Citation-Based Journal Rankings for AI Research A Business Perspective ■ A significant and growing area of business-com- puting research is concerned with AI. Knowledge about which journals are the most influential fo- rums for disseminating AI research is important for business school faculty, students, administra- tors, and librarians. To date, there has been only one study attempting to rank AI journals from a business-computing perspective. It used a subjec- tive methodology, surveying opinions of business faculty about a prespecified list of 30 journals. Here, we report the results of a more objective study. We conducted a citation analysis covering a time period of 5 years to compile 15,600 cita- tions to 1,244 different journals. Based on these data, the journals are ranked in two ways involv- ing the magnitude and the duration of scientific impact each has had in the field of AI. A I research has been striving for the past four decades to increase the intelligence displayed by computing systems. To- day, there are many distinct subfields within AI—natural language processing, speech recognition and synthesis, pattern recognition and computer vision, robotics, knowledge rep- resentation, machine learning, fuzzy logic, and expert systems, to mention a few. Each at- tempts to automate specific aspects of human intelligence, and each is relevant to business research and practice. This relevance cuts across several business fields but is particularly pronounced for the field of business-comput- ing systems, which has a growing intersection with the AI field. Nearly 400 faculty listed in the 1992 MISRC/McGraw-Hill directory iden- tify an AI area as a research specialization (De- Gross, Davis, and Littlefield 1992). Courses dealing with AI topics have become common- place in business school curricula. A recent special issue of Communications of the ACM on commercial and industrial appli- cations of AI provides a timely depiction of the dramatic effects of AI research in business computing (CACM 1994). AI business applica- tions today span the realms of manufacturing, finance, and management, employing such technologies as knowledge-based systems, vi- sion systems, automatic speech recognition, microelectromechanical systems, fuzzy logic, neural networks, and genetic or evolutionary algorithms. Consumer products are now mea- sured in machine intelligence quotients. Companies such as Digital, IBM, and DuPont have ongoing efforts in developing AI applica- tions to solve complex real-world problems, improve productivity, and achieve strategic competitiveness. Many regard AI as the next wave in the ongoing computing revolution (Dutta 1993). To stay at the forefront of this revolution, a guide to the latest and most influential scien- tific developments in AI is critical. The pur- pose of this article is to offer such a guide for researchers and practitioners who operate on the cusp of the AI and business-computing fields. We do so by developing objective rank- ings of journals that have the greatest impact on AI research. The rankings are based on an extensive citation analysis. Because the cita- tion base is determined from a survey of busi- ness school faculty about the quality of AI journals, the rankings have a definite business orientation. The results reported here are of practical interest to business-computing re- searchers contemplating where to submit their own AI research. They are of interest to both faculty and students who need to allo- cate their limited time to reading among a Articles SUMMER 1996 87 Citation-Based Journal Rankings for AI Research A Business Perspective Chun Hung Cheng, Clyde W. Holsapple, and Anita Lee Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00 for each of the 30 journals. This recognition factor is the percentage of the 111 respon- dents who were sufficiently familiar with the journal to rate it on the 1 to 4 scale. These recognition factors ranged from 89 to 20. She argued that journals of relatively recent vin- tage generally have had less time to be recog- nized. Accordingly, she developed two addi- tional rankings. The first ranking had the 12 journals with recognition factors above 50 percent based on their ranked weighted-aver- age scores. The second ranking listed the re- maining journals on the same basis. The in- tent of this dual-ranking approach was to overcome bias introduced by the age differ- ences of the journals. Gupta’s study is a pioneering effort at as- sessing the quality and impact of various jour- nals on AI research from a business perspec- tive. However, it has some notable limitations. First, respondents were given the task of rating a prespecified list of 30 journals. Are these the 30 most influential journals for AI research, or are important journals omit- ted? Second, the study is strictly subjective. The opinions of business school faculty are, of course, important. However, do they accurate- ly reflect the actual relative influences of AI journals? Third, the longevity of a journal might well impact the recognition it garners. However, is a 50-percent recognition factor an appropriate cutoff for partitioning journals in- to two rankings, and might not a single rank- ing that is normalized to adjust for longevity be more useful? The research reported in this article addresses all three of these concerns. One other related study produced rankings of journals based on their relative impacts on expert system research (Cheng, Holsapple, and Lee 1995). Although this study was objective, host of journals. They are of interest to uni- versity administrators who need an objective way to gauge the AI outlets in which their fac- ulty members publish. They are of interest to business school librarians who need a way to assess what AI journals are most important to include in their collections. We begin with a brief review of studies re- lated to ranking journals that publish AI re- search. Next, details of the citation-analysis methodology are described. Findings from this analysis are then presented. Results based on both unnormalized and normalized cita- tion scores are reported. They are compared with a previously reported subjective ranking, showing that our objective rankings yield some major differences from the earlier work. A concluding discussion accentuates insights gained from this study. Related Studies The ranking of journals has long been under- taken as a means to gauge journal quality and influence (Garfield 1979). Our literature review reveals only one prior attempt to rank AI journals. In 1992, Gupta (1994) surveyed the opinions of 111 AACSB accredited busi- ness faculty about the academic quality and reputation of 30 journals she identified as publishing AI research. Each respondent rat- ed each of these journals on a scale of 1 (low quality) to 4 (top quality). Journals were then ranked according to a weighted-average score derived from the respondents’ ratings. Gupta grouped the ranked journals into three cate- gories of roughly comparable size that she called top, medium, and low, reflecting the weighted-average scores. Gupta also reported a recognition factor Articles 88 AI MAGAZINE Field of Interest Basis of Analysis Purpose AI References made to papers published in Artificial Intelligence from 1970–1991 To identify 50 most influential papers in AI (Bobrow 1993) Business computing Over 25,000 citations from 5 base journals covering a time period from 1987–1991 To rank business-computing journals (Holsapple et al. 1993) Decision support systems (DSS) Publishing records in DSS- related areas from 32 U.S. institutions examined To identify the most-influential contributors and the leading U.S. universities in DSS-related research (Eom and Lee 1993) Management information systems (MIS) References from an MIS literature-review article in 1988 To identify a core of MIS journals (Cooper, Blair, and Pao 1993) Table 1. Citation Studies Published in 1993 for AI and Business-Computing Research. expert systems form only one segment of the AI field. Thus, its results are of interest to those focusing on expert systems, but they could be expected to differ from the broader AI study reported here—and indeed they do. Citation Analysis In the interest of objectivity, our ranking is es- tablished through an extensive study of cita- tion patterns existing in a base set of AI arti- cles. This methodology is known as citation analysis, the merits of which are put forth by Cooper, Blair, and Pao (1993): Citation analysis is an unobtrusive way to judge the influence of research within the research community. Such analyses do not require cooperation of respon- dents and thus are not prone to many of the biases associated with eliciting re- searcher perceptions and the noise which can be introduced due to multiple per- ceptions of influence criteria. Reported studies in 1993 using citation analy- sis for various purposes in AI and business fields are summarized in table 1. Regardless of how citation analysis is ad- ministered, the identification of a base set of articles related to the subject under study is crucial. It is important that the base set of arti- cles be representative of the best work in the subject area and that the inclusion-exclusion of articles be immune from researcher judg- ments or bias. In this study, the base set of ar- ticles is collected from six AI journals, cover- ing the period from 1989 to 1993. In assembling this citation base, we exercised no judgment in choosing the specific AI journals or selecting specific articles from them. The journals were effectively chosen by the busi- ness faculty responding to Gupta’s survey. All articles in these journals during the five-year period were included in the base set of arti- cles. To identify the base journals, we established three criteria: (1) the journal must have a clear and exclusive AI focus, as indicated by its stat- ed editorial scope; (2) it must not be perceived by business faculty as having a relatively low academic quality; and (3) it must have a recog- nition factor at least half as large as the maxi- mum for all journals satisfying the first two criteria. The first criterion permits us to avoid deciding whether a specific article has suffi- cient AI content for inclusion in the base set of articles. This decision is made by the journals’ editors. The second criterion ensures that on the whole, articles in the base set are perceived by business faculty as being of sound quality. The third criterion gives a base set of articles that are not obscure from a business faculty perspective. Table 2 provides details behind the identifi- cation of base journals based on these criteria: Artificial Intelligence, AI Magazine, Expert Systems, Expert Systems with Applications, IEEE Expert, and IEEE Transactions on Pattern Analysis and Ma- chine Intelligence. The table shows all 19 jour- nals that meet the second criterion. Of these, five were eliminated because of the first criteri- on. They have a computing focus that includes but also goes beyond AI. Of the remaining jour- nals, IEEE Expert and AI Magazine had the maxi- mum recognition factor (at 86 percent). The other four base journals were all recognized at more than half this rate. The result is a substan- tial set of base articles that we contend is repre- sentative of what business faculty regard as quality research in the AI field. General Findings The base set of 1,519 articles yielded 36,420 citations to books, proceedings, and 1,224 dif- ferent journals in their combined reference lists. This data set is compiled from all vol- umes of the base journals from 1989 to 1993, including a special issue of AI Magazine in 1990. The 36,420 citations do not include ref- erences to working papers, personal commu- nications, presentations, and non-English ar- ticles. A tabulation of citation distributions by year is shown in table 3. The 15,600 journal citations consistently dominate the distribu- tion every year with no major variations. Notable differences in citation patterns are found across base journals, as shown in table 4. First, AI Magazine has a lower percentage of citations to journal articles (28 percent) than any of the other base journals. In contrast, IEEE Transactions on Pattern Analysis and Ma- chine Intelligence is the only base journal hav- ing over 50 percent of its citations to journals. Second, more than 39 percent of the 15,600 total citations to journal articles are from IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, which has nearly 10 times the number of journal citations found in ei- ther Expert Systems or AI Magazine. It has near- ly double the number of articles published in Artificial Intelligence and roughly matches the total number of articles appearing in the oth- er four base journals combined. These major differences lead us to rankings of journals based not on citation counts but rather on ci- tation scores that adjust for the imbalances among base journals’ contributions to the base set of AI articles. Citation analysis is an unobtru- sive way to judge the influence of research within the research community. Articles SUMMER 1996 89 ticles in the base journal citing it, as follows: Let i = a base year, i = 1, …, 5. j = a base journal, j = 1, …, 6. k = a journal referenced by the base set of articles, k = 1, …, 1224. Sk = the citation score of journal k. Cijk = the number of citations received by journal k from base journal j in base year i. nij = the number of articles published by base journal j in base year i. The 1224 different journals are then ranked A Ranking Based on Citation Score The relatively large number of articles pub- lished by IEEE Transactions on Pattern Analysis and Machine Intelligence in the past five years suggests that if raw citation counts are used as a basis for ranking, results will be dominated by the citation pattern of IEEE Transactions on Pattern Analysis and Machine Intelligence arti- cles. Consequently, we adjust the raw citation counts a journal receives by the number of ar- Articles 90 AI MAGAZINE Journal Name Recognition Factor Rank Focus Communications of the ACM 89 Top Computing IEEE Expert 86 Medium AI AI Magazine 86 Medium AI IEEE Transactions on Knowledge and Data Engineering 72 Top Computing Decision Support Systems 68 Top Computing IEEE Transactions on Systems, Man, and Cybernetics 64 Top Computing International Journal of Man-Machine Studies 60 Top Computing IEEE Transactions on Pattern Analysis and Machine Intelligence 60 Top AI Artificial Intelligence 58 Top AI Expert Systems with Applications 53 Medium AI Expert Systems 44 Medium AI Applied Artificial Intelligence 41 Medium AI International Journal of Expert Systems: Research and Applications 38 Medium AI Heuristics: Journal of Knowledge Engineering 37 Medium AI International Journal of Intelligent Systems 36 Medium AI Machine Learning 35 Top AI Knowledge Acquisition 32 Medium AI Journal of Automated Reasoning 28 Medium AI Applied Intelligence 24 Medium AI Table 2. Identifying a Set of Base Journals. 1989 1990 1991 1992 1993 No. of Articles 241 294 337 315 332 Total Citations 5565 7394 8086 8072 7303 Total Journal Citations 2591 3038 3353 3506 3112 Average No. of Journal Citations/Article 10.8 10.3 9.9 11.1 9.4 Journal Citations (%) 46 41 42 44 43 Book Citations (%) 22 27 28 24 24 Proceedings Citations (%) 23 24 23 25 26 Technical Report and Thesis (%) 9 8 7 7 7 Table 3. Distribution of Citations by Base Years. by their citation scores: S1, …, S1224. Only the top 5 percent of journals ranked by the citation score (that is, 62) are listed in table 5. It is unrealistic and not very useful to attempt reporting the ranks of over 1000 jour- nals. Besides, the 62 journals shown in table 5 represent over 70 percent of all journal article citations. It is interesting to see that half our base journals represent the top three: Artificial Intelligence, IEEE Transactions on Pattern Analy- sis and Machine Intelligence, and AI Magazine. Five of them are in the top 10, and all are in the top 25. Half the top 10 journals have a strict AI focus. The other half of the top 10 journals have a broader editorial scope that includes other fields of computing in addition to AI. A Ranking Based on a Normalized Score Journals that have been published over a longer period have a greater opportunity to be cited. To offset bias introduced by the age dif- ferences of the journals, we followed the ap- proach used in two earlier citation studies to obtain a normalized ranking (Cheng, Holsap- ple, and Lee 1995; Holsapple et al. 1994). In arriving at a normalized ranking, the begin- ning year of publication of each journal is ob- tained from Ulrich’s International Periodicals Directory (1993). The citation score is normal- ized by dividing the cumulative score for each journal by the total number of years the jour- nal has been in print during the period 1979 through 1992. In doing so, we assume that the scientific impact from a journal’s article in its field of study cannot last much longer than a decade. That is, few citations appearing in 1989 articles would be to articles published before 1979. This assumption is reasonable because AI is a rapidly growing and changing field. In addition, we assume that few 1993 publications cite other journal articles pub- lished in 1993. Thus, the period for normal- ization only goes through 1992. A ranking based on normalized citation scores is given in table 6. The column labeled differential indicates the relative shift in rank- ing under normalization. A positive differen- tial for a journal means that it is ranked high- er under the normalized scheme as opposed to the previous unnormalized scheme. This indicator represents relatively young, up-and- coming journals for influencing AI research. There are some substantial differences be- tween the two rankings shown in tables 5 and 6. First, despite a drop in ranking for AI Maga- zine, all the 6 base journals are among the 10 most influential journals for AI after normal- ization. Second, with normalization, 11 jour- nals rise from below the 5-percent reporting cutoff: (1) Neural Computation; (2) Neural Net- works; (3) AI Communications; (4) Complex Sys- tems; (5) AI in Medicine; (6) IEEE Transactions on Knowledge and Data Engineering; (7) Applied AI; (8) International Journal of Approximate Rea- soning; (9) Knowledge-Based Systems; (10) Inter- national Journal of Expert Systems; and (11) AI for Engineering Design, Analysis, and Manufac- turing. All these journals began publication af- ter 1986, and nearly all have a clear AI focus. Third, 11 journals drop below the 5-percent cutoff after normalization: (1) Scientific Ameri- can, (2) Journal of the Operational Research Soci- ety, (3) SIAM Journal on Computing, (4) Fuzzy Sets and Systems, (5) European Journal of Opera- tions Research, (6) Computer Journal, (7) Meth- ods of Information in Medicine, (8) Computers Articles SUMMER 1996 91 AI1 AIM2 ES3 ESWA4 IE5 ITPAMI6 Total No. of Articles 312 105 85 235 181 601 1,519 Total Citations 9,525 2,747 1,864 5,081 2,529 14,674 36,420 Total Journal Citations 3,292 764 784 2,218 957 7,585 15,600 Average No. of Journal Citations/Article 10.55 7.28 9.22 9.44 5.29 12.62 10.27 Journal Citations (%) 35 28 42 44 38 52 43 Book Citations (%) 28 28 34 31 28 19 25 Proceedings Citations (%) 27 30 19 20 26 23 24 Technical Report and Thesis 11 14 5 5 8 6 8 Table 4. Distribution of Citations by Base Journals. 1. AI = Artificial Intelligence. 2. AIM = AI Magazine. 3. ES = Expert Systems. 4. ESWA = Expert Systems with Applications. 5. IE = IEEE Expert. 6. ITPAMI = IEEE Transactions on Pattern Analysis and Machine Intelligence. S C n k ijk ij ji = × == ∑∑ 1 6 1 5 100 ulate what would have been her survey results if they had been included in her prespecified list. Clearly, subjective evaluation of journals’ effects on AI is not reflective of the actual cita- tion pattern of the articles in journals rated highly by business faculty. Half the 30 jour- nals ranked in Gupta’s study are below the 5- percent cutoff in our unnormalized ranking, and nearly one-third are not among the top 200 journals. In addition, one-third are below the 5-percent cutoff under the normalized rank. Based on specific journals, the citation pat- terns clearly suggested that business faculty should pay far more attention to the AI jour- nals AI Magazine and Expert Systems than they apparently are prone to do. They should also not overlook such journals as Computer Vision, Graphics, and Image Processing, Cognitive Sci- ences, Computational Intelligence, International Journal of Computer Vision, SIGART Newsletter, and Chemical Engineering, (9) Information Pro- cessing Letters, (10) Journal of the American Statistics Association, and (11) Journal of Experi- mental Psychology. All these journals began publication before 1979, and most have a broader focus than just AI. A Comparison of Rankings Although important differences are found be- tween the unnormalized and the normalized methods, disparities are also observed when these two objective rankings are compared to Gupta’s subjective ranking. As shown in table 7, the only ranks that remain unchanged are for the top two AI journals: (1) Artificial Intel- ligence and (2) IEEE Transactions on Pattern Analysis and Machine Intelligence. Other than these two journals, more than half the top 5 percent of journals in our citation analysis are not rated in Gupta’s study. We can only spec- Articles 92 AI MAGAZINE Rank Journal Name Score 1 Artificial Intelligence 4147.3 2 IEEE Transactions on Pattern Analysis and Machine Intelligence 1805.2 3 AI Magazine 950.3 4 Communications of the ACM 797.2 5 Computer Vision, Graphics, and Image Processing 796.4 6 International Journal of Man-Machine Studies 581.8 7 Expert Systems 569.3 8 IEEE Transactions on Systems, Man, and Cybernetics 565.2 9 Cognitive Science 513.7 10 IEEE Expert 512.4 11 Machine Learning 480.8 12 IEEE Computer 358.1 13 Journal of the ACM 300.2 14 IEEE Transactions on Software Engineering 277.8 15 Pattern Recognition 271.2 16 IEEE Transactions on Computers 225.2 17 Computational Intelligence 223.2 18 AI Expert 205.2 19 ACM Computing Surveys 194.9 20 International Journal of Computer Vision 194.6 21 Management Science 187.1 22 Journal of Automated Reasoning 174.2 23 International Journal of Robotics Research 173.1 24 IEEE Transactions on Robotics and Automation 171.0 25 Expert Systems with Applications 169.6 26 Biological Cybernetics 162.6 27 IEEE Transactions on Signal Processing 150.2 28 Journal of the Optical Society of America 143.3 29 Science 138.1 30 Psychological Review 133.8 Table 5. Ranking of AI Journals by Citation Scores. and IEEE Computer as sources (and outlets) for influential AI articles. Conversely, some jour- nals perceived by Gupta’s respondents to be of high quality have had little impact from a cita- tion-pattern perspective. For example, Heuris- tics and Applied Intelligence have had relatively little impact on the large and representative set of base AI articles derived from Gupta’s business faculty respondents. Conclusions Objective rankings of journals for AI research (from a business perspective) were developed. The method used was citation analysis. Facul- ty and students interested in AI can use the re- sults to create prioritized reading lists for stay- ing abreast of developments in the field. To complement external reviews in promotion cases, administrators can use the rankings to objectively assess the quality of research arti- cle placements. For researchers, the rankings suggest where to submit articles and give evi- dence to buttress merit-review cases. For li- brarians, they provide guidance about what AI journals are most important to have in a busi- ness collection. Some caution should be exercised when ap- plying our results. Just because a journal does not appear near the top of our rankings does not mean that it is not a quality publication. Its editorial scope might be so broad that its impact on AI research is small compared to publications devoted to AI. At the opposite ex- treme, a journal might be so highly specialized on some topic within the AI field that its im- Articles SUMMER 1996 93 Rank Journal Name Score 31 SIGART Newsletter 124.9 32 Pattern Recognition Letter 123.9 33 IEEE Transactions on Information Theory 123.7 34 Nature 122.7 35 Journal of the Royal Statistical Society 114.4 36 Computational Linguistics 110.6 37 Operations Research 110.1 38 Information Sciences 106.6 39 Knowledge Acquisition 106.3 40 Cognitive Psychology 105.7 41 Computers and Biomedical Research 104.6 42 Machine Intelligence 101.6 43 International Journal of Production Research 98.2 44 IBM Journal of Research and Development 94.9 45 Decision Sciences 92.9 46 Scientific American 88.3 47 Journal of the Operational Research Society 85.2 48 SIAM Journal on Computing 84.0 49 Fuzzy Sets and Systems 82.0 50 Decision Support Systems 80.7 51 Journal of Logic Programming 78.5 52 European Journal of Operations Research 76.8 53 Computer Journal 75.5 54 Image and Vision Computing 75.1 55 AI in Engineering 74.1 56 Methods of Information in Medicine 73.8 57 Computers and Chemical Engineering 71.4 58 IEEE Signal Processing 70.6 59 Information Processing Letters 70.1 60 Journal of the American Statistics Association 69.8 61 Journal of Experimental Psychology 69.0 62 Knowledge Engineering Review 66.8 Table 5. Continued. voted to the field of AI. In table 6, the highest- ranked business-computing journal is Decision Support Systems, but its scope is not restricted to AI articles. With the introduction of Wi- ley’s Intelligent Systems for Accounting, Finance, and Management in 1993, AI research affecting the business community appears to have gained a dedicated channel for dissemination. Although gauging the scientific impacts of journals on AI research has been the main purpose here, as well as identifying trends in AI research, other topics of interest (such as mapping the intellectual development in AI) can be investigated as an extension of the cur- rent study. pact is less than mainstream AI journals. Some journals are simply too new to appear in the rankings. Others might suffer from low circulations (for example, because of high subscription rates or modest promotion). Thus, use of the rankings to identify quality publications near the top should be supple- mented, as needed, with quality journals not near the top that are too broad, too narrow, or too new. Although there is a recent emergence of AI- specific journals in certain disciplinary ar- eas—such as AI for Engineering Design, Analy- sis, and Manufacturing; AI in Engineering; and AI in Medicine—there appears to be no estab- lished business-computing journal that is de- Articles 94 AI MAGAZINE Normalized Rank Unnormalized Rank Journal Name (year of origin) Differential 1 1 Artificial Intelligence (1970) 0 2 2 IEEE Transactions on Pattern Analysis and Machine Intelligence (1979) 0 3 10 IEEE Expert (1986) 7 4 3 AI Magazine (1980) –1 5 11 Machine Learning (1986) 6 6 7 Expert Systems (1984) 1 7 4 Communications of the ACM (1959) –3 8 5 Computer Vision, Graphics, and Image Processing (1969) –3 9 25 Expert Systems with Applications (1990) 16 10 6 International Journal of Man-Machine Studies (1969) –4 11 8 IEEE Transactions on Systems, Man, and Cybernetics (1971) –3 12 9 Cognitive Science (1977) –3 13 20 International Journal of Computer Vision (1987) 7 14 31 SIGART Newsletter (1989) 17 15 18 AI Expert (1986) 3 16 17 Computational Intelligence (1985) 1 17 39 Knowledge Acquisition (1989) 22 18 12 IEEE Computer (1971) –6 19 22 Journal of Automated Reasoning (1985) 3 20 13 Journal of the ACM (1954) –7 21 24 IEEE Transactions on Robotics and Automation (1985) 3 22 14 IEEE Transactions on Software Engineering (1975) –8 23 15 Pattern Recognition (1968) –8 24 16 IEEE Transactions on Computers (1968) –8 25 23 International Journal of Robotics Research (1982) –2 26 19 ACM Computing Surveys (1969) –7 27 21 Management Science (1954) –6 28 62 Knowledge Engineering Review (1988) 34 29 83 Neural Computation (1989) 54 30 32 Pattern Recognition Letter (1983) 2 31 68 Neural Networks (1988) 37 Table 6. Ranking of AI Journals by Normalized Citation Scores. References Bobrow, D. G. 1993. Artificial Intelligence in Per- spective: A Retrospective on 50 Volumes of the Arti- ficial Intelligence Journal. Artificial Intelligence 59(1): 5–20. CACM. 1994. Special Issue on Commercial and In- dustrial AI. Communications of the ACM 37(3): 23–119. Cheng, C. H.; Holsapple, C. W.; and Lee, A. 1995. Citation-Based Journal Rankings for Expert Systems Research. Expert Systems 12(4): 1–10. Cooper, R. B.; Blair, D.; and Pao, M. 1993. Commu- nicating MIS Research: A Citation Analysis of Jour- nal Influence. Information Processing and Manage- ment 29(1): 113–127. DeGross, J. I.; Davis, G. B.; and Littlefield, R. S. 1992. 1992 Directory of Management Information Systems Faculty in the United States and Canada. New York: MISRC–McGraw-Hill. Dutta, S. 1993. Knowledge Processing and Applied Arti- ficial Intelligence. Jordan Hill, Oxford: Butterworth- Heinemann. Eom, S. B., and Lee, S. M. 1993. Leading U.S. Uni- versities and Most Influential Contributors in Deci- sion Support Systems Research (1971–1989): A Cita- tion Analysis. Decision Support Systems 9(3): 237–244. Garfield, E. 1979. Citation Indexing: Its Theory and Application in Science, Technology, and Humanities. New York: Wiley. Gupta, U. G. 1994. The Academic Quality of AI Journals and the Role of AI in the MIS Curriculum: Perspectives of Business Faculty. Expert Systems with Applications 7(4): 581–588. Holsapple, C. W.; Johnson, L. E.; Manakyan, H.; and Tanner, J. 1993. A Citation Analysis of Business- Computing Research Journals. Information and Man- agement 25(2): 231–244. Holsapple, C. W.; Johnson, L. E.; Manakyan, H.; and Tanner, J. 1994. Business-Computing Research Jour- nals: A Normalized Citation Analysis. Journal of Articles SUMMER 1996 95 Normalized Rank Unnormalized Rank Journal Name (year of origin) Differential 32 26 Biological Cybernetics (1975) –6 33 79 AI Communications (1988) 46 34 27 IEEE Transactions on Signal Processing (1951) –7 35 55 AI in Engineering (1986) 20 36 28 Journal of the Optical Society of America (1917) –8 37 50 Decision Support Systems (1985) 13 38 70 Complex Systems (1987) 32 39 29 Science (1880) –10 40 30 Psychological Review (1894) –10 41 33 IEEE Transactions on Information Theory (1963) –8 42 34 Nature (1869) –8 43 51 Journal of Logic Programming (1984) 8 44 116 AI in Medicine (1989) 72 45 117 IEEE Transactions on Knowledge and Data Engineering (1989) 72 46 84 Applied Artificial Intelligence (1987) 38 47 35 Journal of the Royal Statistical Society (1838) –12 48 36 Computational Linguistics (1974) –12 49 37 Operations Research (1952) –12 50 86 International Journal of Approximate Reasoning (1987) 36 51 58 IEEE Signal Processing (1984) 7 52 87 Knowledge-Based Systems (1987) 35 53 38 Information Sciences (1969) –15 54 88 International Journal of Expert Systems (1987) 34 55 40 Cognitive Psychology (1970) –15 56 54 Image and Vision Computing (1983) –2 57 41 Computers and Biomedical Research (1969) –16 58 42 Machine Intelligence (1967) –16 59 43 International Journal of Production Research (1961) –16 60 95 AI for Engineering Design and Manufacturing (1987) 35 61 44 IBM Journal of Research and Development (1957) –17 62 45 Decision Sciences (1970) –17 Table 6.Continued. manufacturing, and group technology. Clyde Holsapple holds the Rosenthal Endowed Chair in Management Information Sys- tems and currently serves as area coordinator of Decision Science and Information Systems at the University of Kentucky. He does teaching and research in decision support systems, knowledge man- agement, expert systems, and organizational com- puting. His books include Foundations of Decision Support Systems, Micro Database Management, Busi- ness Expert Systems, and The Information Jungle. Hol- Management Information Systems 11(1): 131–140. Ulrich. 1993. Ulrich’s International Periodicals Direc- tory, 32nd ed. New York: Bowker. Chun Hung Cheng received his Ph.D. in business administration from the University of Iowa in 1990. Prior to joining the Chinese University of Hong Kong, he was an assistant professor at Grand Valley State University and Ken- tucky State University. His re- search interests include knowl- edge-based systems, object-oriented design, distributed computer systems, computer integrated Articles 96 AI MAGAZINE Normalized Rank Unnormalized Rank Gupta’s Rank Journal Name 1 1 1 Artificial Intelligence 2 2 2 IEEE Transactions on Pattern Analysis and Machine Intelligence 3 10 9 IEEE Expert 4 3 16 AI Magazine 5 11 6 Machine Learning 6 7 17 Expert Systems 7 4 3 Communications of the ACM 8 5 NA Computer Vision, Graphics, and Image Processing 9 25 11 Expert Systems with Applications 10 6 8 International Journal of Man-Machine Studies 11 8 5 IEEE Transactions on Systems, Man, and Cybernetics 12 9 NA Cognitive Science 13 20 NA International Journal of Computer Vision 14 31 NA SIGART Newsletter 15 18 26 AI Expert 16 17 NA Computational Intelligence 17 39 18 Knowledge Acquisition 18 12 NA IEEE Computer 19 22 13 Journal of Automated Reasoning 20 13 NA Journal of the ACM 21 24 NA IEEE Transactions on Robotics and Automation 22 14 NA IEEE Transactions on Software Engineering 23 15 NA Pattern Recognition 24 16 NA IEEE Transactions on Computers 25 23 NA International Journal of Robotics Research 26 19 NA ACM Computing Surveys 27 21 NA Management Science 28 62 25 Knowledge Engineering Review 29 83 NA Neural Computation 30 32 NA Pattern Recognition Letter 31 68 NA Neural Networks 32 26 NA Biological Cybernetics 33 79 NA AI Communications 34 27 NA IEEE Transactions on Signal Processing 35 55 NA AI in Engineering Table 7. Comparison of Journal Rankings for AI Research. sapple is also associate editor of Management Science and Organizational Computing; area editor for Deci- sion Support Systems and cofounder of the Interna- tional Society for Decision Support Systems. In 1993, he was named computer educator of the year by the International Association for Computer In- formation Systems. Anita Lee is an assistant professor in the decision science and information systems area at the Univer- sity of Kentucky. She received her Ph.D. in business administration from the University of Iowa. Her research interests include AI, ma- chine learning, knowledge-based systems, computer-integrated manufacturing, and group tech- nology. She is also the author of Knowledge-Based FMS Scheduling: An Artificial Intelligence Perspective. Articles SUMMER 1996 97 Normalized Rank Unnormalized Rank Gupta’s Rank Journal Name 36 28 NA Journal of the Optical Society of America 37 50 7 Decision Support Systems 38 70 NA Complex Systems 39 29 NA Science 40 30 NA Psychological Review 41 33 NA IEEE Transactions on Information Theory 42 34 NA Nature 43 51 NA Journal of Logic Programming 44 116 NA AI in Medicine 45 117 4 IEEE Transactions on Knowledge and Data Engineering 46 84 14 Applied Artificial Intelligence 47 35 NA Journal of the Royal Statistical Society 48 36 NA Computational Linguistics 49 37 NA Operations Research 50 86 NA International Journal of Approximate Reasoning 51 58 NA IEEE Signal Processing 52 87 20 Knowledge-Based Systems 53 38 NA Information Sciences 54 88 15 International Journal of Expert Systems: Research and Applications 55 40 NA Cognitive Psychology 56 54 NA Image and Vision Computing 57 41 NA Computers and Biomedical Research 58 42 NA Machine Intelligence 59 43 NA International Journal of Production Research 60 95 24 Artificial Intelligence for Engineering Design: Analysis and Manufacturing 61 44 NA IBM Journal of Research and Development 62 45 NA Decision Sciences 86 125 12 International Journal of Intelligent Systems NA 827 19 Applied Intelligence NA 414 21 Artificial Intelligence and Law NA NA 28 Artificial Intelligence and Society NA NA 29 Artificial Intelligence Today NA 605 22 Expert Systems for Information Management NA 386 10 Heuristics NA NA 27 Journal of Artificial Intelligence in Education NA 214 30 PC Artificial Intelligence NA 645 23 Robotics and Computer-Integrated Manufacturing NA = Not available. Table 7. Continued.