Influence in the Evolving Citation Network of the Journal of Consumer Research

Diane M. Phillips, Saint Joseph’s University
Hans Baumgartner, The Pennsylvania State University
Rik Pieters, Tilburg University
ABSTRACT - We report a bibliometric study of 27 journals with which the Journal of Consumer Research (JCR) has had significant communication links over the 12-year period 1982 to 1993. Two issues that have not been considered in previous citation studies in marketing are addressed: how influential are journals, and how does influence evolve over time? The analyses indicate that a small set of marketing and psychology journals wields a disproportionate amount of influence and that the influence of marketing journals is almost entirely confined to the marketing field. Longitudinal analyses show substantial stability in the network over the 12-year period, although JCR has clearly increased in influence.
[ to cite ]:
Diane M. Phillips, Hans Baumgartner, and Rik Pieters (1999) ,"Influence in the Evolving Citation Network of the Journal of Consumer Research", in NA - Advances in Consumer Research Volume 26, eds. Eric J. Arnould and Linda M. Scott, Provo, UT : Association for Consumer Research, Pages: 203-210.

Advances in Consumer Research Volume 26, 1999      Pages 203-210

INFLUENCE IN THE EVOLVING CITATION NETWORK OF THE JOURNAL OF CONSUMER RESEARCH

Diane M. Phillips, Saint Joseph’s University

Hans Baumgartner, The Pennsylvania State University

Rik Pieters, Tilburg University

ABSTRACT -

We report a bibliometric study of 27 journals with which the Journal of Consumer Research (JCR) has had significant communication links over the 12-year period 1982 to 1993. Two issues that have not been considered in previous citation studies in marketing are addressed: how influential are journals, and how does influence evolve over time? The analyses indicate that a small set of marketing and psychology journals wields a disproportionate amount of influence and that the influence of marketing journals is almost entirely confined to the marketing field. Longitudinal analyses show substantial stability in the network over the 12-year period, although JCR has clearly increased in influence.

There has been a great deal of interest in bibliometric studies of scholarly communication in recent years (Borgman 1990), and the burgeoning of research into patterns of scientific communication has also resulted in a growing marketing literature on this topic (e.g., Anderson and Haley 1984; Cote, Leong and Cote 1991; Everett and Pecotich 1991; Goldman 1979; Hamelman and Mazze 1973; Hoffman and Holbrook 1993; Jobber and Simpson 1988; Long 1989; Muehling, Cote, Umesh, and Weeks 1987; Zinkhan, Roth, and Saxton 1992; Zinkhan, Saxton, Roth and Zaltman 1990). The fascination with analyzing flows of information within and between disciplines seems to be based, at least in part, on a desire to learn about the intellectual foundations of an academic field of study, to gain insights into the structure of knowledge generated by a scientific community, and to assess the impact of scholarly contributions on other areas of inquiry.

As discussed by Borgman (1990), bibliometric studies of scholarly communication can be classified according to variables studied and research questions asked. Variables studied can be communication producers (e.g., authors, institutions, countries), communication artifacts (e.g., articles, books, journals), and communication concepts (e.g., ideas, themes, motivations behind citations). Research questions commonly asked deal with the composition of a scholarly community, the evolution of a communication network, evaluation of the importance of scholarly contributions, and the diffusion of ideas within and across disciplines. Research in marketing has studied both producers and artifacts of communication, and it has sought to provide insights into structural aspects of the scholarly community, the development of patterns of information exchange over time, and the distribution of influence in the communication network. Still, some shortcomings of prior work can be identified.

First, although bibliometric analyses often explicitly aim to provide evidence on influence in social networks (e.g., Cote, Leong, and Cote 1991), analyses of the influence of scholarly contributions have relied upon rather simple measures of impact (for an exception see Everett and Pecotich 1991). Most commonly, appraisals of influence are based on the frequency with which authors, universities, articles, journals, or disciplines are cited, or the ratio of citations received to citations sent (e.g., Cote, Leong, and Cote 1991; Goldman 1979; Leong 1989; Zinkhan, Roth, and Saxton 1993; Zinkhan, Saxton, Roth, and Zaltman 1990). The impact factors calculated for each journal listed by the Social Sciences Citation Index (SSCI) are based on a similar logic. As pointed out by Salancik (1986) and explained in more detail below, influence measures based on raw citation frequencies are problematic because they ignore indirect dependencies and they treat all sources of citations as equally important.

Second, studies investigating the evolution of scholarly communication in marketing are relatively rare. Those that do track developments over time are limited in scope because they cover relatively short time periods (e.g., 1981-1987 in Everett and Pecotich 1991), are based on periodic assessments of information exchanges (e.g., analysis of volumes 1, 4, 7, 11, and 14 of JCR in Leong 1989), and/or rely on informal comparisons of citation patterns over the years (e.g., Zinkhan, Roth, and Saxton 1992). Hoffman and Holbrook (1993) have recently urged researchers to more explicitly take into account the time dimension in citation studies in order to investigate the dynamic aspects of communication networks.

This paper addresses these shortcomings of prior bibliometric research in marketing. Specifically, we will use a procedure suggested by Salancik (1986), which takes into account both direct and indirect dependencies and considers the importance of the sender of the communication, to assess relative influence in the network. We will apply the procedure to the citation network of JCR and assess the influence of journals in this network over a 12-year period. We will examine the influence of journals in the citation network of JCR as a whole, as well as the influence of journals in specific sub-areas of the network. We will show that the proposed procedure is better suited to the analysis of citation data and leads to insights that cannot be obtained from traditional bibliometric approaches reported in the marketing literature.

INFLUENCE IN COMMUNICATION NETWORKS

An important goal of citation analysis is usually to assess the influence, impact, or importance of producers and artifacts of scholarly communication. Often, influence is determined as a function of the number of citations a member of a citation network receives from other members. For example, the Social Sciences Citation Index (SSCI) reports a measure of journal influence called the impact factor which is calculated as the number of citations received by a journal divided by the number of articles published in the journal during a two-year period. The impact factor thus indicates the average number of citations per article for a given journal. Using this index, Cote, Leong and Cote (1991) found, for example, that the average rate of citations for articles appearing in JCR between 1974 and 1986 was 13.3.

From a social exchange perspective, Zinkhan, Roth and Saxton (1992) argue that influence is a function of both the number of citations received and the number of citations sent. The rationale for this approach is that if a journal is cited frequently but does not cite other journals as much, it is more influential than when it sends and receives citations in equal amounts. Zinkhan, Roth and Saxton (1992) calculate a sending/receiving ratio, where values between 0 and 1 indicate net receiver status and values above 1 signal net sender status. Their analysis revealed that the sending/receiving ratios for JCR vis-a-vis other marketing journals were close to one in most years, indicating that knowledge diffusion tended to reflect reciprocal influence. From a similar perspective, Hoffman and Holbrook (1993) reason that authors in a citation network are more influential to the extent that they are cited more than they cite others.

There are several problems with these measures of influence. One problem is that raw citation counts do not take into account the fact that authors and journals differ widely in how frequently they cite other authors and journals. This problem can be circumvented fairly easily by converting citations into dependencies, which are obtained by dividing journal i’s citations to journal j by the total number of citations made by journal i during a given time period. Dependencies thus refer to the proportion of a journal’s total citations sent to other journals. Two other, more serious problems are that raw citation counts ignore indirect dependencies and that all citations are treated as equally important (Salancik 1986).

Indirect dependencies arise from the fact that some journals may not exert their influence on other journals directly but through journals that mediate their influence. To illustrate this point, assume that A is an important source of information for B and that B is an important source of information for C. Assume further that C does not cite A directly. If only direct citations were considered, we would have to conclude that A has no influence on C. However, intuitively this does not reflect the actual dependence of C on A because presumably C indirectly benefited from the work of A as reflected in B. Ignoring indirect dependencies thus leads to an underestimate of some members’ influence in the network.

Citation inequality refers to the notion that network members tend to differ greatly in how important they are to the network and that citations from relatively important or unimportant sources should not be treated equally. In calculating measures of member influence, citations have to be aggregated across members and this is generally done by simply summing linkages. However, this practice of weighting citations equally is inconsistent with the usual outcome that network members differ in importance, and it can lead to either over- or underestimating members’ influence.

Salancik (1986) suggests an input-output approach to structural influence that solves the problem of indirect dependencies and citation inequality by expressing the structural influence or importance of each member in thenetwork as a weighted function of other members’ dependencies on that member, where the weights are the importances of the members in the network. In matrix form the solution to the problem can be expressed as:

INF=[ I B D ] -1 INT   (1)

where INF is an N x 1 vector of overall influence scores for the N journals in the network, I is an N x N identity matrix, D is an N x N dependency matrix, and INT is a vector containing the intrinsic importances of each member. The latter are usually assumed to be equal and arbitrarily set to one. This means that in the absence of structural influence, a journal’s importance equals 1, its intrinsic importance. The dependency matrix is the transpose of the row-normalized raw citation matrix, as described previously. Since the interest is in deriving a measure of structural influence, which is determined by the dependence of a journal on other journals, the dependency matrix D is defined such that the diagonal entries are all equal to zero.

If a journal communication network is composed of several distinct subgroups of journals, measures of journals’ overall influence might be of less interest than indices of subgroup influence, which provide a picture of a journal’s importance in a particular discipline or area of interest. Within a larger citation network of journals, subgroups can often be distinguished on a priori grounds, for example on the basis of conventional academic classifications (economics, psychology, sociology, marketing, etc.) or other common distinctions (macro-micro, conceptual-methodological, basic-applied, etc.). If there is no particular rationale for partitioning a citation network on a priori grounds, an a posteriori approach (such as positional analysis) can also be used (Scott 1991). Within-discipline or within-area influence scores make it possible to distinguish journals that exert their influence in a single discipline or area from journals that are influential across disciplines or areas. Salancik (1986) extends his analysis of overall structural influence by considering subgroup influence as follows:

SINF=[ I B D ] -1 D M   (2)

where SINF is an N x K matrix of subarea influence scores (K being the number of groups), D is as defined previously, and M is an N x K matrix of zeros and ones representing a journal’s membership in one of the K groups. This formula partitions total network influence into an additive combination of structural influence in each subgroup.

Recently, Johnson and Podsakoff (1994) performed a comprehensive analysis of journal influence in management. They found strong evidence for the construct validity of Salancik’s influence measure, as it was generally highly correlated with other measures of a journal’s influenceCboth subjective measures (such as expert judgments of journal influence) and objective measures (various measures based on citation counts). Both on lgical and empirical grounds, we chose to use Salancik’s measure as the most appropriate index of structural influence in our study.

To summarize, in what follows we analyze the structural influence of members of JCR’s citation network based on direct and indirect weighted dependencies and we will derive influence measures for the network as a whole as well as sub-areas of the network. Finally, we will examine the stability of influence in the network over time.

METHOD

To examine influence of journals in the evolving citation network of the Journal of Consumer Research (JCR), we performed a longitudinal analysis of citation data for four periods of three years each: 1982 to 1984 (period 1), 1985-1987 (period 2), 1988-1990 (period 3), and 1991-1993 (period 4). We decided to start the analysis in 1982 because preliminary analyses showed that most citations were to journal articles published within the last decade or so, and a time lag of nine years (JCR was first published in 1974) was deemed sufficient to avoid the irregularities that might occur during a journal’s initial years of publication. Annual data were aggregated into three-year periods in order to smooth short-term fluctuations in citation patterns while maintaining the integrity of the citation links. Aggregation also allowed for a tractable and parsimonious longitudinal analysis. The data were collected from the Journal Citation Reports of the Social Sciences Citation Index (SSCI) for the years 1982 through 1993 (Garfield 1982-1993).

To identify the journals that were within JCR’s citation network, we compiled raw frequency counts for all journals that cited, or were cited by, JCR over the entire 12-year time span. We then selected the 30 most active journals based on the sum of sending and receiving relations with JCR across all 12 years. We eliminated any publications that were not bona fide journals (e.g., Annual Review of Psychology), although we did include the Proceedings of the Association for Consumer Research (ACR) because of their relevance to JCR. We were unable to include all 30 journals that had active communication exchanges with JCR because SSCI does not report data for all journals (e.g., no data were available for the Journal of Public Policy and Marketing). However, no journals with which JCR had substantial citation links are missing from the network. Network membership changed over the time period studied as journals entered or exited the network (e.g., Marketing Science was only recently included in the Journal Citation Reports) or had missing data for certain time periods. We did, however, include all journals for which we had complete data for at least one time period. The journals used in the analysis and the periods during which they are included are listed in Table 1.

TABLE 1

JOURNALS USED IN THE ANALYSIS DURING AT LEAST ONE TIME PERIOD

The raw data compiled from the SSCI were combined to construct citation matrices containing the absolute frequencies with which each journal in the network cited the other journals during a given three-year period. This procedure resulting in four separate directional, valued N x N matrices, where N varies across different time periods. As mentioned previously, journals differ widely in how many citations they make to other journals (due to different journal sizes and number of references per article), and the raw citation patterns are thus not directly comparable across journals. For the analysis of influence, the dependency matrices were calculated from the raw citation matrices by dividing the number of citations made to a specific journal by the total number of citations made by the citing journal (cf. Salancik 1986). All analyses were performed using UCINET IV (Borgatti, Everett, and Freeman 1992).

RESULTS

The overall influence scores based on equation (1) are shown in Table 2. For each time period, the journals are rank-ordered by importance in order to simplify comparisons across journals and over time. Bar charts of the overall influence scores are shown on the left-hand side of Figure 1. JMR and JPSP were the two most important journals in JCR’s citation network during each of the four three-year periods. PB, PR, and JM ranked in the top five in period 1, and JCR, JM, and PR belonged to the top five during the last three time periods. JCR improved its influence ranking from seventh to fifth between periods 1 and 2 and from fifth to third between periods 2 and 3, and remained in third place thereafter. The left-hand bar chart in Figure 1 shows even more clearly that JCR’s overall influence has improved considerably over the years and that during the final period JMR, JPSP, and JCR, and to a lesser extent JM, PR, and PB, were the dominant journals in JCR’s citation network. Except for the steady improvement in JCR’s influence, few dramatic changes in journal importance have occurred over the years. This conclusion is confirmed by correlations of influence scores between adjacent periods of at least .97; the lowest correlation between periods 1 and 4 is still .89. Thus, the distribution of influence in JCR’s citation network has been rather stable over the years.

TABLE 2

OVERALL INFLUENCE SCORE AND RANK OF EACH JOURNAL BY TIME PERIOD

FIGURE 1

BAR CHART OF OVERALL INFLUENCE SCORES AND SUBAREA INFLUENCE SCORES BY TIME PERIOD

TABLE 3

SUBAREA INFLUENCE SCORES BY TIME PERIOD

We also computed subarea influence scores using equation (2) and these are exhibited in Table 3. Since we were primarily interested in the distribution of influence within marketing, only two subareas were considered: marketing journals and other journals. The journals that were classified as marketing journals (in a broad sense) are underlined in Table 3 and ranks are shown in parentheses. Bar charts of influence scores within marketing are shown on the right-hand side of Figure 3. JMR is the most influential journal in the marketing area in each time period followed by JM or JCR. JPSP is usually the next most influential journal, and JAP, ACR, and JAR also belong to the top five in at least one time period. PB and PR, which ranked highly in the overall influence analysis, are much less important in the marketing area. While JMR was clearly the dominant marketing journal during the earlier periods, JCR has steadily gained ground and during period 4 was almost as influential as JMR. The right-hand bar chart in Figure 3 clearly shows that the distribution of influence is rather concentrated and that JMR, JCR, and JM, and to some extent JPSP and ACR, are by far the most important journals in JCR’s marketing citation network.

Correlations between the marketing and non-marketing influence scores show that the distribution of influence in marketing is completely unrelated to importance ratings outside of marketing (essentially psychology and to some extent sociology). The three most important psychology journals are JPSP, PR, and PB in each time period. No marketing journal ranks among the top ten non-marketing journals in any of the time periods, and in absolute terms the influence of marketing journals on psychology has been minuscule.

Because the sub-area influence scores add up to a journal’s total structural influence in the network (the values reported in Table 1 minus intrinsic importance, which is assumed to be one), one can calculate a relative influence index by dividing a journal’s marketing influence by its total influence minus one. If this index is greater than 50 percent, a journal exerts most of its influence in marketing. The results show that the vast majority of marketing journals’ overall influence is concentrated in marketing (generally greater than 95 percent). In contrast, journals such as JAP, PM, and OBHDP exert about an equal amount of influence on marketing and non-marketing journals, and most non-marketing journals influence marketing journals at least to some extent (generally at least 10 percent of their total influence is in marketing). Although the finding that non-marketing journals influence marketing journals at least to some degree is partly due to the way the citation network was constructed (journals were selected based on whether they had ignificant sending or receiving relationships with JCR), it is striking that marketing journals apparently influence no other journals except other marketing journals.

As in the overall influence analysis, the sub-area influence scores are extremely stable over time. For non-marketing journals all inter-temporal correlations exceed .98, and for marketing the smallest correlation is .89. Thus, while a few journals may demonstrate increases or decreases in influence over time, it appears that overall shifts in sub-area importance occur very slowly, if at all.

DISCUSSION

In this paper we have considered two important issues in bibliometric studies of scholarly communication that have not been addressed satisfactorily in prior work in marketing: how is structural influence distributed over the members of the network, and how does influence evolve over time? The importance of network members was considered based on an index of structural influence suggested by Salancik (1986). This index takes into account indirect dependencies and citation inequalities, and it can be used to assess subgroup influence in more narrowly defined areas of the overall network. We found that the journals that are typically regarded as the premier marketing journals (JCR, JM, JMR) are among the most influential members in JCR’s citation network, but that several general/social psychology journals also exert a significant amount of influence on the network. When total influence was partitioned into subgroup influence in the marketing and non-marketing areas, it became evident that the influence of the marketing journals is almost entirely confined to the field of marketing and that influence is relatively segregated in the two areas. With the exception of a few journals such as JAP, OBHDP, and PM, which tend to spread their (relatively modest) influence in equal measure on marketing and non-marketing journals, most journals have most of their influence in one area. Still, a few psychology journals are quite important in the marketing area, especially JPSP, and to a lesser degree PR and PB. The sub-area influence analysis also showed that the distribution of influence within marketing is rather concentrated and that JMR, JCR, and JM completely dominate the field. Both overall and subgroup influence scores were very stable across the four time periods studied, although there was solid evidence that the importance of JCR has increased steadily over the years, to a point where it now competes head-on with JMR for hegemony in marketing. It should be noted, however, that this conclusion is dependent on the set of journals included in the analysis and that journals were specifically selected based on their citation links with JCR.

Interestingly, the influence of the Advances in Consumer Research (ACR) in the two periods that it was sampled (1985-1987 and 1991-1993) was substantially higher than that of established business and marketing journals such as JA, JAR, JBR, JMRS. This underlines the importance that conference proceedings may have in the dissemination of knowledge, and the particular importance of ACR in marketing.

Probably the most surprising findings of the present analysis is the extent to which journal influence in marketing is concentrated. Marketing journals mostly communicate with other marketing journals, and marketing journals are not a significant source of information for journals in other fields (particularly psychology and sociology). Although JCR bills itself as an interdisciplinary journal, our findings provide little evidence to support this claim. Even within the narrower domain of marketing (or marketing-related) journals with which JCR had significant communication links during the 12-year period 1982 to 1993, only three journals (JMR, JCR, and JM) wield a significant amount of influence and other marketing journals play a relatively minor role in this network.

The structural influence results can be ontrasted with the most commonly used measure of a journal’s importance, SSCI’s impact factor (calculated as the number of citations received by a journal divided by the number of articles published in the journal during a two-year period). We collected impact factors for each journal for which we had complete data during a given time period, averaged the yearly impact factors to obtain impact factors for each three-year time period, and then correlated these averaged scores with the overall, marketing and non-marketing influence scores. The correlations were .37, .41, .49, and .38 for overall, -.05, -.01, .09, and .02 for marketing, and .62, .67, .74, and .69 for non-marketing influence scores. These results suggest that impact factors lead to a somewhat idiosyncratic assessment of journal influence and that they may not provide a useful indication of the importance of journals in more narrowly defined areas of research such as marketing and consumer research. Johnson and Podsakoff (1994) obtained similar results in an analysis of management journals. These authors also showed that the Salancik measure was significantly correlated with other measures of journal impact (such as expert judgments), while the SSCI impact factor was not. It should be acknowledged that both the impact factors and the Salancik measure indicated that JMR, JCR, and JM were the most influential marketing journals in the network and, as indicated previously, these journals are by far the most important members of JCR’s citation network. Our results indicate that although the SSCI’s impact factor converges with Salancik’s more valid measure for the most influential journals, it appears to undervalue the influence of less important journals.

These results are important from a practical point of view as well. As increasing numbers of departments and schools base their tenure and salary increase decisions on publication records, it is important to have the right measures of the importance of the journals in which faculty members choose to publish their research. As the competition for journal space increases, increasing numbers of academic artifacts will be published in non-first tier journals. The authors of such artifacts will benefit if the influence of the journals they publish in is determined appropriately, i.e., not with the SSCI impact factor.

Our citation analysis covered the period from 1982 to 1993. In recent years an important stream of studies in marketing has relied on disciplines other than psychology and economics, the traditional sources of theories and methods. Increasing academic diversity stimulates the influx of ideas, theories and findings from sociology, anthropology, literature, and history to name a few. Future research may examine which journals from these disciplines exert and will exert an influence in marketing, and whether marketing journals in return will be able to exert an influence on those disciplines. We hope that the influence of marketing on the disciplines from which it draws is higher than the influence it has had on psychology and economics, from which it has drawn traditionally.

In conclusion, we have shown that the measure of structural influence proposed by Salancik (1986) offers an appealing solution to the problem of assessing the importance of journals in networks in which there are indirect dependencies and citation inequalities. The present study adds new insights about the influence of JCR in its citation network and contributes to the growing literature about networks in marketing (Iacobucci 1996). An interesting avenue for future research may be to study the communication links between a broader cross-section of marketing journals and possibly other producers and artifacts of marketing, and to examine the specific roles that journals play in disseminating scientific knowledge across the network as whole.

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