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Research Interests

Information Technology and Information Goods Intensity as Predictors of Organizational Expansion Activity October 23, 2002 Virginia Franke Kleist West Virginia University Irene Hanson Frieze William R. King University of Pittsburgh. Research Interests.

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Research Interests

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  1. Information Technology and Information Goods Intensity as Predictors of Organizational Expansion Activity October 23, 2002Virginia Franke KleistWest Virginia UniversityIrene Hanson FriezeWilliam R. KingUniversity of Pittsburgh

  2. Research Interests 1. Long term impact of information technology (IT) on firm organizational structures, particularly regarding the development of electronic markets 2. Unique economics of the information goods (IG) producing industries and electronic commerce 3. Value, performance, productivity and measurement issues of information systems investment 4. Economics of establishing security in networks

  3. Long Term Effects of IT and IG: • Information Technology and Information Goods Intensity as Predictors of Organizational Expansion Activity • Do information technology intense firms have different organizational boundaries than non-IT intense firms? • Do information goods producing firms have different organizational boundaries than non- information goods producing firms? • Is it possible to differentiate between the effect of IT and the effect of information goods production on the nature of organizational boundaries?

  4. The Electronic Markets Hypothesis • Electronic Markets Hypothesis (EMH) predicts that IT will lead to the staged dissolution of vertical firm boundaries • After the alliance phase, the EMH implies that vertical, fully neutral electronic markets will emerge in an IT enabled business world • EMH predictions have not been well verified empirically

  5. EchoStar/DirecTv, blocked FCC October 2002 Sony owns percentage of Palm, Inc. software unit, Oct. 2002 Scansoft (photo software) buys Royal Phillips (speech recognition software), October 2002 AT&T/ Comcast (2002) AOL/Netscape MCI/Worldcom AT&T/TCI Microsoft/Visio Ernst and Young LLP/Cap Gemini GTE/Bell Atlanticom AOL/Time Warner But, Anecdotal Evidence of Alliances and Mergers for Information Goods Producing Firms, e.g.:

  6. Information Producing Firms are Showing Trend of Increasing Mergers and Acquisitions 5000 4000 Mergers and Acquisitions (Worldwide Data) 3000 2000 1995 1996 1997 1998 estimate YEAR Source: Broadview and Assoc.

  7. An information goods firm is a firm where information goods products are the firm’s primary source of revenue. Can think of information goods as bits, while non- information goods are atoms (Negroponte 1995) Decision making (legal case archive, newspaper) Entertainment (songs on CD, tape, videos) Inputs for production (Software, marketing database) Service moving a digital bit stream (telecom or cable TV) What is an Information Goods Producing Firm (IGF)? Definition of IGF: Examples of IGF:

  8. Role of IT in Driving Boundary Change: • Vertical Boundaries: IT reduces the cost of transactions causing firms to make alliances for the purpose of acquiring the input goods needed for production • Horizontal Boundaries: IT reduces the coordination costs of being large in markets. • e.g., Malone, Yates and Benjamin (1987); Gurbaxani and Whang (1991); Clemons and Row (1991); Clemons, Reddi and Row (1993); Bakos and Brynjolfsson (1993); Brynjolfsson, et al. (1994)

  9. Drivers for IGF Vertical Boundary Change • IGF’s may have higher transactions costs due to valuation and intellectual property issues • IGF’s may have higher “connectedness” in design architecture (Lessig 1999, Milgrom 1992) • IGF’s may need to develop future products at same time as current to keep up with market pace (Shapiro and Varian 1999) • IGF’s use tacit, asset specific human inputs in the production process • IGF’s may be more difficult to value, more tightly intertwined to the product

  10. Drivers for IGF Horizontal Boundary Change • IGF’s products may have positive network externalities, leading to market failure • IGF’s production may have economies of scale in large deployments within markets, with high barriers to entry • IGF’s production may have increasing returns to scale • IGF’s products may act more like public goods than private goods • IGF’s may have economies of scope, extending across large markets

  11. TheoreticalModel The EMH: - to mergers, + to alliances Information Technology Intensity of the Firm Vertical Firm Boundary + Information Goods Intensity of the Firm Horizontal Firm Boundary +

  12. Research Model Vertical Integration Changes via Mergers/Sales _ Information Technology Intensity of Firm + + Vertical Integration Changes via Alliances/Sales + + + Information Goods Intensity Production Intensity Horizontal Integration Changes via Alliances/Sales + + Horizontal Integration Changes via Mergers/Sales

  13. Construct Operationalization

  14. Data: Correlations of IT Data

  15. Agway, Inc. AutoZone, Inc. Clorox Corp. Hershey Foods, Inc. Scott Paper Co. William Wrigley, Jr. Sherwin-Williams, Co. American Express Co. AT & T GTE Corp. MCI Telecom Northwest Airlines Donnelley & Sons TCI Data: High/High Firms Vs. Low/Low Firms Low/Low High/High

  16. total of all raw counts 140 120 100 80 60 40 Frequency Std. Dev = 53.15 20 Mean = 32.9 N = 317.00 0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0 220.0 240.0 260.0 280.0 300.0 320.0 340.0 360.0 380.0 total of all raw counts Data: Raw Counts of Merger and Alliance Event Data from WSJ “Hits” for Raw Search Terms: E.G., VENTURE, AGREEMENT, ALLIANCE, PARTNERSHIP, COALITION, LICENSE, LINK MERGER, ACQUSITION, PURCHASE, EXCHANGED STOCK n= 317 Firms

  17. Data: Raw Event Frequency Table

  18. Data: Coded Mergers and Alliance Data from WSJ Vertical and Horizontal Boundary Expansion Activity n= 317

  19. Data: Vertical Mergers/Sales, Vertical Alliances/Sales

  20. Data: Horizontal Mergers/Sales, Horizontal Alliances/Sales

  21. Tested Hypotheses: Vertical Integration Changes via Mergers/Sales Information Technology Intensity of Firm H1 H2** Vertical Integration Changes via Alliances/Sales H5** H6 H3 ** Information Goods Intensity Production Intensity Horizontal Integration Changes via Alliances/Sales H4** H7* H8 * Horizontal Integration Changes via Mergers/Sales

  22. Tested Hypotheses: Vertical Integration Changes via Mergers/Sales Information Technology Intensity of Firm H1 H2** Vertical Integration Changes via Alliances/Sales H5** H6 H3 ** Information Goods Intensity Production Intensity Horizontal Integration Changes via Alliances/Sales H4** H7* H8 * Horizontal Integration Changes via Mergers/Sales

  23. Results: IT Intensity and Scaled Vertical Mergers/Sales (H1)

  24. Results: IT Intensity and Scaled Vertical Alliances/Sales (H2)

  25. Results: IGF and Scaled Vertical Mergers/Sales (H3)

  26. Results: IGF and Scaled Vertical Alliances/Sales (H4)

  27. Results: Interaction of IT and IGF and Scaled Horizontal Mergers/Sales High IGF Firms with High IT have fewer horizontal mergers/sales than High IGF firms with Low IT (significant with post hoc Tukey test of means) :

  28. Contributions of Research • Measurement of information goods producing firms, IT and horizontal and vertical boundary expansion • Model differentiating vertical and horizontal boundary expansion • Some support of EMH • Introduction of information goods firms into the electronic markets hypothesis discussion • Results indicating that information goods producers have different boundary expansion behaviors when compared to non-information goods producers

  29. Future Research • Do these effects hold when controlling for the age of the firms, industry type, stock price expectation management or market exuberance? • Policy issues if IGF’s tend to have more mergers and alliances both horizontally and vertically? • Are ecommerce firms similar to IGF’s? • Evidence of Increasing Returns for IGF’s? Will “post tipping point” digital products be more profitable in the electronic commerce world? • Is there a horizontal electronic markets hypothesis? In ecommerce? • Do firms with more sophisticated IT have enhanced financial performance? • Are mid market sized software firms (pre tipping point) more likely to produce defect free software?

  30. Results: Research Summary

  31. Data: Cell Sizes

  32. Data: Variable Frequencies

  33. MANOVA Results of IT Intensity and IGF to Scaled Horizontal Activity: H5, H6, H7, H8

  34. Data: IGF Scaling

  35. Data: Graph of Dependent Variable Frequencies Number of Events Type of Boundary Expansion

  36. Data: IT Intensity Raw Data Used for Scaling Frequency Highest Expenditure Reported on Questionnaire

  37. Data: IGF Scaling Raw Data

  38. Data: IT Intensity Scaling to Hi/Lo

  39. Data: IGF Scaling Hi/Lo IGIPF Scaled Hi/Lo 300 200 100 Frequency Std. Dev = .40 Mean = 1.20 N = 317.00 0 1.00 2.00 IGF Scaled Hi/Lo

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