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Information Technology and Information Producers: What will our economy look like in 50 years?

February 12, 2002

Virginia Franke Kleist, Ph.D.West Virginia University

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Research InterestsResearch Interests1. Long term impact of information technology (IT)

on firm organizational structures

2. Unique economics of the information goods producing firms (IGF) and electronic commerce

3. Value, performance, productivity and measurement issues of information systems investment

4. Economics of establishing security in networks

5. Long term effects of IT in society

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1. Long Term Effects of Information 1. Long Term Effects of Information Technology and Information Goods on Market Technology and Information Goods on Market

StructuresStructures

Information technology may make it easier for firms to acquire inputs

Firms may tend to get smaller with IT Information goods production has certain unique

economics Information goods producers may tend to get

larger What will market structures look like in the years

ahead?

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2. Unique economics of the information 2. Unique economics of the information goods producing firms (IGF) and goods producing firms (IGF) and

electronic commerce electronic commerce

Information goods may act differently than other goods

Information goods are more like public goods, have economies of scale and scope

Once “post-tipping point,” may exhibit network externalities

Do successful IGF’s outperform other firms? Are the .com failures partially attributable to the

information goods phenomenon?

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3. Value, performance, productivity and 3. Value, performance, productivity and measurement issues of information measurement issues of information

systems investmentsystems investment

Difficult to establish a return on investment (ROI) for information technology

Historical patterns to metrics used for ROI are similar to the technologies themselves

Department of Justice ROI studyKnowledge Management ROI study

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4. Economics of establishing security in 4. Economics of establishing security in networksnetworks

Technologies of network security have improved over time

As technologies of trust improve, the cost of transactions in electronic markets falls

As tools of electronic trust improve, have less need for reliance on human trust in electronic commerce

Long term effects of electronic security on the development of electronic markets

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5. Long term effects of IT in society5. Long term effects of IT in society

IT and underemployment of women and Hispanics IT and IP telephony IT and complex transformation of art: e.g., time

and installations, ease of replication, loss of sound in MP3’s, avatars vs. reality, melding of culture, instruments and songs by single artist, the technology becomes the art form, linear thinking vs. hypermedia, collaborative work and intellectual property

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Detailed discussion of IT, IGF Detailed discussion of IT, IGF and market transformationsand market transformations

Theoretical modelResearch modelHypothesesMeasurementResultsImplications

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The Electronic Markets The Electronic Markets HypothesisHypothesis

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

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But, Anecdotal Evidence of Alliances and But, Anecdotal Evidence of Alliances and Mergers for Information Goods Producing Mergers for Information Goods Producing

Firms, e.g.:Firms, e.g.:

AOL/NetscapeMCI/Worldcom/SprintAT&T/TCIMicrosoft/VisioErnst and Young LLP/Cap GeminiGTE/Bell AtlanticomAOL/Time Warner

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Information Producing Firms are Information Producing Firms are Showing Trend of Increasing Mergers Showing Trend of Increasing Mergers

and Acquisitionsand Acquisitions

YEAR

1998 estimate

199719961995Me

rge

rs a

nd

Acq

uis

itio

ns

(Wo

rld

wid

e D

ata

)

5000

4000

3000

2000

Source: Broadview and Assoc.

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What is an Information Goods What is an Information Goods Producing Firm (IGF)?Producing Firm (IGF)?

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)

Definition of IGF: Examples of IGF:

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Role of IT in Driving Boundary Role of IT in Driving Boundary Change: 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)

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Drivers for IGF Vertical Drivers for IGF Vertical Boundary Change 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

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Drivers for IGF Horizontal Drivers for IGF Horizontal Boundary ChangeBoundary 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

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Theoretical Model

The EMH: - to mergers, + to

alliances

+

Information Technology Intensity of

the Firm

Vertical Firm

Boundary

Information Goods

Intensity of the Firm

Horizontal Firm

Boundary

+

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Research ModelResearch Model

Information Technology Intensity of Firm

Information Goods Intensity Production Intensity

Vertical Integration Changes via Mergers/Sales

Vertical Integration Changes via Alliances/Sales

Horizontal Integration Changes via Alliances/Sales

Horizontal Integration Changes via Mergers/Sales

_

++

+

+

+

+

+

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Construct OperationalizationConstruct OperationalizationConstruct Operationalization

Information Technology Intensity of Firm

IT Expenditures from 1994 Computerworld survey, scaled

Information Goods Intense Production of Firm

Experts guided by NAICS Information Industries, scaled

Mergers and Acquisitions Event study of mergers and alliances from WSJ 1995, 1996 controlled for sales, scaled

Vertical and Horizontal Expert coded based on Dept. of Justice antitrust guidelines.

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Data: Correlations of IT DataData: Correlations of IT DataCorrelation of Highest, Mean and Lowest Expenditure IT Data to Reliability and

Validity Measures

1 2 3 4 5 6 7 1. IT$ MIN. Pearson Corr. 1.000 Sig. (2-tailed) . N 317 2. IT$ MEAN Pearson Corr. .666** 1.000 Sig. (2-tailed) .000 . N 316 317 3. IT$ HIGH Pearson Corr. .277** .845** 1.000 Sig. (2-tailed) .000 .000 . N 316 316 317 4. AVG. PC Pearson Corr. .016 .103 .122** 1.000 Sig. (2-tailed) .784 .074 .032 . N 304 304 305 306 5. HIGH PC Pearson Corr. -.045 .216** .341** .735** 1.000 Sig. (2-tailed) .440 .000 .000 .000 . N 303 303 303 303 304 6. PWC IT/EXP Pearson Corr. .042 .121 .198** .082 .119 1.000 Sig. (2-tailed) .540 .076 .004 .240 .091 . N 215 215 216 206 204 217 7. SGA EXP Pearson Corr. .073 .249** .326** .065 .214** .036 1.000 Sig. (2-tailed) .222 .000 .000 .282 .000 .621 .

N 283 283 284 273 271 193 284 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

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Data: High/High Firms Vs. Data: High/High Firms Vs. Low/Low FirmsLow/Low Firms

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

Low/Low High/High

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Data: Raw Counts of Merger and Alliance Data: Raw Counts of Merger and Alliance Event Data from WSJEvent 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

total of all raw counts

380.0360.0

340.0320.0

300.0280.0

260.0240.0

220.0200.0

180.0160.0

140.0120.0

100.0

80.060.0

40.020.0

0.0

total of all raw countsF

requ

ency

140

120

100

80

60

40

20

0

Std. Dev = 53.15

Mean = 32.9

N = 317.00

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Data: Raw Event Frequency Data: Raw Event Frequency TableTable

Search Term: Mean: Total Number of Hits for 319

Firms: Venture$ 3.86 1219 Agree$ 8.83 2798 Alliance$ 1.37 434 Partner$ 3.38 1073 Coalition 0 25 Licens$ 1.02 322 Link$ .77 244 Merger$ 2.23 708 Acqui$ 7.27 2304 Purchas$ 3.97 1257 Exch. Stock 0 1

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Data: Coded Mergers and Data: Coded Mergers and Alliance Data from WSJAlliance Data from WSJ

Vertical and Horizontal Boundary Expansion Activity

n= 317

total of all coded mergers and alliances

80.0

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

total of all coded mergers and alliancesF

req

ue

ncy

300

200

100

0

Std. Dev = 8.94

Mean = 5.4

N = 317.00

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Data: Vertical Mergers/Sales, Data: Vertical Mergers/Sales, Vertical Alliances/SalesVertical Alliances/Sales

vm's counts divided by sales- raw data

.30.25.20.15.10.050.00

vm's counts divided by sales- raw data

Fre

qu

en

cy

400

300

200

100

0

Std. Dev = .03

Mean = .00

N = 317.00

va's divided by sales- raw data

2.25

2.00

1.75

1.50

1.25

1.00

.75

.50

.25

0.00

va's divided by sales- raw data

Fre

qu

en

cy

400

300

200

100

0

Std. Dev = .15

Mean = .03

N = 317.00

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Data: Horizontal Mergers/Sales, Data: Horizontal Mergers/Sales, Horizontal Alliances/SalesHorizontal Alliances/Sales

ha's divided by sales- raw data

9.008.50

8.007.50

7.006.50

6.005.50

5.004.50

4.003.50

3.002.50

2.001.50

1.00.50

0.00

ha's divided by sales- raw data

Fre

qu

en

cy

300

200

100

0

Std. Dev = .86

Mean = .42

N = 317.00

hm's divided by sales

7.006.50

6.005.50

5.004.50

4.003.50

3.002.50

2.001.50

1.00.50

0.00

hm's divided by sales

Fre

qu

en

cy

200

100

0

Std. Dev = .91

Mean = .50

N = 317.00

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Tested Hypotheses:Tested Hypotheses:

Information Technology Intensity of Firm

Information Goods Intensity Production Intensity

Vertical Integration Changes via Mergers/Sales

Vertical Integration Changes via Alliances/Sales

Horizontal Integration Changes via Alliances/Sales

Horizontal Integration Changes via Mergers/Sales

H1H2**

H8 *

H6H5**

H4 **H3 **

H7 *

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Results: IT Intensity and Results: IT Intensity and Scaled Vertical Mergers/Sales Scaled Vertical Mergers/Sales

(H1)(H1)Hypothesis One. Information technology intensity will

have a negative relationship with the number of vertical

mergers (controlled for sales).

The chi square test was not significant for the information technology to

vertical merger over sales relationship (2 (1) = 1.704, p < .200, n = 317):

Cell Sizes for Information Technology to Vertical Mergers

Information Technology, Scaled Vertical Mergers over Sales, Scaled

Low High

Low 222 (97 %)

84 (94%)

High 6 (3%)

5 (6%)

N= 228 N=89

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Results: IT Intensity and Results: IT Intensity and Scaled Vertical Scaled Vertical

Alliances/Sales (H2)Alliances/Sales (H2)Hypothesis Two. Information technology intensity will have a

positive relationship with the number of vertical alliances

(controlled for sales).

Cell Sizes for Information Technology to Vertical Alliances

Information Technology, Scaled Vertical Alliances over Sales, Scaled

Low High

Low 212 (93%)

74 (83%)

High 16 (7%)

15 (17%)

N= 228 N=89

Hypothesis Two was supported by the analysis; (2 (1) = 7.020, p = .008, n = 317).

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

Hypothesis Three. The number of vertical mergers

(controlled for sales) is positively related to the degree that the

firm is an information goods intense producer.

Cell Sizes for Information Goods Intense Firm to Vertical Mergers

Information Goods Intense Firms, Scaled Vertical Mergers over Sales, Scaled

Low High

Low 248 (98 %)

58 (91%)

High 5 (2%)

6 (9%)

N= 253 N=64

Hypothesis Three supported; (2 (1) = 8.348, p = .004, n = 317).

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

Hypothesis Four: The number of vertical alliances (controlled for sales) is

positively related to the degree that the firm is an information goods intense

producer.

Cell Sizes for Information Goods Intense Firms to Vertical Alliances

Information Goods Intense Firms, Scaled Vertical Alliances over Sales, Scaled

Low High

Low 242 (96 %)

44 (69%)

High 11 (4%)

20 (31%)

N= 253 N=64

Hypothesis Four was supported by the analysis; (2 (1) = 41.899, p < .001, n = 317).

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Results: Interaction of IT and Results: Interaction of IT and IGF and Scaled Horizontal IGF and Scaled Horizontal

Mergers/SalesMergers/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) :

Estimated Marginal Means of Horizontal

Mergers by sales scaled 1, 3

IGF Scaled Hi/Lo, Regular

2.001.00

Est

imat

ed M

argi

nal M

eans

2.4

2.3

2.2

2.1

2.0

1.9

1.8

1.7

1.6

IT high $ Hi/Low

1.00

2.00

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Results: Research SummaryResults: Research Summary

Hypothesis: Variables of Interest: Support and Significance Level:

H1: Information technology will have a negative relationship with the number of vertical mergers (controlling for sales).

IT, Vertical Mergers No support.

H2: Information technology will have a positive relationship with the number of vertical alliances (controlling for sales).

IT, Vertical Alliances (2 (1) = 7.020, p = .008, n = 317)

H3: The number of vertical mergers (controlling for sales) is positively related to the degree that the firm is an information goods intense producer.

IGF, Vertical Mergers (2 (1) = 8.348, p = .004, n = 317).

H4: The number of vertical alliances (controlling for sales) is positively related to the degree that the firm is an information goods intense producer.

IGF, Vertical Alliances (2 (1) = 41.899, p < .001, n = 317).

H5: The number of horizontal alliances (controlling for sales) is positively related to the degree that the firm is an information goods intense producer.

IGF, Horizontal Alliances Univariate information goods intense firm main effect (F (1, 317) = 24.756, p < .001),

H6: The number of horizontal mergers (controlling for sales) is positively related to the degree that the firm is an information goods intense producer.

IGF, Horizontal Mergers No support.

H7: The number of horizontal alliances (controlling for sales) is expected to be positively related to the degree that the firm intensively uses information technology.

IT, Horizontal Alliances Univariate main effect for IT to the horizontal alliances variable, (F (1, 317) = 6.117, p < .05).

H8: The number of horizontal mergers (controlling for sales) is expected to be positively related to the degree that the firm intensively uses information technology.

IT, Horizontal Mergers

No support. Univariate main effect for IT to a negative relationship of horizontal mergers to the degree that a firm intensively uses information technology, (F (1, 317) = 6.998, p < .05).

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Contributions of ResearchContributions 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

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Future ResearchFuture 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?

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Data: Cell SizesData: Cell Sizes

Cell Frequencies for Chi Square and MANOVA Analyses:

IT high dollars scaled Hi/Lo

Total

1 2 IGF

Scaled Hi/Lo

1 185 68 253

2 43 21 64 Total 228 89 317

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Data: Variable Frequencies Data: Variable Frequencies

IGF

Responses IT high dollars

(Millions)

Total counts of Vertical

Mergers

Total counts of Vertical Alliances

Total counts of Horiz.

Alliances

Total counts of Horiz. mergers

N 317 317 317 317 317 317 Mean 1.614 84.294 .060 .321 2.953 2.035

Median 1 17.500 .000 .000 1.000 1.000 Range 4 499.750 4 16 53 30

Minimum 1 .250 0 0 0 0 Maximum 5 500.000 4 16 53 30

Sum 19 102 936 645

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

H8H8Information Goods Firm, Scaled

Low HighInformationTechnologyIntensity,Scaled

Low High Low High

n = 185 68 43 21HorizontalAlliances/ Sales,Scaled Hi,Medium, Low(1),(2)Mean 1.514 1.941 2.233 2.381Standard Error .057 .094 .118 .168HorizontalMergers/ Sales,Scaled Hi,Medium, Low(1),(3)Mean 1.897 1.853 2.326 1.714Standard Error .060 .100 .125 .179

1. Significant univariate main effect for InformationTechnology Dollars, Scaled2. Significant univariate main effect for Information GoodsFirm, Scaled3. Significant univariate interaction effect for InformationTechnology Dollars, Scaled by IGF Scaled

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Data: IGF ScalingData: IGF ScalingTotal of all IGF responses

Frequency Percent Valid Percent

Cumulative Percent

Valid 4.00 183 57.7 57.7 57.7 5.00 38 12.0 12.0 69.7 6.00 18 5.7 5.7 75.4 7.00 14 4.4 4.4 79.8 8.00 10 3.2 3.2 83.0 9.00 2 .6 .6 83.6 10.00 4 1.3 1.3 84.9 11.00 4 1.3 1.3 86.1 12.00 9 2.8 2.8 89.0 13.00 4 1.3 1.3 90.2 14.00 4 1.3 1.3 91.5 15.00 1 .3 .3 91.8 18.00 3 .9 .9 92.7 19.00 9 2.8 2.8 95.6 20.00 14 4.4 4.4 100.0 Total 317 100.0 100.0

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Data: Graph of Dependent Data: Graph of Dependent Variable FrequenciesVariable Frequencies

Type of Boundary Expansion

VMVAHMHA

Me

an

Nu

mb

er

of C

od

ed

Eve

nts

1000

800

600

400

200

0

Type of Boundary Expansion

Num

ber

of E

ven t

s

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Data: IT Intensity Raw Data Data: IT Intensity Raw Data Used for ScalingUsed for Scaling

IT$ HIGH reported to CW

500.0400.0300.0200.0100.00.0

IT $ HIGH Reported to CWF

req

ue

ncy

300

200

100

0

Std. Dev = 148.74

Mean = 84.3

N = 317.00

Highest Expenditure Reportedon Questionnaire

Fre

quen

cy

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Data: IGF Scaling Raw DataData: IGF Scaling Raw Data

Total of all IGIPF responses

20.017.515.012.510.07.55.0

Total of all IGIPF responsesF

req

ue

ncy

300

200

100

0

Std. Dev = 4.53

Mean = 6.5

N = 317.00

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Data: IT Intensity Scaling to Data: IT Intensity Scaling to Hi/LoHi/Lo

IT high dollars scaled hi, lo

2.001.501.00

IT high dollars scaled hi, lo

Fre

quency

300

200

100

0

Std. Dev = .45

Mean = 1.28

N = 317.00

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Data: IGF Scaling Hi/LoData: IGF Scaling Hi/Lo

IGF Scaled Hi/Lo

2.001.00

IGIPF Scaled Hi/LoF

requ

ency

300

200

100

0

Std. Dev = .40

Mean = 1.20

N = 317.00