1 information technology and information goods intensity as predictors of organizational expansion...
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Information Technology and Information Goods Intensity as
Predictors of Organizational Expansion Activity
October 23, 2002
Virginia Franke KleistWest Virginia University
Irene Hanson FriezeWilliam R. King
University of Pittsburgh
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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
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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?
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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
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But, Anecdotal Evidence of Alliances and Mergers for Information Goods Producing
Firms, e.g.:• 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
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Information Producing Firms are Showing Trend of Increasing Mergers and
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 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 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 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
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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
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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 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 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 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. Low/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 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
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 Table
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 Alliance 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, Vertical 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, Horizontal 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:
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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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 Scaled Vertical Mergers/Sales (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 Scaled Vertical 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 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 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 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) :
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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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
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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?
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Results: 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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Data: 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
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 to Scaled Horizontal Activity: H5, H6, H7, H8
Information Goods Firm, ScaledLow High
InformationTechnologyIntensity,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 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 Variable 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 Used 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
37
Data: 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 Hi/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/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