bachelors' and nine master's level project management degrees identified, ... How can we objectively measure the project management deployment level within ...
INVESTIGATING THE DEPLOYMENT OF PROJECT MANAGEMENT: A TIME-DISTANCE ANALYSIS APPROACH OF G8, EUROPEAN G6, AND OUTREACH 5 COUNTRIES CHRISTOPHE N. BREDILLET, PHD, D.SC. Dean Postgraduate Programmes Lille School of Management Research Group Euralille, France
PHILIPPE RUIZ, PHD Associate Professor of Statistics and Research Methods, Co-Director of the PhD Programme Lille School of Management Research Group Euralille, France
FAYSAL YATIM, PMP Operations & Services Manager of the Middle East area Bull Company
Background Project management has spread all over the world, increasing yearly during the last decades. It covers a large variety of countries, with different social, economic, and cultural specifics. Through projects of all kinds, organizations are creating value by translating vision and implementing strategy. World Bank data indicate that 21% of the world’s gross domestic product (GDP) is gross capital formation (World Bank, 2005), which is almost entirely project-based. According to KPMG’s 2005 Global IT project management survey of 600 organizations in 22 countries, “The boards and executives are making commitments to deliver benefits through projects, and during the last 12 months, increases in project activity, budgets and complexity have been recorded” (KPMG, 2005, p. 3). The growth of professional associations aimed at developing and supporting project management globally or regionally, such as the Project Management Institute (PMI), International Project Management Association (IPMA), APM Group (U.K.) (APMG-UK), Project Management Association Japan (PMAJ), Greater-China Project Management Association (GPMA, China) and others, attests to the huge resources and energies committed to and invested in supporting and assisting deploying project management globally. The development of standards and research by these professional bodies is both grounded in and reinforces this deployment. Grounded in and supported by the development of standards, professional certifications and credentials have been developed and are updated constantly to meet new challenges faced by project management. In 2006 alone, PMI awarded its PMP® certification to more than 33,000 individuals (PMI, 2007a), and IPMA planned to certify more than 14,000 individuals (IPMA, 2006). The educational systems in many countries have begun to contribute to this project management deployment. The number of project management programs at the major universities and academies is increasing significantly. A look at the Global Accreditation Center (GAC) of PMI (PMI, 2007b) shows this move: in 1994, there were only two bachelors’ and nine master’s level project management degrees identified, primarily in construction management. By 2007, the number had grown to over 360 degree programs at 250 institutions worldwide. The major demanding customer of project management, organizations—big, medium and small, private and public, local, national, regional, and international—are more and more adopting project management as an integrated part of their structure. A look at the number and size of the organizations joining the PMI Global Corporate Council program (PMI, 2007c) shows the level of commitment in these organizations to project management: These predominantly multinational organizations represent more than two million employees in 190 countries and within 13 industries. They include Accenture, Boeing, BAE Systems, ICF International Inc., Huawei Technologies 1 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
Company, KPMG International, Siemens Business Services, Deloitte (U.K.), IBM Corp., NASA, and SAP (PMI, 2007c). The above observations lead to the recognition that project management is in fact rapidly deploying all over the world, but we should state that this deployment is not the same in all the countries, and there is no uniformity of its acceptance between the various political or economical regions. In 2005, for example, PMI recorded 75 new certified individuals in Austria, 2 in Azerbaijan, and 2578 in Brazil, while in the same year IPMA recorded 958 in Austria, 18 in Azerbaijan, and 13 in Brazil. This raises several questions. For instance—the list is of course not exhaustive—beyond the big picture: 1. 2. 3. 4. 5.
How can we objectively measure the project management deployment level within a group (country, region, or socio-group), and how can we measure the degree of uniformity— the level of disparity and the difference of the project management deployment between elements of the group—of such deployment? What are the differences in terms of the pace of deployment between elements of a group? Why? What is the role of professional bodies in such deployment? What are the consequences in term of socio-economic development?
The purpose of this paper is to suggest the use of a special indicator (Project Management Deployment Index, or PMDI) and to apply a specific relevant approach called Time-Distance Analysis (TDA) in order to provide some elements of answers to the first two questions and new insights compared to classical approaches and studies. The other questions will be addressed in future papers.
Classical Perspective As per our literature review, the consulted studies of the project management deployment theme are mainly based on classical approaches using for instance: •
•
For data collection: one or more project management maturity models to collect data and/or rely on existing collected data available from international bodies and organizations. For instance, the main objectives of the research program project orientation [international] were the analysis and benchmarking of 10 projectoriented nations (Gareis, 2004; Gareis & Fuessinger, 2007). For data analysis: the conventional statistical measurement and comparisons tools. We mean that the growth variations are recorded mainly on a time period basis (generally one year), and comparisons are made between these percentages to evaluate the differences between the measured units (countries or regions or socio-economic groups). The current state of the art of the comparative analysis is based mainly on some conventional statistical measures. The basic formulas are presented below (formula (1) to (3)). In the following formulation the subscripts (i) and (j) indicates respectively two time series or units (i) and (j). X indicates the level of the Indicator (variable) at time t (Sicherl, 2004c). •
Absolute difference between units i and j at time t: Aij(t) = Xi(t) – Xj(t)
•
Ratio between units i and j at time t: Rij(t) = Xi(t)/Xj(t)
•
(1) (2)
Percentage difference between units i and j at time t: Pij(t) = [Xi(t)/Xj(t) – 1]*100
(3)
Other project management development or deployment studies have been made, but they were based mainly on the number of certifications awarded or membership affiliations to specialized organizations like PMI and IPMA. An interesting illustration in Majewski (2004) shows the use of the approach to measure, analyze, and compare its certification program’s performance all over the world. PMI uses the indicator of the yearly number of certified PMPs to measure the growth of project management and to compare the various countries and regions (see the PMI Fact File section in the PMI Today publication from PMI).
2 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
While the yearly number indicator limits the analysis to the current measured year only, the cumulative number indicator allows us to measure also the dynamic variation of the indicator during the studied period of time, such as 1998-2006. Other organizations, such as IPMA, are using the same approaches to measure and analyze their own certification programs (IPMA, 2006). Although producing valuable results, Sicherl exposed that the present state of the art of comparative analysis needs improvement: “comparisons over indicator space and over time need to be better integrated” (Sicherl, 2004a, p. 11). Beyond a comparison at a given time between levels of a given Indicator, and its percentage of variation between two periods of time, there is a need to capture information like the relative growth of an Indicator among elements of a group: who is moving faster, or slower? Who is late, who is ahead? And from how many months, years…?When a country will catch-up with another one?. The classical approach is quite static and cannot provide answers to the need of understanding the dynamic at stake.
Gaining New Insights: Time-Distance Analysis According to Sicherl (1998), existing methods in economics and statistics fail to extract the notion of time embodied in the existing data (time is embodied in an Indicator, whose measure is done at a specific given time: the Indicator as such doesn’t mean anything without time) and to fully use this information. This, comparative analysis based on time series data does use the time as an identifier only : in 2006, the number of PMPs in the U.S. was 115,933) . It does not incorporate the time as an indicator that can be measured and compared. The analysis of disparities between different units (for instance, between different countries) results in a one static dimensional view of the analyzed variable(s) (for instance, Per Capita Income).. The degree of disparities of the studied situation may be very different when incorporating the time measurement, and the dynamic at stake, to complement the conventional static measurements (Sicherl, 1998). Sicherl (2006) derived from the time matrix presentation of the time series a novel statistical measures: the S-timedistance measure, as being the time difference for a given level L of the variable XL: Sij (XL) = ti(XL) – tj(XL)
(4)
This statistical S-time-distance (S stands for Sicherl) measure is intended to enhance the analytical framework of the time series comparisons process by adding a new dimension of analysis: the time dimension (Sicherl, 1998b). Based on existing time series data, the S-time-distance offers a new perception (time distance) of the data, offering to the comparative dynamic analysis a new and complementary instrument that brings new insights, in addition to static measures and a general presentation tool (Sicherl, 2004a). This generic concept of Time-Distance Analysis is applied in a variety of domains: economic and social development (Sicherl, 2001, 2002a, 2004d, 2004e, 2004f, 2004g), social indicators (Sicherl & Vahčič, 1999), information society (Sicherl, 2005), monitoring implementation of development goals (Sicherl, 2007), and other socio-economic domains (Sicherl, 2004a, 2004b, 2004c). Granger and Jeon (2003) used the concept of TimeDistance as a criterion for evaluating forecasting models. It is used to analyze a variety of problems “in time series comparisons, regressions, models, forecasting and monitoring, the notion of time distance was always there as a hidden dimension” (Sicherl, 2004a, p. 14). Time-Distance Analysis is a supplementary statistical tool that complements the existing conventional statistical tools. This novel tool provides a new perception of the disparities and differences in time between the studied units (countries here). It should help us to better identify the uniformity dimension (the status of disparity and difference of the project management deployment when observed within many various countries or groups—time distance) of our questions and to benchmark the studied countries based on time space and not only on the level space (static ratio) of project management deployment.
Research Design Project Management Deployment Index (PMDI) Our paper is proposing to address project management deployment by applying: •
For data collection: A simple new defined indicator based only on existing data. 3 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
•
For data analysis: Time-Distance Analysis.
We suggest the use of a simple designed indicator that represents the project management deployment level dimension of our questions by the use of already existing individual data that facilitate the measurement process. In order to build this indicator we use the concept of certification. The meaning of certification is clear and recognized internationally for the organizations issuing the certifications and for the practitioners and organizations. This indicator we propose is called Project Management Deployment Index (PMDI), defined as follow: PDMI represents the level or the degree of deployment of project management within a country (or group) by dividing the total number of the certified individuals within this country (or group) by the total population of that country (or group). This—the number of certified individuals worldwide, reaching about 350,000 by the end of 2006 (PMI and IPMA announced about 280,000 certified individuals in 2006)—reflects the direct implication and personal interest of the individuals of the community for project management. It also reflects the indirect and high level commitment of the group in terms of budget, resources, infrastructure, efforts, and so forth to support and maintain the development of project management. The credibility and pertinence of the number of certified individuals to represent the degree or level of project management deployment is supported when we consider some various economic, social, and other indicators used and largely adopted by the major international organizations, such as the World Bank and the United Nations. Indicators such as gross domestic product (GDP) per capita, GDP per employed, research and development in industry, personal computer use, Internet use, telecommunication use, and much more, are using generally the same simple approach of dividing the significant population by the total population of the studied group (country). Sicherl (2001), for example, considered some of the abovementioned indicators and proceeded with the TimeDistance Analysis approach to analyze the gap in research and development between the U.S., Japan and the EU. Another study made by Sicherl (2005) based on personal computer use and Internet use indicators has been conducted with the time analysis theory to compare the EU countries. The total population defines the total number of individuals within the studied countries (or groups). For the purposes of this paper, we considered the total midyear populations of the studied countries collected from the U.S. Census Bureau, International Data Base, available at http://www.census.gov/ipc/www/idb/idbsprd.html on August 5, 2007. The PMDI is a simple and clear Index that identifies the degree or level of deployment of project management by a country or group. For example, France recorded in 2000, 180 PMPs and 61.172 million people. So, its PMDI is PMDIFRA = (180/61.172) = 2.943. For 2000, the PMDI of the U.S. was PMDIUSA = 35.415.
Choice of Data The data about certifications can be collected from two main international sources: PMI and IPMA. PMI provides data about the number of PMPs in 131 countries for 1998 through 2006 (Data delivered to us upon request by e-mail on September 27, 2007 by the Bernard Smith Operations Reporting Administrator. Project Management Institute). The data for IPMA certifications were collected from IPMA Certification Yearbook 2005 (p. 16-21) for about 35 countries and for 1992 through 2005, with an estimate for 2006. Based on this data, we have built the PMDI indexes for 34 countries for 1998 through 2006. A comparative analysis of the two sets of data shows differences in: •
•
Major countries of deployment: The major countries of deployment for PMI, representing the highest presence of PMPs, are different from those with the highest number of IPMA-certified individuals. The U.S. had about 116,000 PMPs in 2006, but only 230 IPMA-certified individuals, and the U.K. had 3,261 PMPs and 17,214 IPMA-certified individuals. There is no significant correlation between the available two time series; however, the trend of the coefficient of correlation (r) is a straight line (y = 0.014x-0.059 – R² = 0.959). The two measures have a tendency to converge: the countries that have PMI certifications have an increasing tendency to also have IPMA certifications, and vice versa. 4 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
Taking into consideration these results, we have decided, for the purposes of this paper, to conduct the study with only the set of data available from PMI. One of the main reasons is that PMI has a wider international presence than IPMA and has awarded the largest number of certifications at the global level. We know that this choice leads to some bias for some countries (under-representation of Germany, Russia, and the U.K.), but this has no affect in the following analysis. We have selected 15 countries for this study. The countries are selected to be representative of the major economical and social actors in the international market. We included the following groups of countries: •
• •
The G8 countries: Canada, France, Japan, Germany, Italy, Russia, the U.K., and the U.S. They constitute an international forum and, together, these countries represent about 65% of the world economy (Wikipedia, 2007). The selection of these countries was based only on their economical size and their presence at the international level as the most developed countries. The European G6 countries: France, Germany, Italy, Poland, Spain, and the U.K. The selection of these countries was based only on geographical and historical factors. They constitute the largest European countries in terms of population and economies. The Outreach 5 or O5 countries (Myatt, Savao, Torney, & Zommers, 2007): Brazil, China, India, Mexico, and South Africa, also called the “emerging powers” (Sevallos, 2007, p. 1). The selection of these countries was based on their significant growth rates and economic readiness as the most important developing countries.
The first two groups overlap each other, but the comparisons within each group will offer a different perspective of the measured index. The comparison of the PMDI of these three groups of countries should be of special interest, as it allows an inside view of the project management deployment level of each country and the degree of uniformity of deployment within the countries. The collected population sizes and the number of cumulative PMPs from 1998 to 2006 are presented in Table 1.
Results and Analysis The following analysis is based on the PMDI variable. The total number of certified individuals considered in the definition of PMDI means the cumulative absolute number calculated over time, not the number of persons certified per year. The results are presented, analyzed, and discussed according to the two comparative analysis methods introduced earlier, the classical statistical method and the Time-Distance Analysis method.
The “Classical” Perspective Based on the data presented in Table 1 we consider the indicator cumulative number of PMPs certified to date as a standard measurement, which is generally used by PMI to measure the absolute growth rate of project management deployment throughout the countries and regions. This deployment term is used in accordance with the terminology of this paper.
Cumulative Number of Certified PMPs , 1998–2006 We consider here the evolution of the cumulative number of certified PMPs as a conventional statistical measure for deployment, respectively for G8, G6, and O5 countries.
G8 Countries From Table 1, we notice the superiority of the U.S. in terms of the cumulative number of certified PMPs throughout the period 1998–2006. It is followed by Japan, with no important competition from any of the other G8 countries. Canada, Germany, the U.K., France, Italy and Russia follow in that order, but with important gaps. An important general jump of the indicator is first observed in 2003 and repeated in 2004 and 2005. The total number of certified PMPs doubled from 2002 to 2003, and from 2003 to 2004, and from 2004 to 2005.
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Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
G6 Countries Table 1 shows a jump in the cumulative number of certified PMPs starting in 2003 but it jumps even more in 2004 through 2006. The separation into three subgroups by order of cumulative number can be identified with Germany and the U.K. as the first subgroup (highest project management deployment), France and Italy as the second subgroup, and Poland and Spain as the third subgroup (lowest project management deployment).
O5 Countries The same jump is observed within the O5 group: starting in 2003, and increased in 2004 through 2006. China is leading with the highest cumulative number since 2003. Two subgroups can be identified: China and India (highest project management deployment) followed by Brazil on the one hand, and South Africa and Mexico on the other hand (lowest project management deployment).
Project Management Deployment Index, 1998–2006 We are now using PMDI based on data presented in Table 2. Here, PMDI is presented in a classical perspective, which is used to measure the relative growth rate of project management deployment in the studied countries. By using PMDI, the measurement of the cumulative number of certified PMPs variable within a country is no more an absolute value but it is now relative to the country’s population size. This gives a better comparison between the units. Table 2 with PMDI allows a different interpretation of the data and of project management deployment.
G8 Countries The U.S. is no longer the leading country for project management deployment as shown in previous results. Canada is recording the highest PMDI, from 1998 through 2006. Canada and the U.S. constitute the two major countries leading the G8 and distancing Japan, the following country. The U.K. and Germany follow with PMDI2006 at 53 and 40, respectively, but far behind Japan at 144. Italy and France follow with 20 and 19, respectively. Finally, Russia recorded PMDI2006 = 2, the lowest PMDI index in 2006 from the G8 countries. Considering PMDI, we observe the same jumps starting in 2003 and accelerating in 2004 as we noticed previously using cumulative numbers of certified PMPs.
G6 Countries Table 2 shows an equivalent separation in the same subgroups in project management deployment as we noticed previously using the cumulative number of certified PMPs. In fact, for the G6 countries, we observe U.K. and Germany as a first subgroup with values of 53 and 40, respectively. Following is Italy with 20 and France with 19, then Poland (15) and Spain (10), constituting the three subgroups of the G6 countries.
O5 Countries If we consider now the PMDI for the O5 countries, we note again the jump observed in 2003 and later, as well as the grouping into two subgroups: Brazil and South Africa recording 2006 PMDI values of 24 and 20, respectively, and China, India and Mexico recording 12, 9, and 8, respectively, for 2006. Here the two approaches (cumulative number of certified PMPs and PMDI) show a different level of deployment between countries within this group.
6 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
Table 1. Cumulative certified PMPs and population in millions – 1998/2006 1998 Country Grou p
PMPs
1999
Population
PMPs
2000
Population
PMPs
2001
Population
PMPs
2002
Population
PMPs
2003
Population
PMPs
2004
Population
PMPs
2005
Population
PMPs
2006
Population
PMPs
Populatio n
Brazil
O5
24
170.956
60
173.294
124
175.553
243
177.753
422
179.914
783
182.033
1,265
184.101
3,843
186.113
4,673
188.078
Canada
G8
523
30.629
864
30.957
1,482
31.278
2,393
31.593
3,451
31.902
5,386
32.207
7,409
32.508
12,573
32.805
15,073
33.099
China
O5
0
1,250.366
8
1,260.107
50
1,268.853
86
1,276.883
282
1,284.276
2,688
1,291.496
5,328
1,298.848
9,882
1,306.314
France
G8, G6
25
60.572
76
60.860
180
61.172
245
61.514
294
61.864
445
62.206
603
62.569
918
62.947
1,201
63.329
Germany
G8, G6
30
82.024
88
82.075
207
82.188
397
82.281
528
82.351
876
82.398
1,423
82.425
2,648
82.431
3,361
82.422
India
O5
15
969.153
44
986.477
77
1,004.124
156
1,021.967
320
1,039.691
1,093
1,057.504
2,744
1,075.473
8,186
1,093.563
Italy
G8, G6
5
57.550
30
57.604
68
57.719
111
57.845
154
57.927
288
57.998
484
58.057
808
58.103
1,160
58.134
16,909 1,313.974
10,533 1,111.714
Japan
G8
63
126.246
293
126.494
503
126.729
872
126.972
1,225
127.187
3,348
127.358
6,841
127.480
14,796
127.537
18,413
127.515
Mexico
O5
11
97.325
46
98.617
84
99.927
127
101.247
165
102.480
230
103.718
354
104.960
678
106.203
915
107.450
Poland
G6
0
38.669
0
38.666
2
38.654
12
38.644
22
38.626
151
38.603
301
38.580
512
38.558
589
38.537
Russia
G8
0
147.813
0
147.352
9
146.710
16
145.990
19
145.163
35
144.308
80
143.507
198
142.776
292
142.069
South Africa
O5
47
43.335
64
43.746
87
44.066
108
44.296
133
44.434
239
44.482
372
44.448
743
44.344
913
44.188
Spain
G6
7
39.906
14
39.953
27
40.016
51
40.087
61
40.153
92
40.217
147
40.281
331
40.341
423
40.398
U.K.
G8, G6
47
59.036
108
59.293
212
59.522
377
59.723
580
59.912
984
60.095
1,544
60.271
2,693
60.441
3,261
60.609
U.S.
G8
3,590
276.115
5,779
279.295
9,999
282.339
16,532
285.024
23,043
287.676
35,254
290.343
51,171
293.028
95,775
295.734 115,933
298.4442
145,854
183,249
Totals:
3,888
6,670
11,753
19,576
27,670
47,289
73,922
Table 2. Project Management Deployment Index (PMDI) – 1998/2006 Country
Group
1998
1999
2000
2001
2002
2003
2004
2005
2006
Brazil
O5
0.14
0.35
0.71
1.37
2.35
4.30
6.87
20.65
24.85
Canada
G8
17.08
27.91
47.38
75.75
108.17
167.23
227.91
383.26
455.39
China
O5
0.00
0.01
0.04
0.07
0.22
2.08
4.10
7.56
12.87
France
G8, G6
0.41
1.25
2.94
3.98
4.75
7.15
9.64
14.58
18.96
Germany
G8, G6
0.37
1.07
2.52
4.82
6.41
10.63
17.26
32.12
40.78
India
O5
0.02
0.04
0.08
0.15
0.31
1.03
2.55
7.49
9.47
Italy
G8, G6
0.09
0.52
1.18
1.92
2.66
4.97
8.34
13.91
19.95 144.40
Japan
G8
0.50
2.32
3.97
6.87
9.63
26.29
53.66
116.01
Mexico
O5
0.11
0.47
0.84
1.25
1.61
2.22
3.37
6.38
8.52
Poland
G6
0.00
0.00
0.05
0.31
0.57
3.91
7.80
13.28
15.28
Russia
G8
0.00
0.00
0.06
0.11
0.13
0.24
0.56
1.39
2.06
South Africa
O5
1.08
1.46
1.97
2.44
2.99
5.37
8.37
16.76
20.66
7 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries Spain
G6
0.18
0.35
0.67
1.27
1.52
2.29
3.65
8.20
U.K.
G8, G6
0.80
1.82
3.56
6.31
9.68
16.37
25.62
44.56
53.80
U.S.
G8
13.00
20.69
35.41
58.00
80.10
121.42
174.63
323.86
388.46
1.08
1.84
3.21
5.29
7.42
12.56
19.46
38.07
49.39
PMDI SC Average (SC = Selected Countries)
8 © 2008 Project Management Institute
10.47
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
Comparative Analysis Using Static Measure We undertake a comparative analysis of the PMDI between the considered countries during the period 1998–2006 using the classical approach.Data are presented in Table 2. We consider the group G8, as it shows important variations between its countries, ranging from Canada with a PMDI2006 = 455 to Russia with a PMDI2006 = 2. Table 3 shows for 2006 Canada in the lead, and the U.S. with the highest percentage difference, followed by Japan, the U.K., Germany, Italy, France, and Russia. It is important here to mention the decrease of the percentage difference for the U.S. and Canada, while Japan is recording an increase with its percentage difference values. France shows an increase until 2000 than a constant decrease until 2006. Germany shows also an increase until 2001 than a certain stability until 2006. The U.K. shows an increase until 2002, then a decrease in 2005 and 2006. Italy and Russia are generally stable in their percentage levels since 2000. This can be interpreted as a general decline of project management deployment in the countries, including the U.S. and Canada, with possible stability in the U.K. and Germany, but with clear advancement in Japan. Table 3. Percentage difference between the G8 countries and the SC Average: PMDI, 1998–2006 Percentage difference(*) from SC PMDI Average Year
U.S.
Canada
Japan
Russia
France
Germany
Italy
U.K.
1998
1100.54
1476.67
-53.92
-100.00
-61.89
-66.23
-91.98
-26.49
1999
1025.19
1417.72
25.96
-100.00
-32.09
-41.69
-71.68
-0.95
2000
1003.80
1376.76
23.71
-98.09
-8.29
-21.50
-63.28
11.01
2001
995.68
1330.85
29.73
-97.93
-24.76
-8.85
-63.75
19.24
2002
980.23
1358.83
29.89
-98.23
-35.91
-13.53
-64.15
30.55
2003
866.67
1231.36
109.29
-98.07
-43.05
-15.36
-60.47
30.36
2004
797.25
1071.03
175.72
-97.14
-50.48
-11.30
-57.17
31.62
2005
750.75
906.82
204.76
-96.36
-61.69
-15.61
-63.47
17.05
2006
686.45
821.96
192.34
-95.84
-61.61
-17.44
-59.60
8.93
(*):Percentage difference between units I and J at the time t: Pij(t) = [Xi(t)/Xj(t) – 1]*100. Example : Percentage difference for France in 2003 = [PMDIFRANCE/PMDISC-Average -1]*100 = [7.15/12.56-1]*100 =-43.05 (rounded figure)
The analysis and discussion of the results obtained with the PMDI have shown us some of the possibilities offered with the conventional statistical measurement methods implemented and largely used for years by professional bodies and the research communities.
Time-Distance Analysis Perspective (S-Time Distance) In this section we deal with the same data from Table 2, and we apply the S-Time Distance approach. Table 2 has been transformed first into a Time Matrix table with two dimensions: Country (Unit) names & PMDI (indicator) values. The PMDI values considered are those contained within the interval [0.00; 456.00]. For each selected country, we indicate the years where the PMDI values were recorded. For example, Germany got a PMDI = 32.12 in 2005, and PMDI = 40.78 in 2006. Based on the Time Matrix table, we calculate the S-Time Distance, taking into account the following rules and assumptions: • •
We consider the selected countries’ PMDI average (see Table 2), calculated for the period 1998–2006, as the reference unit to calculate the S-Time Distance. The PMDI SC average of 2006 (PMDI SC average2006), which is the reference point of calculation, was found to be 49.39, as indicated in Table 2. We base our calculation on the PMDI values rounded to two decimal points, but we round the PMDI values to one decimal point when we calcutate the Time Matrix. 9 © 2008 Project Management Institute
Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
•
We assume that the PMDI variable is uniformly distributed between two observed PMDI values for the same country. This allows us to use the formula of interpolation we adopted to calculate the missing time stamps when needed.
S-Time Distance Calculation and Table Formula (4) gives: S-time-distance: S (Xt) = t(Xt) – T(Xt) for a given level Xt Where S (Xt) = actual time t – time on the line to target T for each actual value of the variable Xt For example, in 2006: U.S.: S (49.39) = 2000.62 (49.39) - 2006 (49.39) = -5.38 years. This means that the U.S. is 5.38 years ahead compare to the PMDI Selected Countries average (SC Average) in 2006. The SC Average is 49.39 in 2006, and the U.S. reached a PMDI of 49.39 in 2000.62 that is 5.38 years ahead the SC Average in 2006. Italy: S (19.95) = 2006 (19.95) – 2004.02 (19.95) = 1.98 years This means that Italy, in 2006, is 1.98 years behind the SC Average. Based on this calculation principle and on the data in Table 2, we get Table 4, presenting the S-Time Distance (in years) for each country for the period 1998–2006. The “-” indicates time lead (ahead), the “+” indicates time lag (behind). The value (x) indicates unavailable data for the related country. Table 4. S-Time Distances (in years), 1998–2006 SC Average1999 -2.45
SC Average2000 -3.27
SC Average2001 -4.00
SC Average2002 -4.73
SC Average2003 -5.05
SC Average2004 -5.16
SC Average2005 -4.88
SC Average2006 -5.38
U.S.
G8
SC Average1998 -1.55
Canada
G8
-1.48
-2.42
-3.29
-4.09
-4.90
-5.42
-5.78
-5.48
-5.93
Japan
G8
0.86
-0.28
-0.47
-0.55
-0.81
-0.82
-1.41
-1.57
-2.16
U.K.
G8,G6
0.43
0.00
-0.18
-0.36
-0.68
-0.57
-0.88
-0.34
-0.48
Germany
G8,G6
1.00
1.00
0.50
0.24
0.71
0.40
0.32
0.32
0.76
Brazil
O5
1.43
2.14
2.57
1.57
2.64
2.52
2.24
0.94
1.72
South Africa
O5
0.00
0.43
0.86
1.57
2.14
1.95
1.81
1.41
1.94
Italy
G8,G6
1.43
1.86
1.86
1.93
2.36
2.14
1.83
1.81
1.98
France
G8,G6
1.00
0.71
0.21
0.67
1.29
1.14
1.58
1.71
2.07
Poland
G6
x
x
3.43
4.14
4.71
2.67
1.92
1.90
2.61
China
O5
x
x
x
4.43
5.29
3.80
3.57
2.96
2.96
India
O5
x
x
3.43
4.29
5.14
5.14
4.43
2.98
3.60
Mexico
O5
1.43
1.86
2.43
2.71
3.29
3.71
3.90
3.48
3.79
Spain
G6
1.29
2.00
2.57
2.71
3.43
3.64
3.76
2.85
3.40
Russia
G8
x
x
3.43
4.43
5.43
6.29
6.71
6.57
6.80
S-Time Distance: discussion of the situation in 2006 Figure 1 shows the S-Time Distances for the selected countries in 2006.
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Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
S-Time Distance between SC average and the selected countries: PMDI, 2006
Russia Spain Mexico India China Poland France Italy South Africa Brazil Germany United Kingdom Japan Canada USA -8.00
-6.00
-4.00
-2.00
0.00
2.00
4.00
6.00
8.00
S-Time Distance / years: -time lead, +time lag
Figure 1. S-Time Distances for the selected countries – 2006 The S-Time Distances shown in Figure 1 give a different perception of the evolution of the PMDI during the period 1998–2006. It allows us to easily see the position of each country compared to the others using a universal and understandable unit, which is time. For the G8 countries, Canada is almost 6 years ahead of the average of the studied countries, followed by the U.S. with 5 years and 4 months ahead. Japan, with all the results observed with the conventional statistical measurement tools, and despite its being 2 years and 2 months ahead of the average, still has a ways to go to catch Canada and the U.S. In 2006, Russia is far behind the considered average. And we can say by how much it is behind: 6 years and 10 months. Mexico, India and China are around 3 years behind the average, and Brazil and South Africa of the same group of O5 countries are less than 2 years behind the average. For the G6 countries, we see that all of them are well behind the average, except for the U.K., recording the best score, 6 months ahead. Germany is 9 months late, Italy and France are around 2 years behind the average. Poland is 2 years and 7 months and Spain is 3 years and 5 months behind the average. In 2006, the U.K. leads France by two and a half years, and Germany leads Poland by almost 1 and a half years. Within the studied countries, the time distance between the most ahead country and the most behind country is more than 12 years. The policies makers and strategists of countries and professional bodies concerned about project management and its deployment can rapidly evaluate the situation, which is not fully described using the classical approach and based on the results can consider actions to deploy.
S-Time Distance and Dynamic of Evolution We consider now the evolution of S-Time Distance over the period 1998–2006 for each group.
G8 Countries We can see (Table 4) the decrease in the S-Time Distances for Canada and the U.S. during the period 1998–2006, which means an increase in the time lead between these two countries and the other countries of the G8. Japan is decreasing also during this period, mainly since 2003. This means that Japan is closing the gap with Canada and the U.S. The U.K. and Germany are more stable, with about a half year ahead of the average for the U.K. ahead and a
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Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
half year behind the average for Germany. France and Italy are getting later (time lag is increasing), far behind the other G8 countries.
G6 Countries We notice the trend observed since 2003 – an improvement of the S-Time distance for allthe countries of G6, but not for France, which shows a continuous trend far from the average since 2000 (time lag is increasing). A global deterioration of the S-Time distance in 2006 is recording for all the G6 countries, behind the average of the studied countries, except the U.K.
O5 Countries One can see the group made of Brazil and South Africa ahead of the group made of China, India and Mexico. Enhancement of the S-Time distances of all the O5 countries shows in 2005, but again deterioration is registered in 2006. The years 1999–2002 recorded a period of a growing gap, followed by a period in 2003 and 2004 of approximate stagnation, and then 2005, when the gap started to reduce. Based on the above S-Time distance analysis, we can state some important observations about the trends of the various studied countries in terms of project management deployment: • • •
Canada, the U.S., and Japan are increasing more and more their time leads over the other countries of the studied groups. Japan is behind Canada and the U.S., but during the studied period it shows important enhancement and maintaining of the time difference. The main European countries of G6 are showing a growing time lag with the average and with the leading countries of Canada, the U.S., and Japan. The O5 group countries are facing difficulties to close the time gap with the other countries.
Two-Dimensional Overall Degree of Disparity One of the main advantages of the Time-Distance Analysis concept is its general framework, offering an overall degree of disparity view. It takes into account the integration of the conventional static measures as well as the “novel” time-distance measure. Both are considered statistical measurements tools, but offer complementary views and perception of the situation. While our percentage difference analysis of Table 3 stated that the U.S. and Canada seem to be declining in terms of project management deployment (because of the observed decreasing percentage differences), Table 4 shows that the U.S. and Canada are recording important increases in time-distance compared to the other countries. The perception given by the percentage difference measure is different from the perception given by the S-time-distance measure. The two measures evolve differently: the static measure decreases while the S-time-distance increases. With Japan, the two measures evolve the same way: the percentage difference and the S-time-distance are both increasing and supporting the interpretation of good performance for Japan. Table 5 present a summary of the results observed for both measures for the G8 countries.
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Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
Table 5. Percentage difference and Time-Distance Analysis summary for the G8 countries: PMDI, 1998–2006
U.S. Canada Japan Russia France Germany Italy U.K.
Percentage Difference Analysis with SC-Average (see Table 3) Classical approach Decreasing, showing decline from 1100 (in 1998) to 686 (in 2006) percent Decreasing, showing decline from 1476 (in 1998) to 821 (in 2006) percent Increasing, showing improvement from -53 (in 1998) to 192 (in 2006) percent Almost stable around -100% showing very slow participation Increasing (until 2000) and decreasing after (-61% in 2006). Always under the average. Increasing (until 2001) and decreasing after (-17% in 2006). Always under the average. Increasing (until 2000) and almost stable after (-59% in 2006). But always under the average. Increasing, showing improvement from -26 (in 1998) to 31 (in 2004), than decreasing until 8% in 2006.
Time Distance Analysis with SC-Average (see Table 4) TDA approach Increasing, showing time lead growing from 1.55 (in 1998) years to 5.38 years (in 2006) Increasing, showing time lead growing from 1.48 (in 1998) years to 5.93 years (in 2006) Increasing, growing from 0.86 year behind the average (in 1998) to 2.16 years ahead the Average in 2006. Decreasing, showing lag time from 3.43 years in 2000 to 6.8 years in 2006 Increasing until 2000 with 0.21 years behind the average, and decreasing after (2 years lag time in 2006) Increasing until 2001 with 0.24 years behind the average, and decreasing after (0.76 years lag time in 2006) Decreasing, showing lag time between 1.43 years (in 1998) and 2.14 years (in 2004), and almost stable after (1.98 years lag time in 2006) Increasing until 2004 with 0.88 years ahead of the average, and decreasing until 0.48 years lead time in 2006.
When reading the time-distance measures in Table 5, the terms Increasing and Decreasing should be interpreted as follows: • •
Increasing: means enhancement of the time difference, that is, getting lower in arithmetic value Decreasing: means deterioration of the time difference, that is, getting higher in arithmetic value
These different perceptions are not contradictory, but complementary, as they offer an overall view of the disparity between the compared countries. A more detailed analysis could be made to explain this difference, which is mainly related to the growth rate of each of the concerned countries, but we will not consider this issue further here, as it is beyond of the scope of this paper. One should consider that the static measurement alone cannot deliver this new perception of the disparity. It needs to be complemented by the Time-Distance Analysis dimension to deliver a full picture of the studied case. The S-time distances analysis offers a new view of the data disparities presented in time differences between the studied countries: • • • •
First, it provides to the experts another perspective of the approaches to proceed, with further analysis to understand the reasons behind these differences. Second, it offers to the decision makers a new way to perceive the measurement of the result of their strategies and to set new targets based on the time concept. Third, it makes available to a wider public an understandable comparative analysis based on a well known standard unit of measurement—time. This new view complements the classical statistical tools with a valuable time measurement-oriented tool that is easy to evaluate, interpret, and analyze.
Limitations We must state that the central indicator of the present study, PMDI, as defined in this study, should be considered carefully with the following three main limitations. 1.
The Project Management Deployment Index design should probably be enhanced and improved. In this paper, we are using individual professional certifications as key variables for measuring project management deployment. Other factors should probably be integrated, as, for instance, it is assumed by
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Investigating the Deployment of Project Management: A Time-Distance Analysis Approach of G8, European G6, and Outreach 5 Countries
2.
3.
some PMI studies (2001) that about 20 million people around the world are dealing with projects. So here a clear limitation in getting a full picture of project management deployment. Furthermore, PMDI has been calculated based only on PMI’s PMP certifications, and as such: • It represents only part of the population of the certified people around the world. This means that our data is not complete, and it needs to be supplemented by other data from other certification programs. But considering the important size of the population certified by PMI, with nearly 242,000 PMPs in over 160 countries (PMI, 2007d), we considered our data to be representative and supportive of the results found. For information purposes only, IPMA is operating in almost 40 countries and had more than 45,000 certificates awarded (at four levels) at the end of 2005 (IPMA, 2006). • It reflects the weight of the PMP certification, which may differ from other certification programs’ numbers. If the data presented in this paper are homogenous (one level), as coming from the same source, any future mix of data coming from different sources may have to consider a number and level harmonization process before integration. The Time-Distance Analysis is applied within the scope of this paper on the data of the 15 selected countries and, accordingly, the results obtained are closely linked to their average. Any modification in the list of the studied countries could have implied a modification of the referenced average and consequently could have changed the results. Moreover, the obtained results are directly related to a reference unit, which can be the average of the studied countries as in our case, but it can be any other decided reference unit if so decided by the researcher. The detailed explanation of this issue is beyond of the scope of this paper and can be found in the Time-Distance Analysis literature.
Conclusion This paper introduces the PMDI as a project management deployment measurement indicator based on the certification concept. This indicator is simple and clear. It relies on existing data available from recognized and trusted international bodies such as PMI, IPMA, and the U.S. Census Bureau. The application of the Time-Distance Analysis on the obtained PMDI time series suggests a new approach to measure the project management deployment level and uniformity, as presented at the beginning of this paper. The Time-Distance Analysis offers a different perspective of the project management deployment field than the classical statistical methods, and highlights new insights that were hidden before. The simplicity of the PMDI, allied with the power of the Time-Distance Analysis, should open interesting analysis opportunities for decision makers and strategists aiming to understand how, why, and for which socio-economic results the project management phenomenon propagate inside a group (country, region, or socio-group), and to improve the comparative analysis between groups. The groups can be modified to represent regions, organizations, or professions. Other socio-economic indicators can be added and analyzed along with the PMDI. Reference unit can be set to define targets or to perform simulations. A variety of combinations is offered, exposing the project management deployment field to new analysis views and in-depth studies that allow better understanding and management. In parallel, further studies dedicated to PMDI definition refining and validation as a concept, and to other applications of the Time-Distance Analysis theories on PMDI should be undertaken in order to promote and support the use of this duo-tool, PMDI-TDA.
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