父母迁居外地，要求转学的应由家长到万冲镇中心学校申请，经学校领导批准后方可办理转学手续， 4、转进、转出的学生都要进行登记，并装入档案。 三、休学、复学 1、学生因生病或其他原因不能坚持学习时，可持医院证明，家长签字的休学申请书，向学校申请休学，经班主任审核，校长批准后，
办理休学手续，休学期限为一年。 2、学生休学期满或病愈后持休学证，医院证明回校办理复学手续，复学时按学生实际程度编班。 3、休学期满后仍不能复学者，应在休学期满前半个月内办理延期休学手续，经校领导批准后续休学。 4、学生休学期间申请提前复学者，经校领导批准，按其具体情况编班。 四、档案管理制度 （一）档案包括的内容 1、学生花名册； 2、转学登记册； 3、转入、转出学生花名册； （二）、档案管理的规定 1、除健康卡由卫生老师负责管理外，其它有关材料一律由教导处管理。 2、档案管理是一项政治性很强的工作，应严肃对待，不得草率马虎，凡有关学生档案登记的各种表格一律按统一规定填写，做到整齐准确，统一规范。 3、凡转入学生必须向学校索取转学证；凡转出学生要开出转学证，并分别登记归档.
开展“两学一做”学习教育的总体要求 1. 20XX年党中央做出在（）以上的领导干部中开展“三严三实”专题教育的安排。√ A 乡科级 B县处级 C 厅局级 D 省部级 正确答案： B 2. 习总书记说，（）是共产党人精神上的钙。√ A理想信念 B 为人民服务 C 政治信仰 D 理论水平 正确答案： A 3. 一个政党的精髓在于它的（）。× A 纲领 B 理想 C 信念 D思想 正确答案： D 4. 生活纪律是指党员在（）应当遵守的行为规则。√ A日常生活和社会交往中 B 工作时间之外 C 保证工作纪律的前提下 D 出境活动时
正确答案： A 5. 从严治党的重点在于是（）。× A 整肃党风 B 依法治党 C从严治理 D 以党风正社风 正确答案： C 6. 在“两学一做”活动中，领导要成为活动的（）。√ A 引领者 B 示范者 C 实践者 D以上说法均正确 正确答案： D 7. 开展“两学一做”，要把（）贯穿学习教育的全过程。√ A 真学 B 真懂 C 真信 D以上说法均正确 正确答案： D 多选题 8. 1942年2月的整风运动的内容是（）。√ A反动主观主义整顿学风 B反对宗派主义整顿党风 C反对党八股整顿文风 D 进行阶级斗争
正确答案： A B C 9. 我党内进行“两学一做”的目标要求做到的”四个进一步“包括（）。√ A坚定理想信念 B坚定正确的政治方向 C树立清风正气 D用于担当作为 正确答案： A B C D 10. 党员精神不振的问题有（）。√ A不作为 B不会为 C不善为 D逃避责任 正确答案： A B C D 判断题 11. 学习教育活动是落实党章关于加强党员教育的管理要求，面向群体党员深化党内教育的重要实践。√ 正确 错误 正确答案：正确 12. 党的十五大决定，继续在市级以上领导干部中深入进行党性、党风教育。× 正确 错误 正确答案：错误 13. 20XX年党中央做出开展“三严三实”专题教育的安排。√ 正确 错误
两学一做知识试题库(附答案) 提高改革决策的科学性，很重要的一条就是要广泛听取群众意见和建议，及时总结()。 A、群众创造的新鲜经验 B、各种经验 C、提炼 正确答案A 党章总纲指出马克思列宁主义揭示了()，它的基本原理是正确的，具有强大的生命力。 A、共产党执政规律 B、社会主义建设规律 C、人类社会历史发展规律 正确答案C 节约()是保护生态环境的根本之策。 A、资源 B、能源 C、水资源 正确答案A 我国的社会主义建设，必须从我国的国情出发，走()道路。 A、资本主义 B、中国特色社会主义 C、社会主义 正确答案B 党和国家的各项工作都要把有利于发展社会主义社会的生产力，有利于增强()，有利于提高人民的生活水平，作为总的出发点和检验标准。
A、社会主义国家的综合国力 B、社会主义国家的经济实力 C、社会主义国家的国家安全 正确答案A 历史告诉我们每个人的前途命运都与()紧密相连。 A、人的努力奋斗 B、国家和民族的前途和命运 C、经济发展水平 正确答案B 党的各级组织要自觉担负起执行和维护()的责任，加强对党员遵守政治纪律的教育。 A、政治纪律 B、纪律 C、党章规定 正确答案A 党组织对违犯党的纪律的党员，应当本着()的精神，按照错误性质和情节轻重，给以批评教育直至纪律处分。 A、从严治党 B、批评与自我批评 C、惩前毖后、治病救人 正确答案C 党的十八大的主题是高举中国特色社会主义伟大旗帜，以邓小平理论、“三个代表”重要思想、科学发展观为指导，解放思想，改革开放，凝聚力量，攻坚克难，坚定不移沿着中国特色社会主义道路前进，为全面()小康社会而奋斗。
陕西省中小学学籍系统相关操作说明 一、如何新增学校部门 二、如何新增学校职工 三、如何提交教师审核 四、如何新增学校的角色名称 五、如何将角色授权给相应教师 六、年级班级设置 七、如何查看毕业学生和在校学生学籍号及其它信息 八、如何打印和上传学生确认表 九、如何增加权限（如需要增加关键数据变更模块） 十、如何修改学生信息 十一、学籍管理系统照片规格要求和上传方法 十二、学籍管理系统网址如果打不开 一、如何新增学校部门 点击【部门管理】菜单添加学校相关部门
点击【新增】按钮添加学校部门 【备注】新增部门时由于学校职工尚未加入所以负责人、分管领导无法选择，可暂不选择，待职工加入后可以去选择。 学校录入教师范围：本校所有在职的正式、临聘、包括借调兼职人员全部按照部门录入二、如何新增学校职工 点击【教职工维护申请】
可点击【新增】单个新增学校职工，也可点击【导入】 单个新增页面 批量新增页面 【注意】学校职工导入时注意编号不能重复，导入时部门必须为系统在“部门管理”中已经增加的部门。 导入教师模版
三、如何提交教师审核 凡是单个新增或者批量导入的教师都会出现在未提交按钮下如上图 单选或者全选教师点击【提交】按钮，既可将教师信息提交教育局审核，在“待审核”中可以看到已经提交的尚未审核的教师，在“审核未通过”的地方可以看到审核未通过的教师及原因。 四、如何新增学校的角色名称 点击【角色管理】菜单按钮 首先可以点击各个系统默认的内置角色，检查默认是否正确，以本次学籍涉及的学校1个默认角色为例，现介绍如下： 1、中小学学籍学生数据上报审核（下级学校录入学生数据后提交审核时教育局学生数据提交角色） 点击后正确的设置如图所示，如不正确没有勾选框，请请点击勾选框勾选且保存。
---------------------------------------------------------------最新资料推荐------------------------------------------------------ 中小学学籍管理情况汇报材料 中小学学籍管理情况汇报材料中小学学籍管理情况汇报材料[1/2] 各位领导、老师： 大家好！能坐在这里就学籍管理工作跟大家做个交流汇报，我感到非常荣幸！我们老学籍管理员都知道，学籍管理是一项非常琐碎且繁杂的工作，最需要我们用十分的细心、耐心去对待。 下面我分三个方面向大家汇报我的工作情况。 一、开学报到人数统计。 1.报到情况统计。 学生报到的时候也是我们管理员最忙的时候，报到一结束，我就到每个班级去统计报到谅情况。 （结合表格讲解曲 1）具体是按照上学期耘人数，加上转进生人数斑，减去转出算出一个迎应到人数，再从班主侮任那里了解实到人数扒，如果这两个数据不镇符合，就要将那个学生殊找出来，记下名字，并敢询问具体情况，先让班庸主任联系家长，若班主习任联系有困难的，我就砧要逐个打电话，并记录毫下具体情况。 请看 09 敖年 8 月 31 日学生报到介情况统计表，9 月 1 日诛报到后再次到每个班级蜒询问并记录。 （接着根谤据 10 年人数统计表格肉讲解 2） 2.开学时发城的统计表。 1 / 6
每学年告开学初，学校都要做事熟业单位统计报表，其中吁关于学生这块是要我们套学籍管理员提供数据的弹，会计报表上的小数据跨非常多，细到每个班级淬每个年龄的男、女生人碰数、少数民族人数、转诸进转出人数等等，纵横执表里只要错了一个数据淆，电脑就无法录入。 为悠了保证这项细致的工作丙不出错，我制作了这样征两张表格在开学初发给猜班主任。 （出示表格并傻讲解 3）在这两张表格郧的帮助下，几次的事业伴报表上的数据都是一次渴性成功，没有任何出入喳。 二、学生花名册的制鸥作。 1.信息收集。 界收集每个学生的信息补是第一步，这个环节一钒定要细，要把我们可能廓需要的所有信息都收集练到，否则后面的表格都故无法做好。 请看我做的旭学生信息收集表。 （出一示表格 4）将此表发给旨班主任，每个学生填写奇完整后上交班主任，班颁主任根据学生的信息表止完成班级名册的初步制厉作。 2.选择与整理。 姬班级学生名册的整楷理是开学初我们管理员帚的工作之一，当我第一由年接手学籍工作时，我寥没有经验，就根据老管汕理员给我的表格制作，姻（打开表格 4）。
两学一做知识竞赛试题 “两学一做”学习教育，指的是“学党章党规、学系列讲话，做合格党员”学习教育。那么关于两做一学的知识测试题你做了吗?下面yjbys小编为大家提供的是两做一学的选择题及答案，希望对大家有所帮助! 1、《党章》规定，党的建设的基本要求是。(ABCD) A、坚持党的基本路线 B、坚持解放思想，实事求是，与时俱进，求真务实 C、坚持全心全意为人民服务 D、坚持民主集中制 2、党章规定，党员必须履行的义务有条。(C) A、6 B、7 C、8 D、9 3、党章规定，党员享有的权利有条。(C) A、6 B、7 C、8 D、9 4、科学发展观是发展中国特色社会主义必须坚持和贯彻的(B) A.基本国策 B.指导思想 C.根本方针
D.基本标准 5、是我们党执政兴国的第一要务(A) A、发展 B、改革 C、开发 D、稳定 6、做好各项工作的总的出发点和检验标准是(ABD) A、有利于发展社会主义社会的生产力 B、有利于增强社会主义国家的综合国力 C、有利于党的执政水平提高 D、有利于提高人民的生活水平 7、我国现代化建设必须促进同步发展。(ABCD) A、工业化 B、信息化 C、城镇化 D、农业现代化 8、加强社会主义核心价值体系建设，坚持马克思主义指导思想，树立中国特色社会主义共同理想，弘扬以为核心的民族精神，以为核心的时代精神。(B) A、改革创新爱国主义 B、爱国主义改革创新 C、爱国主义改革发展 D、爱国主义科学发展 9、预备党员的权利，除了没有以外，也同正式党员一样。(ABD)
A、表决权 B、选举权 C、对党的工作提出建议 D、被选举权 10、中央委员会认为有必要，或者以上的省一级组织提出要求，全国代表大会可以提前举行。(A) A、三分之一 B、三分之二 C、二分之一 D、四分之三
两学一做知识题库及参考答案 单选题 0为人民服务是党的根本宗旨，以人为本、()是检验党一切执政活动的最高标准。 A、执政为民 B、依靠人民 C、加快发展 D、造福于民 0党内()是党的生命。 A、监督 B、纪律 C、民主 D、原则 0十八大报告提出，要实行()提案制。 A、党代会代表 B、党内选举 C、全委会决策 D、党代会 0要严格党内()，健全党员党性定期分析、民主评议等制度。 A、民主生活 B、组织生活 C、政治生活 D、学习生活
0党的十八大报告中提出坚持标本兼治、综合治理、惩防并举、注重预防方针，全面推进惩治和预防腐败体系建设，做到干部清正、政府清廉、()。 A、正大光明 B、明镜高悬 C、队伍廉洁 D、政治清明 0党的()纪律是维护党的集中统一，保持党的战斗力的重要保障。 A、政治 B、组织 C、经济 D、群众 0党员受到警告处分()内、受到严重警告处分()内，不得在党内提升职务和向党外组织推荐担任高于其原任职务的党外职务。 A、一年半两年 B、一年一年半 C、半年一年 D、一年两年 0党员受到撤销党内职务处分，或者依照前款规定受到严重警告处分的，()内不得在党内担任和向党外组织推荐担任与其原任职务相当或者高于其原任职务的职务。 A、半年 B、一年 C、两年 D、三年 多选题
0党的十八大报告指出，“坚定理想信念，坚守共产党人精神追求”，并以此为主线对党的思想建设进行全面的部署，要抓好()。 A、思想理论建设这个根本 B、党性教育这个核心 C、反腐倡廉这个重点 D、道德建设这个基础 、党的十八大报告指出“抓好道德建设这个基础，教育引导党员、干部模范践行社会主义荣辱观，做()，以实际行动彰显共产党人的人格力量。” A、社会主义道德的示范者 B、诚信风尚的引领者 C、公平正义的维护者 D、社会和谐的推动者 填空题 、党必须按照总揽全局、协调各方的原则，在同级各种组织中发挥_______作用。 、党必须集中精力领导_______，组织、协调各方面的力量，同心协力，围绕_______开展工作，促进_______全面发展。 、党必须在_______和_______的范围内活动。 、党必须适应形势的发展和情况的变化，完善__________，改进__________，增强__________。 、共产党员必须同党外群众亲密合作，共同为建设_______而奋斗。 【参考答案】 ACABDBBCABD.ABC .领导核心 .经济建设经济建设经济社会
四川省中小学生学籍信息管理系统（义务教育阶段） 运行使用办法（试行） 第一章总则 第一条为规范全省义务教育阶段学生的电子学籍管理，确保四川省中小学生学籍信息管理系统顺利运行，根据《中华人民共和国义务教育法》、《教育部关于做好全国中小学生学籍信息管理系统建设工作的通知》(教基一函〔2012〕12号)、《四川省教育厅关于进一步做好学籍管理系统建设工作的通知》（川教函〔2013〕311号）等有关法律和政策文件，特制定《四川省中小学生学籍信息管理系统（义务教育阶段）运行使用办法（试行）》，以下简称《办法》。 第二条我省辖区内所有义务教育阶段学生学籍信息均应纳入四川省中小学生学籍信息管理系统（以下简称学籍管理系统），实施电子化管理。 第三条学籍管理系统采用“一级建设，四级应用”，“一级建设”指建立省级数据业务中心进行物理部署，“四级应用”指省、市（州）、县（市、区）、学校四级应用。实行“学校登录，以县为主，分层汇总，全省统一”的原则进行管理。 学籍管理系统中的数据信息由省、市（州）、县（市、区）三级教育行政部门与学校共同管理。学校负责学生信息的采集、初审、录入、更新和本校基本信息登录及维护；县（市、区）教育行政部门负责所辖学校、学生信息的审核和本单位信息维护； — 1 —
市（州）教育行政部门负责辖区内所有学校、学生信息的最终确认和本单位信息登录及维护。 本系统实行定期锁定及开放。所有在学籍管理系统中新增或修改信息只能在每年3月1日至3月31日和6月1日至9月30日期间进行。 已在学籍管理系统中生成的信息，因错误确需修改而在系统中无法进行修改的特殊情况，由市（州）教育行政部门在系统开放期内以正式文件向省教育厅提出修改申请，省教育厅统一处理。 第四条学生学籍必须在学籍管理系统中进行注册方可获得。学生的学籍号包括学籍主号和学籍辅号（编码规则附后）。学籍主号为学生身份证号；港澳台学生的学籍主号采用该学生的回乡证号；华侨及外籍学生的学籍主号采用该学生的护照号。学籍辅号在学生注册学籍成功后由学籍管理系统自动生成。学生的学籍号全省唯一，终身使用。 第五条学生学籍不得同时在两所学校注册。学生入学注册学籍时，学校应确认学生未在其他学校注册过学籍。学校以虚假信息注册的学生学籍一律无效。 第六条省教育厅统筹组织全省中小学生电子学籍管理，指导、监督、检查各地和学校的电子学籍管理工作。省教育厅基础教育处牵头负责，其他处室和有关单位按相关要求各负其责。 各市（州）、县（市、区）教育行政部门应制定适应本地学生学籍电子化管理实际需要的管理办法，落实管理人员、职责， — 2 —
两学一做知识测试题及答案大全 两学一做知识测试题及答案 一、单项选择 1.《中国共产党廉洁自律准则》的施行日期为____。(C) A2015年10月12日B2015年10月18日C2016年1月1日 2.《中国共产党廉洁自律准则》对全体党员和各级党员领导干部提出了____的总要求。(A) A“三个必须”B“四个必须”C“五个必须” 3.《中国共产党廉洁自律准则》对全体党员提出了____条廉洁自律规范。(B) A3B4C5 4.《中国共产党廉洁自律准则》要求党员领导干部廉洁修身，____。(C) A自觉维护人民根本利益 B自觉保持人民公仆本色 C自觉提升思想道德境界 5.《中国共产党廉洁自律准则》要求党员领导干部廉洁用权，____。(A) A自觉维护人民根本利益 B自觉保持人民公仆本色 C自觉提升思想道德境界 6.《中国共产党廉洁自律准则》要求党员领导干部____，自觉保持人民公仆本色。(B) A廉洁用权B廉洁从政C廉洁修身 7.《中国共产党廉洁自律准则》要求党员领导干部____，自觉带头树立良好家风。(A) A廉洁齐家B廉洁修身C廉洁用权 8.《中国共产党廉洁自律准则》分为两部分：一是党员廉洁自律规范;二是_____。(C) A领导廉洁从政规范 B领导干部廉洁自律规范
C党员领导干部廉洁自律规范 9.《中国共产党纪律处分条例》的印发日期为____。(A) A2015年10月12日B2015年10月18日C2016年1月1日 10.____是最根本的党内法规，是管党治党的总规矩。(C) 两学一做基本知识测试题库及答案 一、填空题(每空1分，共20分) 1.为维护党的章程和其他党内法规，严肃党的纪律，纯洁党的组织，保障党员民主权利，教育党员遵纪守法，维护党的团结统一，保证党的路线、方针、政策、决议和国家法律法规的贯彻执行，根据《中国共产党章程》，制定《中国共产党纪律处分条例》。 2.《中国共产党纪律处分条例》适用于违犯党纪应当受到党纪追究的党组织和党员。 3.党员受到警告处分一年内、受到严重警告处分一年半内不得在党内提升职务和向党外组织推荐担任高于其原任职务的党外职务。 4.《党章》规定，中国共产党是中国工人阶级的先锋队，同时是中国人民和中华民族的先锋队，是中国特色社会主义事业的领导核心,代表中国先进生产力的发展方向，代表中国先进文化的前进方向，代表中国最广大人民群众的根本利益。 5.党章是最根本的党内法规，是管党治党的总规矩。 6.生活奢靡、贪图享乐、追求低级趣味，造成不良影响的，给予警告或者严重警告处分;情节严重的，给予撤销党内职务处分。 7.对党员的纪律处分种类分警告、严重警告、撤销党内职务、留党察看、开除党籍五种。 8.在党内组织秘密集团或者组织其他分裂党的活动的，给予开除党籍处分。 9.党员领导干部违反有关规定组织、参加自发成立的老乡会、校友会、战友会等，情节严重的，给予警告、严重警告或者撤销党内职务处分。 10.党员受留党察看处分期间，没有表决权、选举权和被选举权。 二、单项选择(每题1分，共30分)
参考题 1.中国共产党是哪个阶级的先锋队？中国工人阶级 2.党的最高理想和最终目标是什么？实现共产主义 3.什么揭示了人类社会历史发展的规律？马克思列宁主义 4.哪次会议以后，以邓小平同志为主要代表的中国共产党人，总结建国以来正反两方面的经验，解放思想，实事求是，实现全党工作中心向经济建设的转移？十一届三中全会 5.我国正处于并将长期处于什么阶段？社会主义初级阶段。 6.《党章》指出，必须坚持和完善以什么经济制度为主体、多种所有制经济共同发展的基本经济制度？公有制 7.留党察看最长不超过多长时间？两年。 8.党员在留党察看期间同其他党员相比，没有那三项权利？表决权、选举权和被选举权。 9.对党的中央委员会和地方各级委员会的委员、候补委员，给以撤销党内职务、留党察看或开除党籍的处分，必须由本人所在的委员会全体会议几分之几以上的多数决定？三分之二 10.《党章》指出，要坚持和完善以什么分配制度为主体、多种分配方式并存的分配制度？按劳分配制度 11.中国共产党在领导社会主义事业中，必须坚持什么以为中心，其他各项工作都服从和服务于这个中心？经济建设 12.实施科教兴国战略、人才强国战略和可持续发展战略，要充分发挥什
么作为第一生产力的作用？科学技术 13.坚持什么是我们的强国之路？改革开放 14.中国共产党领导人民发展社会主义市场经济。毫不动摇地巩固和发展公有制经济，毫不动摇地鼓励、支持、引导非公有制经济发展。要发挥市场在资源配置中的什么作用？基础性作用 15.党在任何时候都把什么利益放在第一位？群众利益 16.我们党的最大政治优势是什么？密切联系群众 17.党的各级代表大会的代表和委员会的产生，要体现选举人的意志。选举采用怎样的投票方式?无记名 18.凡是成立党的新组织，或是撤销党的原有组织，必须由什么组织决定？上级党组织 19.有关全国性的重大政策问题，只有什么机构有权作出决定，各部门、各地方的党组织可以向中央提出建议，但不得擅自作出决定和对外发表主张。党中央 20.党组织讨论决定问题，必须执行什么原则？少数服从多数 21.党的全国代表大会几年举行一次？每五年 22.党的全国代表会议调整和增选中央委员及候补中央委员的数额，不得超过党的全国代表大会选出的中央委员及候补中央委员各自总数的几分之几？五分之一 23.中央委员会全体会议由中央政治局召集，每年至少举行几次？一次。 24.在全国代表大会闭会期间，什么机构执行全国代表大会的决议，领导党的全部工作，对外代表中国共产党？中央委员会
中小学学籍管理系统2008 实施方案 一、中小学学籍管理系统2008的设计依据 1、中小学学籍管理系统2008设计的软件平台 《中小学学籍管理系统2008》采用微软公司最流行的数据库管理系统Visual Foxpro设计，Visual Foxpro是XBase系列数据库管理系统中革命性的一代产品，它具有改进的界面、增强的数据库性能、面向对象的程序设计、新的数据库容器。Visual Foxpro 提供了经过改进而更可靠的Automation服务程序，这些服务程序能更好地与Microsoft Transaction Server, Microsoft Server Pages产品紧密协作。Visual Foxpro是企业级解决方案的最佳选择。 2、中小学学籍管理系统2008设计的学籍依据 《中小学学籍管理系统2008》设计时严格按照《福建省普通中学学籍管理和成绩考核暂行办法》的规定，软件所涉及的表格样式均按该办法提供的样式表设计。软件设计时得到培元中学学籍管理员王永章老师的顶力支持，软件真正做到“从学校中来，到学校中去”的设计流程。所有功能都经周密考虑与设计。 二、中小学学籍管理系统2008的版本区分与收费方法
中小学学籍管理系统2008分为“学校版”和“教委版”，学校版用于学校学籍管理室安装，教委版用于教育局学籍管理科安装。 学校版每校分别定制，内嵌该校学校名称，并在软件启动后显示于屏幕中间。 教委版软件安装后将显示教育局名称的界面：
中小学学籍管理系统2008学校版以学校为单位，每校分别计费。教育局版若学校版数量超过50套时赠送，否则按学校版的两倍价格收取。 三、中小学学籍管理系统2008学校版与教委版间的数据传递 学校学籍管理员负责输入（或导入）新生名册，卡片一（学籍基本信息），卡片二（学籍成绩信息），并做好学籍变动登记操作（转学、留级、借读、寄读、寄藉等）。后通过“数据接口”菜单下的“上报到县市教育局数据”：
“两学一做”学习教育知识测试题及答案（判断题） “两学一做”学习教育知识测试题及答案（判断题） 1.广西壮族自治区永福县违规发放考核奖金共计208.9万元，县委书记黄永跃作为直接责任人受到党内严重警告处分，并被免去县委书记职务；违规发放的奖金全部清退。县委常委、县纪委书记经翠艳履行监督责任不力，受到党内警告处分，调离纪委书记岗位。这样处理妥否？（A） A.正确 B.错误 2.共产党员诬告陷害他人的，给予警告或者严重警告处分；情节较重的，给予撤销党内职务或者留党察看处分；情节严重的，给予开除党籍处分。（ A ） A.对 B.错 3.共产党员在操办婚丧喜庆事宜中，借机敛财或者有其他侵犯国家、集体和人民利益行为的，从重或者加重处分，直至开除党籍。（ A ） A.对 B.错 4.党员受到留党察看处分，其党内职务自然撤销。（A ） A.对 B.错 5.减轻、加重处分，是指在应当受到的处分幅度以内给予较轻或者较重的处分。（B） A.对 B.错 6.串供或者伪造、销毁、隐匿证据的，可以依照规定从重或者加重处分。（ A ） A.对 B.错 7.党员一人犯有两种以上（含两种）应当受到党纪处分的违纪行为，可按数种违纪行为中应当受到的最高处分给予处分。（ B ） A.对 B.错 8.对因故意犯罪被依法判处《刑法》规定的主刑（含宣告缓刑）的党员，应当给予开除党籍处分。（ A ） A.对 B.错 9.直接经济损失，是指与违纪行为有直接因果关系而造成财产损毁的实际价值。（ A ） A.对 B.错 10.对于违纪行为所获得的经济利益，应当收缴或者责令退赔。（ A ） A.对 B.错 11.对于全体或者多数党员严重违犯党纪的党组织，应当予以改组。（ B ） A.对 B.错 12.党员重婚或者包养情妇(夫)的,给予开除党籍处分。（A ） A.对 B.错 13.党的工作人员,是指党的各级机关中除工勤人员以外的工作人员
两学一做测试题及答案 2016年两学一做测试题及答案 单项选择： 1.加强党的作风和纪律教育，需教育和引导各级领导干部自觉遵守党的政治纪律、组织纪律、经济工作纪律和(D)纪律。 A学习 B劳动 C考试 D群众工作 2.遵守党的纪律，首要的是严格遵守党的(A)纪律。 A.政治 B.组织 C.人事 D.经济 3.选拔任用党政领导干部，必须坚持(A)原则。 A.党管干部 B.效率优先 C.近亲回避 D.科学决策 4.反腐倡廉教育要以(C)为重点。 A人民群众 B特定群体 C领导干部 5.坚持教育、制度、监督并重，(A)。三者统一于惩治和预防腐败体系之中，相互作用，共
同发挥作用。 A.教育是基础，制度是保证，监督是关键 B.教育是保证，制度是基础，监督是关键 C.教育是关键，制度是基础，监督是保证 6.党风廉政建设的核心问题是(B)。 A.解决党同人民群众的鱼水关系 B.密切党同人民群众的血肉联系 C.反对 D全社会 7.党风廉政建设和反腐败工作必须围绕(C)，服务大局，研究解决妨碍改革发展稳定的突出问题，优化经济社会发展环境，促进社会主义物质文明、政治文明、精神文明协调发展。A中心 B实际 C发展 D创新 二、案例题 1.1月以来，广东省梅州市蕉岭县法院先后有8名干警因涉嫌滥用职权、收受贿赂等行为被检察机关立案侦查，其中包括院领导班子成员1人，正、副庭长7人。陈xx作为该院党组书记、院长，对上述问题的发生应当承担教育监管及查处不力的领导责任。目前，陈xx清已受到行政记过处分，相关部门拟依照有关程序提请免去其院长职务，并给予其相应的党纪处分。追究的是。(B) 两学一做知识竞赛试题含答案1 1、《党章》规定，党的建设的基本要求是。(ABCD) A、坚持党的基本路线 B、坚持解放思想，实事求是，与时俱进，求真务实
1.问：什么是学籍和学籍号？ 答：学籍是指学生作为某一学校学生的资格，符合学校入学条件的学生按时到校办理入学注册手续后取得该校学籍，学籍档案内容包括基础信息、证明材料、素质评价、体质健康、获奖资助等情况。学校应当从学生入学之日起一个月内为其建立学籍。 学籍号是学籍信息的核心要素，以学生居民身份证号为基础，从学生入学初次采集学籍信息后开始使用，基础教育、职业教育、高等教育、成人教育有机衔接。全国中小学学籍信息管理系统建立后，学生学籍号由教育部统一制订下发，是全国范围内学籍管理的唯一识别代码，与公安部的身份证系统对接，实行“一人一籍、籍随人走、终身不变”。 2.问：中小学生如何取得学籍？ 答：义务教育阶段：符合小学、初中入学条件的学生按规定时间到学校报到并办理入学注册手续，学校从学生入学之日起一个月内为其建立学籍。学籍注册时间为9月1日至30日，由学校采集新生信息，上报到全省学籍管理系统，系统根据姓名和身份证号从前一学段的毕业历史库中寻找可以匹配的学生，匹配成功的学生进入全省招生库，招生库中的学生经学校所属教育行政部门审核后，获得学籍。小学一年级入学儿童年龄必须是当年8月31日前（含8月31日）年满6周岁。 普通高中：每年8月31日前，各省辖市完成普通高中招生工作并将各学校新生录取库上报到全省电子学籍信息管理系统中，未按时进入录取库的无法注册普高学籍。9月10日至9月30日期间，招生录取学校在电子学籍信息管理系统中为录取该校（已入该校录取库）并按时报到的学生注册，经所属教育行政部门核准后取得该校学籍。 已录取的新生因故不能如期办理入学注册手续的，须在开学后一周内向学校申请延期办理，延期期限不超过两周。开学后一周内不到校办理入学注册手续，又不办理延期手续，或延期期限内仍不报到办理者，取消其入学资格。其他普通高中不能再录取，也无法为其注册学籍。任何学校不得接收未按规定办理手续的学生入学。 3.问：如何理解全面取消中小学借读的问题？ 答：从今年起全省中小学校全面取消借读，我省中小学学籍管理规定将原来需要借读的情形，统一作为准予转学的项目，符合转学条件、确有转学需求的，可准予办理转学。从今年起新入学的中小学生不再允许借读，因此建议每位初中毕业生在填报高中阶段学校志愿时，
两学一做知识竞赛题库及答案一 两学一做知识竞赛题库及答案一、单选题(共158题) 1.《党章》规定，中国共产党的宗旨是_ _。 (B) A.实现社会主义现代化B.全心全意为人民服务 C.坚持党的基本路线和基本纲领不动摇 D.坚持群众路线 2.《党章》规定，我们党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。_ _、党同人民群众联系问题是关系党生死存亡的问题。 (A) A.党风问题 B.信念问题 C.民主问题D.思想问题 3.《党章》规定，党坚持标本兼治、综合治理、惩防并举、注重预防的方针，建立健全惩治和预防腐败体系，坚持不懈地反对腐败，加强_ _建设和_ _建设。(D) A.思想民主B.党风思想 C.廉政思想 D.党风廉政 4.《党章》规定，中国共产党党员是_ _的有共产主义觉悟的先锋战士。
(A) A.中国工人阶级B.中国无产阶级C.中国先进阶级 D.中国劳动人民 5.《党章》规定,中国共产党党员永远是劳动人民的普通一员。除了法律和政策规定范围内的个人利益和工作职权以外，所有共产党员都不得谋求任何_ _。(A) A.私利和特权 B.政治权力 C.经济利益D.物质享受 6.《党章》规定，党员必须坚持_ _高于一切，个人利益服从党和人民的利益，吃苦在前，享受在后，克己奉公，多做贡献。(D) A.个人利益 B.局部利益 C.集体利益 D.党和人民的利益7.《党章》规定，党员必须切实开展批评和自我批评，勇于揭露和纠正工作中的缺点、错误，坚决_ _。 (B) A.同消极现象作斗争B.同消极腐败现象作斗争 C.同腐败现象作斗争D.同不良现象作斗争8.《党章》规定，党员必须密切联系群众，向群众宣传党的主张，遇事同群众商量，及时向党反映群众的意见和要求，维护_ _。(A) A.群众的正当利益 B.群众的全部利益
小学学籍管理制度范文4篇 学生学籍信息是学校的一项非常重要的数据资源，学籍信息的管理是教育机构管理工作不可缺少的一部分。学籍管理是高等学校教学管理的一个重要组成部分,加强学籍管理,对于提高教学与教育质量有着十分重要的意义。本文是为大家整理的小学学籍管理制度范文，仅供参考。 [小学学籍管理制度范文篇一:中小学学籍管理制度] 一、入学 1、新生入学，采取入学通知书制，并将入学通知书发给其父母或其监护人的手里，接到入学通知书后，应在规定时间内送子女或其被监护人到指定学校办理入学手续。 2、新生入学后，学校教导处应立刻批准取得学籍，编列正式学号。学生学号长期不变，休学一年后一律使用原学籍号。 3、新生入学后，学校要填写新生花名册，上报区教育局教育科审理。 二、转学与借读 1、学生因家长变动工作地区、家庭搬迁(户籍普更)或有其他特殊原因需要转学的，经学生家长(监护人)申请，学校审查后，按规定办理有关转学手续。 2、个别学生确有特殊情况要求借读的，须由家长或监护人向借
读学校申请，取得同意后，经区教育行政监管部门审核，条例条件并办理有关手续，方准予借读。学校建立学籍时，必须注明是借读，并记录其身份证号码和户口所在地。 3、借读生中途借读的，借读生的学籍由原校保留，学习成绩由借读学校按照有关管理规定考核评定;借读生离校时，借读学校须出具借度证明将学习成绩、在校表现、借度年限等情况转给原学校登记入册;毕业时由原学校发给义务教育证书。属从始起年级开始借读的，学籍和学习成绩由借读学校管理与考核，毕业时由借读学校发给义务教育证书。 4、毕业年级原则上不办理转学、借读手续;小学借读生毕业后，须回户口所在地升入初中。 三、休学、复学 1、有下列情况之一者，准予休学; (1)因伤病需长期治疗，不能坚持学习者; (2)患病染性疾病未愈，不适宜集体生活者; (3)学期内，请病、事假累计超过六周，跟班学习有困难者。 2、符合休学条件的学生(病休学生须持县或县以上医疗单位证明)，由学生家长提出申请，经学校审查属实后，报区教育局批准，准其休学，由学校发给休学证明书。 3、学生休学以一年为限，期满不能复学的，可申请延长。学生休学期间，学籍仍然保留。学生休学期满，应按时复学。复学时，应凭休学证明书和有关材料提出申请，经学校审核批准后，即可到校复
“两学一做”专题练习 一、单选题 1、开展“两学一做”，要针对不同群体党员实际情况，提出具体要求。那么，在窗口单位和服务行业，重点落实（C）。 A、党员设岗定责和承诺践诺制度 B、党员示岗和党员责任区制度 C、党员挂牌上岗、亮明身份制度 D、党员到社区报到、直接联系服务群众制度 2、充实和完善党的总纲，包括哪些方面：（ D ）。 A、对科学发展观的新定位 B、充实中国特色社会主义容 C、强调改革开放 D、以上都包括 3、中国共产党的根本宗旨是：（ C ）。
A、从严治党 B、全面建成小康社会 C、全心全意为人民服务 D、实现中国梦 4、新修订的《中国共产党纪律处分条例》是对（ B ）的具体化。 A、习总书记讲话精神 B、党章规定 C、十八大精神 D、十八届三中全会精神
5、党员领导干部某，因违纪受到留党察看二年处分。依据《中国共产党纪律处分条 例》有关规定，在留党察看处分期间，某有（ D ）。 A、表决权 B、选举权 C、被选举权 D、申诉权 6、《中国共产党廉洁自律准则》规定，中国共产党全体党员和各级党员领导干部必须 坚定共产主义理想和（ B ）信念。 A、马克思主义 B、中国特色社会主义 C、全心全意为人民服务 D、中国特色社会主义道路 7、《党章》规定，对党员的纪律处分，必须经过（ A ）讨论决定，报党的基层委员会批准。 A、支部大会
B、委员会会议 C、总支部委员会会议 D、以上都不是 8、党的十八届三中全会对巡视工作作出重要部署，要求做到对（C）全覆盖。 A、地方、城镇，企事业单位 B、地方、企事业单位、乡镇 C、地方、部门、企事业单位 D、地方、乡镇、企事业单位 9、“全面从严治党”的两个层面分别是：D
小学学籍管理制度「范本」 篇一：小学学籍管理制度 红色边疆农场学校小学学籍管理制度 为了全面贯彻党的教育方针，保证学校的正常教学秩序，根据《中华人民共和国教育法》、《中华人民共和国义务教育法》和《中华人民共和国未成年人保护法》的有关规定，结合我校实施义务教育的实际情况，特制定下列学籍管理办法： 一、入学、编班 1、一年级新生的入学、编班。 （1）审核入学资格。凡属我场招生范围的学龄儿童，由校领导把关，按有关部门的规定，依户口本和接种证严格审查入学资格。 （2）新学期开始，凡被我校录取的新生，必须按规定时间到校进行注册。如因故不能按时报到的由家长持有关证明，向学校申请，经学校领导批准后可延期注册。 2、新生入学后，教导处在一个月内填写“学籍卡片”“学号”，并编制花名册，报送教育局，建立学籍档案。 3、在校学生，每学期开学必须按学校规定的日期报到注册，因故不能按期报到者，应由家长持有关证明到学校请假。否则按旷课处理。 二、转学、借读 1、凡从外地随家长迁入我场的学生，必须持转学证明向学校提出申请，依班额情况，经校长批准，方可办理入学手续。 2、非本场来我校就读，经教导处考核合格，可插入合适年级并办理外地借读手续。 3、凡在校学生随父母迁居外地，要求转学的应由家长到学校申请，经教导处批准后方可办理转学手续，转学后将学生“学籍卡片”学号到教育局消除。 4、转进、转出的学生都要进行登记，并装入档案。 三、休学、复学 1、学生因生病或其他原因不能坚持学习时，可持医院证明，家长签字的休学申请书，向学校申请休学，经班主任审核，校长批准后，办理休学手续，休学期限
为一年。 2、学生休学期满或病愈后持休学证，医院证明回校办理复学手续，复学时按学生实际程度编班。 3、休学期满后仍不能复学者，应在休学期满前半个月内办理延期休学手续，经校领导批准后续休学。 4、学生休学期间申请提前复学者，经校领导批准，按其具体情况编班。 四、档案管理制度 （一）档案包括的内容 1、学生花名册； 2、体检表； 3、体育达标成绩； 4、学生成绩册； 5、毕业生登记册； 6、转学登记册； 7、优秀学生证明材料； 8、教师业务档案，学生学籍档案； 9、适龄学生花名册； 10、学龄前儿童花名册； 11、转入、转出学生花名册； （二）、档案管理的规定 1、除健康卡由校医负责管理外，其它有关材料一律由教导处管理。 2、档案管理是一项政治性很强的工作，应严肃对待，不得草率马虎，凡有关学生档案登记的各种表格一律按统一规定填写，做到整齐准确，统一规范。 3、凡转入学生必须向学校索取转学证；凡转出学生要开出转学证，并分别登记归档。 篇二：小学电子学籍管理制度 忠信小学电子学籍管理制度 第一条为了全面贯彻党的教育方针，加强中小学学生学籍规范化管理，推进教育管理的信息化和现代化，特制定本规定。
1、在选举产生党的各级代表大会的代表和委员会委员时，下列表述中，正确的是（ ）。 A.只能采取差额选举的办法 B.必须先采取差额选举办法进行预选，产生候选人名单，然后进行 正式选举 C.可以直接采用候选人数多于应选人数的差额选举办法进行正式选 举；也可以先采用差额选举办法进行预选，产生候选人名单，然后进行正式选举 2、党的纪律处分有（ ）。正确答案是 A.警告、严重警告、撤销党内职务、留党察看、开除党籍 B.警告、撤销党内外职务、留党察看、取消党籍、开除党籍 C.警告、严重警告、撤职查办、留党察看、开除党籍 3、《中国共产党纪律处分条例》规定，二人以上（含二人）共同故意违纪的，对为首者，除本条例分则中另有规定的外，从重处分；对其他成员，按照其在共同违纪中（ ），分别给予党纪处分。 A.情节的轻重 B.职务的高低 C.所起的作用和应负的责任 4、党和国家机关违反有关规定经商办企业的，对直接责任者和领导责任者，给予警告或者严重警告处分；情节严重的，给予（ ）处分。 A.警告 B.严重警告 C.撤销党内职务 5、党员的义务之一是党员要在生产、工作、学习和（ ）中起模范带头作用。 A.日常生活 B.党的生活
C.社会生活 6、组织、参加旨在反对党的领导、反对社会主义制度或者敌视政府等组织的，对策划者、组织者和骨干分子，给予（ ）处分。 A.留党察看 B.开除党籍 C.撤销党内职务 7、根据《中国共产党纪律处分条例》规定，党的各级代表大会的代表受到（ ）处分的，党组织应当终止其代表资格。 A.严重警告（含）以上 B.留党察看（含）以上 C.撤销党内职务（含）以上 8、建设社会主义文化强国关键是（ ）。 A.建设社会主义核心价值体系 B.发展文化产业、繁荣社会主义文化 C.增强全民族文化创造活力 9、我们推进任何一项重大改革，都要站在人民立场上把握和处理好改革的重大问题，都要从（ ）出发谋划改革思路、制定改革举措。 A.人民利益 B.实际情况 C.基本国情 10、党的基层组织要宣传和执行党的路线、方针、政策，宣传和执行党中央、上级组织和本组织的决议，充分发挥党员的先锋模范作用，积极（ ），团结、组织党内外的干部和群众，努力完成本单位所担负的任务。 A.创先争优 B.学习理论 C.组织活动
全国大学生党员“两学一做”专题网络培训试卷 您的分数为100.0分！ 单项选择题 | 本大题共有20小题，总分为50.0分 题目： 中国共产党要领导全国各族人民实现社会主义现代化的宏伟目标，必须紧密围绕党的基本路线，加强党的执政能力建设、____建设。 选项： A 先进性 B 纯洁性 C 先进性和纯洁性 分值： 2.5分 题目：
党章总纲指出：马克思列宁主义揭示了____，它的基本原理是正确的，具有强大的生命力。 选项： A 共产党执政规律 B 社会主义建设规律 C 人类社会历史发展规律 分值： 2.5分 题目： 党的全国代表大会的职权是：（一）听取和审查中央委员会的报告；（二）听取和审查中央纪律检查委员会的报告；（三）讨论并决定党的重大问题；（四）____；（五）选举中央委员会；（六）选举中央纪律检查委员会。 选项： A 修改党的章程
B 解释党的章程 C 选举中央政治局 分值： 2.5分 题目： 下列表述中，正确的是____。 选项： A 党员在留党察看期间没有表决权，但有选举权和被选举权 B 党员在留党察看期间有表决权，但没有选举权和被选举权 C 党员在留党察看期间没有表决权、选举权和被选举权 分值： 2.5分
题目： 入党誓词如下：我志愿加入中国共产党，拥护党的纲领，____，履行党员义务，执行党的决定，严守党的纪律，保守党的秘密，对党忠诚，积极工作，为共产主义奋斗终身，随时准备为党和人民牺牲一切，永不叛党。 选项： A 遵守党的章程 B 遵守国家的法律 C 遵守党的纪律 分值： 2.5分 题目： 党员如果没有正当理由，连续____不参加党的组织生活，或不交纳党费，或不做党所分配的工作，就被认为是自行脱党。 选项：