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Article Excerpt
1. Introduction
In information-intensive industries where state-ofthe-art development and deployment of information systems are strategic necessities, infrastructure is a central issue. Infrastructure is a critical enabler of competitive performance, new organizational forms, and improved business processes (Broadbent et al. 1999, Hamel and Prahalad 1994) and is key to the time, cost, and feasibility of implementing information systems (Duncan 1995). Infrastructure refers to "the enabling base of shared capabilities that provides the foundation for business systems ... these capabilities include both the internal (technical equipment, software, and cabling) and managerial expertise required to provide reliable services" (Broadbent et al. 1996, p. 175). Infrastructure services include activities such as computer operations, data integration, and configuration management that facilitate software development (Broadbent et al. 1999, Boehm 1981).
Empirical studies suggest that the costs of infrastructure are substantial. A study by Weill and Broadbent (1998) reveals that firms spend on average more than half (58%) of their total information technology (IT) investments on computing hardware and infrastructure activities and the remainder on the development of information systems. Information-intensive firms spend even more (65%) of their total IT investments on computing hardware and infrastructure activities. A broad-based analysis of IT costs across 2,151 U.S. corporations by Strassman (1999) found that 40% of IT spending is on infrastructure activities alone. As these examples illustrate, the costs of infrastructure activities can be substantial, and can rival or exceed software development costs. This highlights the importance of understanding how the costs of infrastructure activities can be mitigated.
Software process improvement is an innovation that yields significant cost savings in software development. A software process is a set of well-defined procedures leading to the development of software products (Zahran 1998). Software processes are improved by implementing work practices such as training, quality assurance, measurements, design and code reviews, and change control (Dekieva and Drehmer 1997). Prior studies have linked software process improvement to higher product quality, increased development productivity and reduced development cycle time and costs (e.g., Harter et al. 2000, Krishnan et al. 2000). This research has theorized that the primary driver of improved software development performance is reduced rework as a result of more effective software processes and higher product quality.
While increasing evidence supports the performance benefits of quality improvement for software development, it is not known whether infrastructure activities also benefit. However, as software development processes become more reliable and product quality improves, less rework could be required in infrastructure to support product development. Consider program control, for example. A primary function of this infrastructure activity is to enter and update software development schedules and plans. If software development processes are inconsistent, and products have many defects leading to development delays, significant rework is required in program control to re-enter and rebudget development schedules. However, as software processes become more reliable, products have fewer defects and are more likely to be on schedule and within budget. Thus, the costs of rework in program control are reduced. Generalizing this logic suggests that the infrastructure cost savings from rework avoidance due to quality improve ment in product development could be substantial.
This study draws upon theories of task interdependence and organizational inertia to relate quality improvement in software development to infrastructure activity costs. The effects of organizational inertia on infrastructure costs are modeled because inertia could delay the responsiveness of infrastructure activities to quality improvements, given the long-term nature of investments in infrastructure. We empirically evaluate our models by examining monthly cost data collected in nine infrastructure activity centers over 10 years of software development in a major IT firm. During this time, the firm advanced two levels in process maturity using the Software Engineering Institute's Capability Maturity Model (CMM).
A primary contribution of our study is the examination of the effects of quality improvement on infrastructure activities in software product development. Quality improvement literature has focused on development activities. To the best of our knowledge, prior studies have not considered the cost impact of quality initiatives on infrastructure, although resources expended on infrastructure activities can be substantial. Our work extends the stream of research on quality management by focusing on infrastructure activities in a software product development context. Further, we capitalize on our unique access to a detailed, longitudinal archive of cost data collected over a decade in a firm undergoing a quality improvement transformation. This quasi-controlled setting enables us to evaluate the relative impact on different activities as quality improves. Finally, our study contributes to the literature on quality management by incorporating theories of task interdependence and organizational inertia to explain h ow and why quality improvement impacts infrastructure activities. Organizational inertia and task interdependence have been well documented in the management literature, but few studies have analyzed their effects in the adoption of quality initiatives.
This paper is organized as follows. The next section develops our research framework and hypotheses. Section 3 provides details about our research site and data collection. Model estimation and results are presented in ??4. We interpret and discuss our findings and provide directions for future research in the final sections.
2. Software Process Maturity, Software Quality, and Infrastructure Activity Costs
Our research framework (Figure 1) integrates two models that interrelate software process maturity, software quality, and infrastructure activity costs. Model 1 relates software quality to process maturity, controlling for other factors. Software quality at time t is modeled as a function of process maturity at time t as process improvements have a direct impact on development activities: software [quality.sub.t] = f (software process [maturity.sub.t], software development [controls.sub.t]). Model 2 relates costs in infrastructure activity center c at time t to software quality at time t, controlling for other factors. The effects of software process improvements are mediated through quality in terms of their impacts on infrastructure costs. This is because while process improvements are focused directly on facilitating software development, such improvements have only indirect effects on infrastructure activities. In addition, the mediated relationship reflects our focus on modeling the net cost impact of qu ality improvement on infrastructure activities. The net impact reflects the difference between the investments in and benefits of quality improvement in the infrastructure activity centers. Finally, we include the effects of organizational inertia via lagged infrastructure costs: Infrastructure [costs.sub.c,t] = f (software [quality.sub.t], infrastructure [costs.sub.c, t-1], infrastructure [controls.sub.t]). In the following sections, we describe each model in more detail and present our hypotheses.
2.1. Software Process Maturity and Software Quality (Model 1)
The software engineering literature suggests a strong association between software process maturity and product quality (e.g., Harter et al. 2000, Krishnan et al. 2000, Herbsleb et al. 1997). Software processes become more mature--that is, more disciplined and consistent--as organizations add practices like design reviews, code reviews, configuration control, and measurement (Krishnan and Kellner 1999). These practices can reduce defects in software products through identification of disparities between requirements, design specifications, and the code. Such activities ascertain whether the developed software matches customer expectations and design specifications early in the software development life cycle. Practices promoting early detection and correction of errors help to design quality into the delivered software products (e.g., Laitenberger and DeBaud 2000, Slaughter et al. 1998, Dyer and Kouchakdjian 1990). Consistent with prior research, we therefore expect the following.
HYPOTHESIS 1. Higher software process maturity is associated with higher software quality.
We state this hypothesis controlling for the potential effects of product size and the use of computer-aided software engineering (CASE) tools in software development. Product size has been identified as a primary determinant of product defects (Banker et al. 2002). As products increase in size, there are more opportunities to introduce errors. Larger products also tend to be more complex both functionally and technically, and software complexity is strongly associated with defects. Thus, we expect that software quality will decrease with product size. We also control for the use of CASE tools in software development. CASE tools automate the software development process by generating software code to match design parameters entered into the tools by software engineers. Because the code is automatically generated, CASE tools mitigate discrepancies between the design and the code, introduce a consistency in the code, and reduce syntactical errors, thereby improving the technical quality of the software. In addi tion, CASE tools facilitate end-user involvement in the software development process (Finlay and Mitchell 1994), which increases the likelihood that...
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