The impact of project management on SME productivity

University of Technology, Sydney

Daniel Adler

University of Technology, Sydney

Abstract

A fundamental assumption of project management practice and research is that using project management to achieve organizational objectives improves organizational performance. However, there is little published research that has directly questioned this assumption. The research presented in this paper addresses one aspect of organizational performance by focusing on reported changes in organizational productivity, use of project management, and use of other comparable business skills.

This paper tests the hypothesis that using project management increases the productivity of small to medium enterprises. The data used to test this hypothesis comes from two longitudinal databases created by the Australian Bureau of Statistics which contain data on Australian businesses with less than 200 staff. This data was used to create models of the relationship between productivity and business skills using binary logistic regression. The models demonstrate that project management has a significant impact upon productivity.

Keywords: productivity; small to medium enterprises; longitudinal study; business skills

Introduction

A fundamental assumption of project management practice and research is that using project management to achieve organizational objectives improves the performance of that organization. This assumption is so ingrained that it appears to be self-evident. If it were otherwise, there would be little reason to justify the considerable expense that many organizations go to in developing and maintaining project management systems and certifying staff in external standards, or to justify the not inconsiderable intellectual effort applied by academics and researchers around the world to develop and refine project management theory and practice.

A wide variety of authors comment that project management has a positive effect on aspects of an organization’s success. Whether this is broadly expressed in terms of the impact on overall productivity (Cleland, 1984; McHugh & Hogan, 2011), performance (Abbasi & Al-Mharmah, 2000), efficiency (Stimpson, 2008), or effectiveness (Shenhar, Dvir, Levy, & Maltz, 2001), the underlying assumption is that it is good business to use project management to achieve organizational objectives. However, this assumption typically remains unexamined.

With the exception of research by Thomas and Mullaly (2008) and Lappe and Sprang (2014), there is little research in the literature which directly questions whether project management leads to increased organizational performance. Responding to Hällgren’s (2012) call for research which challenges the fundamental assumptions on which project management research is based, the research presented in this paper questions whether the use of project management as a core business skill does actually have an impact on an organization’s productivity.

In addition, and in contrast to some of the prevailing tendencies in project management research to focus on large scale projects, this paper focuses on the use of project management in small to medium enterprises (SME). SMEs account for a large proportion of the projects that are undertaken, and it has also been identified that project management is critical to the survival of small organizations (Sádaba, Pérez-Ezcurdia, Lazcano, & Villanueva, 2014). However, surprisingly little research has focused on the ways in which project management is used in SMEs.

Literature Review

A large proportion of the research literature focuses on ways in which project management can be improved. Implicit in this is the assumption that project management is good for business, and that with improved project management come ancillary benefits to the greater organization. Ng, Skitmore, Lam, and Poon (2004) provide one example that focuses on factors that affect productivity on projects, while Reyck, Gurushka-Cockayne, Lockett, Calderini, Moura, and Sloper (2005) have examined the impact of a robust portfolio management system on project success. Research has also demonstrated that there is a link between the maturity of project management processes and project success (Mir & Pinnington, 2014). However, these studies remain broadly at the project level, with any implication for impact at the organizational level left solely to implication.

Other authors have looked more broadly at the impact on the organization as a whole. For instance, Ozcelik (2010) reported on the impact of one particular kind of project on overall business performance. Lappe and Sprang (2014) have also developed a model to determine whether investment in project management provides a significant return. Their research was based on 251 projects from a German life insurance company, and showed a clear relationship between the costs associated with project management and resultant benefits.

Research by Thomas and Mullaly (2008) also endeavored to understand the return on investment from project management. This study was hampered by few of their 65 participant organizations actively collecting data on the return from their investment in project management capability. Their research was, however, able to demonstrate that more than half of their case study organizations derived tangible value from the implementation of project management.

SMEs provide the context in which the question of the impact of project management on business productivity will be examined this paper. A large proportion of project management research focuses on large projects (e.g. Eweje, Turner, & Müller, 2012; Chang, Chih, Chew, & Pisarski, 2013; Winch, 2013) and this is understandable. There is considerable glamour associated with the large amount of money spent on such projects, and the spectacular successes and failures of these endeavors make for entertaining reading. However, a disproportionately large focus on mega-projects can lead to overestimation of both their impact on the economy and their prevalence. Mega-projects may be more dramatic, but they remain the minority of projects executed compared to those undertaken by much smaller organizations.

The importance of the SMEs to the social and economic health of economies around the globe has been widely recognized and researched for some time (Ayyagari, Beck, & Demirguc-Kunt, 2007; Beck, Demirguc-Kunt, & Levine, 2005; Schiffer & Weder, 2001). It was found recently that SME’s make up between 70% and 90% of all enterprises in Organisation for Economic Co-operation and Development (OECD) countries and are important drivers of innovation and growth, accounting for between 40% and 70% of value added by the business sector, as well as being critical providers of goods and services to larger organizations (OECD, 2013a; 2013c).

Australia provides one example of importance of SMEs in an OECD country, where at least one million SMEs were found to be actively operating in 2012. This comprised more than ninety percent of all active businesses and accounted for the majority of employed people, with the productivity of this sector critical to the welfare of the Australian economy (ABS, 2012a; 2012c). Recent surveys of Australian SMEs by the Australian Bureau of Statistics (ABS) have found that one in eight of all SMEs, and one in five innovation active SMEs value project management as a core skill. Interestingly, project management was more valued than either engineering or scientific and research skills by respondents (ABS, 2013a).

One key aspect influencing productivity of businesses is human capital, of which management skills are a core component (OECD, 2001). In a comprehensive review of the literature on the link between investment in human capital and productivity, the Australian Workforce and Productivity Commission found that investment in management and leadership skills was positively associated with better performing businesses (AWPC, 2013). Most research into improving the performance of SMEs in relation to this, though, has tended to be focused on entrepreneurship and innovation with the management of project related activities submerged in general business discussions around sales and marketing, accounts, human resource management, and information technology (Hudson, Smart, & Bourne, 2001; Turner, Ledwith, & Kelly, 2009; Turner, Ledwith, & Kelly, 2010).

However, along with human, marketing, financial, and general business management skills, project management has been identified as a valuable skill for SMEs (Lo & Humphreys, 2000; Turner, Ledwith, & Kelly, 2012). Project management as a competency critical to business success has been known for some time now, and a comprehensive framework for measuring this in individuals have been developed and administered by professional associations across the world (Kerzner, 2013; Turner, 1999). However, measurement of the impact of this on productivity has been limited to evaluations of individual managers rather than the importance of project management on productivity at a business unit level (Crawford, 2005).

Method

The data for this research is found in the Australian Bureau of Statistics Business Longitudinal Database (BLD). The BLD focuses on increasing the understanding of characteristics and factors that affect business performance. Each year, starting in the 2004–05 Australian financial year, a panel of businesses has been selected as representative of the Australian business population.

Panel members are requested to respond to a survey for five consecutive years, with no new panel members added after the survey has been initiated. The size of each panel has been determined based on the anticipated drop-off rate of survey respondents to ensure that there are a sufficient number of respondents remaining in each industry and business size classification at the end of the five year period. At the time of writing, three panels of the BLD had been released. The survey covering the years 2004-05 to 2009-10 (panel 2) and 2006-07 to 2010-11 (panel 3) both included questions relating to project management as a core business skill (ABS 2012b, 2012c), and are the focus of this research.

The BLD exclusively focuses on actively trading companies with fewer than 200 employees, and includes only those businesses with a simple structure and a single Australian Business Number. Other exclusion criteria also apply (ABS, 2013b), while many of the categories used in the survey have been developed based on the Oslo Manual survey guidelines (OECD/Eurostat, 2005) for measuring innovation in business. The survey questions have changed between and within surveys. As a result, it is not possible to directly compare results between panels, although many significant questions have maintained a similar focus.

Due to the constraints of this external dataset, this research has broadly worked with the classification of a SME as a trading company with fewer than 200 employees. Previous research by other authors has also been based on the data from the ABS BLD (ABS, 2014), focusing on topics such as innovation (Gronum, 2012; Huang, 2009; Bhattacharya, 2004), family business (Dharmadasa, 2009), outsourcing (Bakhtiari, 2013), performance (Steffens, 2009), industrial relations (Farmakis-Gamboni & Prentice, 2001), and entrepreneurship (Fitzsimmons, 2006).

Data Analysis

This analysis focuses on two groups of questions from each of the two databases referred to in this paper, as follows.

BLD 2004-05 to 2009-10 (panel 2):

  • Compared to the previous year, did any of the following decrease, stay the same or increase: Productivity
  • During the year ended 30 June, were any of the following types of skills used by the business in undertaking its core business activities (engineering, scientific and research, IT professionals, IT support technicians, trades, transport, plant and machinery operation, marketing, project management, business management, financial)

BLD 2006-07 to 2010-11 (panel 3):

  • Compared to the previous year: Productivity (decrease, stay the same, increase)
  • Skills used in undertaking core business activities (engineering, scientific and research, IT professionals, IT support technicians, trades, transport, plant and machinery operation, marketing, project management, business management, financial)

The first of each of these questions required respondents to select only one of the provided options. Responses to the questions on business skills were independent and could be treated as individual and separate questions for each business skill.

Is there a relationship between project management and productivity?

Analysis was conducted to understand whether there was any correlation between responses regarding a change in productivity and respondents’ use of project management.

For data contained in the Business Longitudinal Database, Australia, 2004-05 to 2009-10 a correlation (p<;0.01) was found between responses regarding businesses’ change in productivity and their use of project management. Response frequencies are provided below (Table 1).

Compared to the previous year - productivity
Decrease Stay the same Increase Total
Didn’t use PM 961 2270 974 4205
Used PM 82 253 204 539
Total 1043 2523 1178 4744

Table 1: Frequencies for use of PM (project management) and productivity change (2004-05 to 2009-10)

The difference in response rates becomes clearer when graphed (Figure 1), where it can be seen that 37.8% of respondents who used project management reported an increase in productivity from last year, compared to only 23.2% of respondents who did not use project management.

Frequencies for use of PM and productivity change (2004-05 to 2009-10)

Figure 1: Frequencies for use of PM and productivity change (2004-05 to 2009-10)

A similar pattern is repeated in the data contained in the Business Longitudinal Database, Australia, 2006-07 to 2010-11. A correlation (p<;0.01) was found between responses regarding businesses’ change in productivity and their use of project management. Response frequencies are provided below (Table 2 and Figure 2). In this dataset, 37.9% of respondents who used project management reported an increase in productivity from last year, compared to only 22.3% of respondents who did not use project management.

Compared to the previous year - productivity
Decrease Stay the same Increase Total
Didn’t use PM 1163 2603 1083 4849
Used PM 102 251 215 568
Total 1265 2854 1298 5417

Table 2: Frequencies for use of PM and productivity change (2006-07 to 2010-11)

Frequencies for use of PM and productivity change (2006-07 to 2010-11)

Figure 2: Frequencies for use of PM and productivity change (2006-07 to 2010-11)

In both of these datasets, approximately 15% more respondents who used project management reported an increase in productivity compared to last year, compared to those who did not use project management. This is a promising result; however, by itself, it is not sufficient to suggest that project management leads to greater productivity. Correlation does not imply causation, and relying on correlation alone, an alternate explanation could be that more productive businesses are those that are more likely to use project management, and perhaps other business skills.

Modeling the 2004-05 to 2009-10 dataset

In order to understand whether project management does have an impact on productivity, it was necessary to model the relationship between productivity and project management while controlling for other comparable variables. A model of the relationship between small business productivity and a range of core business skills, including project management, was created using binary logistic regression, based on data in the Business Longitudinal Database, Australia, 2004-05 to 2009-10. The model describes the respondents’ tendency to identify that the productivity of their business increased, as opposed to staying the same.

Response frequencies for the questions in used the model are as follows:

Question Response Frequency
Compared to the previous year, did any of the following decrease, stay the same or increase: Productivity Stayed the same 2523
Increased 1178
During the year ended 30 June, were any of the following types of skills used by the business in undertaking its core business activities:
Project management (PM) Yes 457
No 3244
Engineering (ENG) Yes 618
No 3083
Scientific and research (SCI) Yes 260
No 3441
IT professionals (ITP) Yes 708
No 2993
IT support technicians (ITS) Yes 819
No 2882
Trades (TRA) Yes 1016
No 2685
Transport, plant and machinery operation (MAC) Yes 982
No 2719
Marketing (MAR) Yes 920
No 2781
Business management (BUS) Yes 898
No 2803
Financial (FIN) Yes 1115
No 2586
Total selected cases 3701

The following hypotheses were created to understand the usefulness of this model and to understand the influence of particular business skills on productivity.

Null hypothesis 10-1 None of the coefficients in the model are significantly different from zero
Alternative hypothesis 10-1 At least one of the coefficients in the model is significantly different from zero and the model is useful
Null hypothesis 10-2 Project management skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-2 Project management skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-3 Engineering skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-3 Engineering skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-4 Scientific and research skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-4 Scientific and research skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-5 IT professionals have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-5 IT professionals have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-6 IT support technicians have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-6 IT support technicians have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-7 Trades have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-7 Trades have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-8 Transport, plant, and machinery operation skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-8 Transport, plant, and machinery operation skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-9 Marketing skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-9 Marketing skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-10 Business management skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-10 Business management skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 10-11 Financial skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 10-12 Financial skills have a significant effect on whether an organization reported an increase in productivity

Backwards elimination was used to exclude variables of less significance. The process of backwards elimination went through five steps, successively excluding the following independent variables in this order: ITS; ENG; MAC; and BUS. The final model was:

Logit(increase) = (0.237 * PM) + (0.267 * SCI) + (0.427 * ITP) + (0.155 * TRA) + (0.243 * MAR) + (0.299 * FIN) - 1.108

The significance of the final model was 0.000 using the Omnibus Tests of Model Coefficients. The final model accounts for 68.9% of the variation in the dependent variable. Using the Hosmer and Lemeshow Test, the model has a significance of 0.634 and a Chi-square score of 3.433 suggesting an acceptable goodness of fit. On this basis, the Null hypothesis 10-1 can be rejected, the alternative hypothesis can be accepted, and the model can be considered to be useful.

The significance of PM was 0.035 at Step 5. On this basis, the Null hypothesis 10-2 can be rejected and the alternative hypothesis accepted. It has been shown that Project management skills have a significant effect on the response, after controlling for other predictors in the model. The use of project management to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 26.7%, as opposed to their productivity staying the same.

The significance of ENG was 0.323 at Step 5. On this basis, the Null hypothesis 10-3 can be accepted. It has been shown that Engineering skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of SCI was 0.054 at Step 5. On this basis, the Null hypothesis 10-4 can be accepted. It has been shown that Scientific and Research skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of ITP was 0.000 at Step 5. On this basis, the Null hypothesis 10-5 can be rejected and the alternative hypothesis accepted. It has been shown that IT Professionals have a significant effect on the response, after controlling for other predictors in the model. The use of IT professionals to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 53.2%, as opposed to their productivity staying the same.

The significance of ITS was 0.992 at Step 5. On this basis, the Null hypothesis 10-6 can be accepted. It has been shown that IT Support technicians’ skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of TRA was 0.056 at Step 5. On this basis, the Null hypothesis 10-7 can be accepted. It has been shown that Trades have no significant effect on the response, after controlling for other predictors in the model.

The significance of MAC was 0.327 at Step 5. On this basis, the Null hypothesis 10-8 can be accepted. It has been shown that Transport, plant, and machinery operation skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of MAR was 0.007 at Step 5. On this basis, the Null hypothesis 10-9 can be rejected and the alternative hypothesis accepted. It has been shown that Marketing skills have a significant effect on the response, after controlling for other predictors in the model. The use of marketing skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 27.5%, as opposed to their productivity staying the same.

The significance of BUS was 0.226 at Step 5. On this basis, the Null hypothesis 10-10 can be accepted. It has been shown that Business management skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of FIN was 0.001 at Step 5. On this basis, the Null hypothesis 10-11 can be rejected and the alternative hypothesis accepted. It has been shown that Financial skills have a significant effect on the response, after controlling for other predictors in the model. The use of financial skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 34.8%, as opposed to their productivity staying the same.

Modeling the 2006-07 to 2010-11 dataset

A model of the relationship between small business productivity and a range of core business skills, including project management, was created using binary logistic regression, based on data in the Business Longitudinal Database, Australia, 2006-07 to 2010-11. The model describes the tendency of respondents to identify that the productivity of their business increased, as opposed to staying the same.

Response frequencies for the questions used in the model are as follows:

Question Response Frequency

Compared to the previous year: Productivity

Stayed the same 2854
Increased 1298
Skills used in undertaking core business activities:
Project management (PM) Yes 466
No 3686
Engineering (ENG) Yes 651
No 3501
Scientific and research (SCI) Yes 321
No 3831
IT professionals (ITP) Yes 827
No 3325
IT support technicians (ITS) Yes 952
No 3200
Trades (TRA) Yes 1066
No 3086
Transport, plant and machinery operation (MAC) Yes 1078
No 3074
Marketing (MAR) Yes 1069
No 3083
Business management (BUS) Yes 1042
No 3110
Financial (FIN) Yes 1319
No 2833
Total selected cases 4152

The following hypotheses were created to understand the usefulness of this model and to understand the influence of particular business skills on productivity.

Null hypothesis 11-1 None of the coefficients in the model are significantly different from zero
Alternative hypothesis 11-1 At least one of the coefficients in the model is significantly different from zero and the model is useful
Null hypothesis 11-2 Project management skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-2 Project management skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-3 Engineering skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-3 Engineering skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-4 Scientific and research skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-4 Scientific and research skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-5 IT professionals have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-5 IT professionals have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-6 IT support technicians have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-6 IT support technicians have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-7 Trades have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-7 Trades have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-8 Transport, plant, and machinery operation skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-8 Transport, plant, and machinery operation skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-9 Marketing skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-9 Marketing skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-10 Business management skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-10 Business management skills have a significant effect on whether an organization reported an increase in productivity
Null hypothesis 11-11 Financial skills have no significant effect on whether an organization reported an increase in productivity
Alternative hypothesis 11-12 Financial skills have a significant effect on whether an organization reported an increase in productivity

Backwards elimination was used to exclude variables of less significance. The process of backwards elimination went through five steps, successively excluding the following independent variables in this order: TRA; BUS; ITS; ENG. The final model was:

Logit(increase) = (0.313 * PM) + (0.278 * SCI) + (0.428 * ITP) + (0.408 * MAC) + (0.287 * MAR) + (0.403 * FIN) – 1.276

The significance of the final model was 0.000 using the Omnibus Tests of Model Coefficients. The final model accounts for 69.4% of the variation in the dependent variable. Using the Hosmer and Lemeshow Test, the model has a significance of 0.624 and a Chi-square score of 3.493 suggesting an acceptable goodness of fit. On this basis, the Null hypothesis 10-1 can be rejected, the alternative hypothesis can be accepted, and the model can be considered to be useful.

The significance of PM was 0.004 at Step 5. On this basis, the Null hypothesis 10-2 can be rejected and the alternative hypothesis accepted. It has been shown that Project management skills have a significant effect on the response, after controlling for other predictors in the model. The use of project management to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 36.8%, as opposed to their productivity staying the same.

The significance of ENG was 0.102 at Step 5. On this basis, the Null hypothesis 10-3 can be accepted. It has been shown that Engineering skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of SCI was 0.028 at Step 5. On this basis, the Null hypothesis 10-4 can be rejected and the alternative hypothesis accepted. It has been shown that scientific and research skills have a significant effect on the response, after controlling for other predictors in the model. The use of scientific and research skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 32.1%, as opposed to their productivity staying the same.

The significance of ITP was 0.000 at Step 5. On this basis, the Null hypothesis 10-5 can be rejected and the alternative hypothesis accepted. It has been shown that IT Professionals have a significant effect on the response, after controlling for other predictors in the model. The use of IT professionals to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 53.4%, as opposed to their productivity staying the same.

The significance of ITS was 0.375 at Step 5. On this basis, the Null hypothesis 10-6 can be accepted. It has been shown that IT Support technicians’ skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of TRA was 0.872 at Step 5. On this basis, the Null hypothesis 10-7 can be accepted. It has been shown that Trades have no significant effect on the response, after controlling for other predictors in the model.

The significance of MAC was 0.000 at Step 5. On this basis, the Null hypothesis 10-8 can be rejected and the alternative hypothesis accepted. It has been shown that Transport, plant, and machinery operation skills have a significant effect on the response, after controlling for other predictors in the model. The use of Transport, plant, and machinery operation skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 27.5%, as opposed to their productivity staying the same.

The significance of MAR was 0.000 at Step 5. On this basis, the Null hypothesis 10-9 can be rejected and the alternative hypothesis accepted. It has been shown that Marketing skills have a significant effect on the response, after controlling for other predictors in the model. The use of marketing skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 33.3%, as opposed to their productivity staying the same.

The significance of BUS was 0.353 at Step 5. On this basis, the Null hypothesis 10-10 can be accepted. It has been shown that Business management skills have no significant effect on the response, after controlling for other predictors in the model.

The significance of FIN was 0.000 at Step 5. On this basis, the Null hypothesis 10-11 can be rejected and the alternative hypothesis accepted. It has been shown that Financial skills have a significant effect on the response, after controlling for other predictors in the model. The use of financial skills to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 49.7%, as opposed to their productivity staying the same.

Conclusion

This research has tested and confirmed the hypothesis that the use of project management to undertake core business activities has a significant impact on a businesses’ productivity, within two specific but substantial datasets. In each dataset, approximately 15% more of the survey respondents who used project management reported an increase in productivity, compared to those who didn’t use project management.

In the 2004-05 to 2009-10 dataset, the use of project management to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 26.7%, as opposed to their productivity staying the same. In the 2006-07 to 2010-11 dataset, the use of project management to undertake core business activities was found to increase the odds of respondents reporting an increase in their productivity compared to last year by 36.8%, as opposed to their productivity staying the same. In both datasets, IT professionals, marketing skills, and financial skills were also found to have a significant impact on the tendency to report an increase in productivity, as opposed to productivity staying the same.

However, there are limitations to this study. This research has been limited to SMEs with less than 200 staff. While research that focuses on SMEs makes a valuable contribution to an otherwise under-researched area of project management, caution should be used when extending these findings to larger organizations. In addition, both datasets focus exclusively on businesses in Australia, and although the business context in Australia is comparable to that in other developed countries, some caution should be exercised when extending the results of this paper to either another context, a global population, or a different time period. Existing or planned surveys of business practice in other countries should be encouraged to include similar questions, so that the findings presented in this paper can be examined in the light of comparable data.

Lastly, it should also be noted that the dependent variable was itself dependent upon the ability of the respondents to accurately assess a difference in the productivity of their businesses. A possibility is also acknowledged that respondents will have varyingly interpreted what it is to “use project management,” as this phrase was not accompanied by a definition in the survey instrument; although, given the size of the survey population it is anticipated that responses will have converged on some common interpretation. Future research may wish to more closely explore what it means for a small to medium enterprise to use project management, and whether this qualitatively differs from the use of project management in larger organizations.

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Lead Author Biography

Julien Pollack started working in project management in the Australian public sector delivering organizational change programs, where he completed an Action Research PhD on the ways in which systems thinking could be used with project management to address complex projects. This research won national and international awards. After completing his PhD, he managed telecommunications and heavy engineering projects, before joining the University of Technology, Sydney in 2011 to teach in the Master of Project Management program. Julien has had one book published on project management, Tools for Complex Projects with Kaye Remington, five book chapters, eleven articles in peer-reviewed journals, and eight papers presented at peer-reviewed conferences.

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.

©2014 Project Management Institute Research and Education Conference

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