What is MIS?

1. What is MIS?

2. Two Specific Questions • How can MIS be identified within academia? • What differentiates high and low quality MIS research?

3. Method • Determine fields related to MIS (Katerattankul, Han, & Rea, 2006) • Gather article attributes from the Top 6-9 journals in each of these related fields and MIS

4. Disciplines

5. Data • Scrape ISIknowledge.com • 102,388 articles • Attributes analyzed included • Title • Publication • Abstract • Keywords • Citations per Year • References to other articles • Many more

6. Coded Articles • 50 citation classics were randomly chosen from the MIS articles • Matched with 50 non-citation classics on journal and publication year • Coded each of these 100 articles in groups of 3 after a training session and 2 trials • Attributes coded • Theoretical contribution • Type of article (Empirical, Theoretical, Review, Methodological) • Type of study

7. How can MIS be identified within academia?

8. Abstract Analysis Jaebong and John

9. Analysis of Research Paper Abstracts • Determine disciplines similar to MIS • Comparative definition of MIS discipline • 13 Disciplines • MIS, Accounting, Communication, … • Variables • 3 Numeric variables • No. of authors • No. of pages (end page – start page = no. of pages) • No. of total citations (received to date) • 817 Text variables - nouns and noun phrases • Extracted from abstracts

10. Descriptive Statistics 13 Disciplines; 38,642 Papers

11. Framework for Analysis Extract nouns and noun phrases by term frequency (TF) for each discipline MIS Mgmt Psychology Computer Science … Extract most frequent 150 terms from each disciplineResult: 817 distinct terms Global Vocabulary (817 distinct terms) Build a bag-of-words model for each paper Bag-of-Words for Each Paper Apply cluster analysis to bag-of-words from papers Cluster Analysis

12. 5 Naturally Formed Clusters Total # of papers: 38,642 No. of papers / cluster

13. 1 Info Systems for Decision Support ● Core: Library Science ● Communication-based ● Not psychology

14. 2 Organizational Behavior ● Human side ● Sociology in business school ● Collaborative

15. 3 Electrical Engineering & Healthcare ● Technical side ● Data-driven ● Not human

16. 4 Economics & Accounting ● Econ & Acct very similar ● No psychology ● Numbers-based

17. 5 What MIS is NOT ● Outside business school ● Stress related ● MIS does not research

18. Percent of Each Discipline in Clusters

19. MIS in Clusters

20. Keyword Analysis John and Yu-Kai

21. Keyword Analysis in a Nutshell • Questions to be asked and addressed: • How to represent a discipline? • Vector Space Model • Based on the representation, how to compare the relations/similarities among different disciplines? • Cosine Similarity • How’s the relations/similarities between MIS and the other disciplines evolve over time?

22. Vector Space Model = <w11, w12, … , w1x> = < w21, w22, … , w2x >

23. Cosine Similarity Illustration of cosine similarity

24. Similarity of MIS with the other Areas(measurement unit: each year) Similarity

25. Similarity of MIS with the other Areas(measurement unit: every two years) computer science Similarity marketing management healthcare sociology economics education psychology electronical engineering accounting

26. Reference Analysis Justin G., Devi, Shan

28. Interaction of MIS vs others • Indicators: • MIS Contribution (CMIS) • MIS Consumption (MISC) Contribution to MIS CMIS Who are buying ideas?

29. MIS Contribution MIS Contribution

30. MIS Consumption MIS Consumption Education

31. MIS Consumption MIS Consumption Library science Education Healthcare

32. Citation Analysis using Google Motion Charts 1970 - 2009

33. Number of citations received by a discipline 1970 - 2009

35. Number of references given by a discipline 1970 - 2009

37. Number of self citations by a discipline 1970 - 2009

39. Number of citations receivedVsNumber of references given 1970 - 2009

42. Market share of total citations received by a discipline 1970 - 2009

44. Market share of total references given by a discipline 1970 - 2009

46. What differentiates high quality and low quality articles in MIS? Dan, Julian, and Justin W.

47. Overview • Identify factors that determine high quality MIS articles • “High quality” = 100 or more citations • Logistic regression models • Dependent variable is binary variable called “quality” • 1 = high quality • 0 = not high quality

48. Analysis • Analysis used 6 models • 2 “standard” models • 5 or 6 explicit variables from ISI data set • 4 “conceptual phrase” models • Numerous phrases derived from article title, author keywords and ISI keywords generated by text mining

49. Two “Standard” Models “Standard” model • Years since publication • Number of references • Number of authors • Number of pages • Type of document “Standard” + name model • Years since publication • Number of references • Number of authors • Number of pages • Type of document • Name of journal* * Name of journal suspected of dominating “standard” model

50. Four “Conceptual Phrase ” Models Steps to find new possible “conceptual phrase” variables • Text-mine fields for most frequently used terms in • Article titles • Author keywords • ISI keywords • Group terms into conceptual phrases • Add conceptual phrases to “standard” models • “standard” + title • “standard” + author keywords • “standard” + ISI keywords • “standard” + title + author keywords + ISI keywords