doi:10.1111/j.1365-2575.2009.00332.x Info Systems J (2009)
1
A socio-cognitive interpretation of the potential effects of downsizing on software quality performance Paul J. Ambrose* & Ananth Chiravuri† *Management Computer Systems, Department of Information Technology/Business Education, College of Business and Economics, University of Wisconsin – Whitewater, 800 W. Main Street, Whitewater, WI 53190, USA, email:
[email protected], and † Department of Management Information Systems, School of Business and Management, American University of Sharjah, PO Box 26666, Sharjah, United Arab Emirates, email:
[email protected]
Abstract. Organizational downsizing research indicates that downsizing does not always realize its strategic intent and may, in fact, have a detrimental impact on organizational performance. In this paper, we extend the notion that downsizing negatively impacts performance and argue that organizational downsizing can potentially be detrimental to software quality performance. Using social cognitive theory (SCT), we primarily interpret the negative impacts of downsizing on software quality performance by arguing that downsizing results in a realignment of social networks (environmental factors), thereby affecting the self-efficacy and outcome expectations of a software professional (personal factors), which, in turn, affect software quality performance (outcome of behaviour undertaken). We synthesize relevant literature from the software quality, SCT and downsizing research streams and develop a conceptual model. Two major impacts of downsizing are hypothesized in the conceptual model. First, downsizing destroys formal and informal social networks in organizations, which, in turn, negatively impacts software developers’ self-efficacy and outcome expectations through their antecedents, with consequent negative impacts on software development process efficiency and software product quality, the two major components of software quality performance. Second, downsizing negatively affects antecedents of software development process efficiency, namely top management leadership, management infrastructure sophistication, process management efficacy and stakeholder participation with consequent negative impacts on software quality performance. This theoretically grounded discourse can help demonstrate how organizational downsizing can potentially impact software quality performance through key intervening constructs. We also discuss how downsizing and other intervening constructs can be managed to mitigate the negative impacts of downsizing on software quality performance.
© 2009 Blackwell Publishing Ltd
2
P J Ambrose & A Chiravuri
Keywords: downsizing, social cognitive theory, software quality, theory development INTRODUCTION
The US Department of Commerce’s National Institute of Standards and Technology (NIST) estimates that an inadequate infrastructure for software testing costs the national economy $59.5 billion annually (Tassey, 2002). The NIST further estimates that with feasible infrastructure improvements, this can be reduced by $22.2 billion, leaving about $37.3 billion in costs to the economy on account of inadequate software testing. Organizational factors such as software developer and end-user issues, rather than technical issues, account for a large portion of these remaining costs (Tassey, 2002). Inadequate software testing infrastructure hinders the detection and correction of bugs, consequently affecting software quality performance. Our reference to software quality performance includes both software product quality as evidenced in terms of product characteristics such as functionality and reliability, and software development process quality as evidenced in process efficiencies, in line with the organizational perspective on software quality performance (Ravichandran & Rai, 2000). The cost to the economy is hence on account of software failing to meet quality standards. However, testing infrastructure is only one among several technical and organizational factors that affect software quality performance, and the collective impact of these factors are likely to be of a much higher cost to the economy. The NIST study indicates that firms need to pay greater attention to the recruitment, development and retention of top-performing information technology (IT) professionals to prevent software errors and enhance software quality performance. Similarly, information systems (IS) research has highlighted how experienced IT professionals and the apposite management of these professionals are essential in ensuring software quality performance. This sentiment is restricted not just to the testing phase but also over the entire development process. For example, Basili & Caldiera (1995) emphasize that reuse of experience and collective learning of developers are important in ensuring software quality, and suggest that organizations build ‘experience factories’ of such experience and learning. On the issue of appropriate management strategies for IT professionals, Cusumano & Selby (1997), through their observation of software development practices at Microsoft, suggest that small development groups are more likely to develop quality software. Austin (2001), through his study on the effect of time pressure on developers, suggests innovative time management tactics such as de-linking project deadlines from realistic estimates of project completion for better developer management. While organizations can adopt different software development strategies, which can range from creating custom components using their own developers, to integrating packaged software through customization and workaround, to outsourcing the project, they are still heavily dependent on experienced IT professionals (Haines et al., 1997; Fulbright & Routh, 2004) and hence must appropriately manage these professionals. The NIST study also highlights the important role that end-users play in enhancing software quality performance, a factor also echoed in IS research. While top management support and
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
3
product or project champions are important for software quality and software development success (Ravichandran & Rai, 2000; Aladwani, 2002), end-users’ involvement is also important for enhancing software quality (Rondeau et al., 2002). End-users provide requirements and feedback during systems development, participate in testing and conversion, and even help develop end-user systems (Bowen et al., 2002; Rondeau et al., 2002). End-users are more knowledgeable in the details of the business processes embedded in the system and are indispensable to the development process (Cossick et al., 1992). For example, Costabile et al. (2006) point to a trend where tasks traditionally associated with professional software developers are being moved to end-users. Ferneley (2007) draws attention to a movement where actual end-user development is actively encouraged, albeit covertly. Rather than developing a single software product, Krueger (2006) encourages building software product lines that involve users across business functions. End-user participation and development is on the rise to the extent of an estimated 55 million end-user developers as compared with 2.75 million professional developers in the USA alone (Sutcliffe & Mehandjiev, 2004). End-user communities are hence essential for systems development. While experienced IT professionals are important for developing software and maintaining and enhancing its quality, the US IT industry has been plagued by chronic shortages of skilled IT labour since the late 1980s (Niederman & Brancheu, 1991; Lawrence Pfleeger & Mertz, 1995; Ahuja, 2002; Guerrera et al., 2006). First, enrolments in IT programs are down worldwide, resulting in fewer graduates with IT skills. For example, Granger et al. (2007) estimate that there was a 70–80% drop in enrolments in IT programs worldwide in the mid-2000s, while Kessler (2005) notes that enrolments in the USA were dropping at the rate of 10% per year since the dot com burst. Second, organizations are downsizing their IT operations to cut costs, and this is further exacerbating the shortage of skilled IT professionals in organizations (Ang & Slaughter, 2004; Ranganathan & Samant, 2006). For example, 28% of firms surveyed by InformationWeek in 2005 indicated that they were most likely to downsize in the following 2 years (InformationWeek, 2005). Third, IT staffing is not expected to improve even if the economy is strong as organizations are not hiring for economic reasons (Hayes, 2002). For example, in spite of better results and a growing optimism in the economy, 140 696 jobs were lost in the US tech sector in the first three quarters of 2005 compared with a loss of 118 427 jobs in the first three quarters of 2004, a decline of 19% (Weiss, 2005) indicating a negative hiring trend. Ang & Slaughter (2004), quoting both the director of the workforce development programme at the Computing Technology Industry Association and the US Department of Commerce, state that while IT lay-offs continue, IT managers are struggling to find the right IT personnel, and that the demand–supply gap for IT professionals is causing a backlog for IT services. Evidently, the IT industry is faced with a shortage of qualified IT personnel, which can be detrimental to developing and maintaining quality software. Lay-offs, a key constituent of downsizing, affect all employees, not just IT employees. Since the heyday of job security in the 1950s and 1960s, US organizations initiated major lay-offs starting in the mid-1970s and continue to do so till date (Uchitelle, 2006). While organizations lay-off employees for cost benefits, lay-offs and the possibility of lay-offs undermine employee productivity, loyalty, commitment, dignity, and job security and are detrimental to the overall
© 2009 Blackwell Publishing Ltd, Information Systems Journal
4
P J Ambrose & A Chiravuri
success of organizations (Benson, 2006; Molinsky & Margolis, 2006; Uchitelle, 2006; Zatzick & Iverson, 2006). For example, De Meuse et al. (2004) point out through longitudinal analysis using data over a 12-year period that companies that downsized experienced lower performance compared with companies that did not downsize during the same period, and that it took several years for companies that downsized to regain financial health. However, top management continues to use downsizing as a strategy to survive and grow during times of economic turbulence in spite of evidence that indicates that downsizing can be detrimental to the functioning of the organization (Amabile & Conti, 1999; De Meuse et al., 2004). Both IT professionals and end-users can potentially be affected by organizational downsizing. These stakeholders can be laid off, creating a shortage of skills required for a successful software development. Or the ability of these personnel to work productively can be hampered when the organization downsizes either by laying off their co-workers or through the adoption of other downsizing strategies that affect organizational structures and processes. As a result, software quality performance can be compromised. In this paper, we examine the potential effects of organizational downsizing, which includes IT downsizing, on software quality performance within the organization. The rest of the paper proceeds as follows. The next section presents the research motivation and premises, and research questions. The section following lays the theoretical foundation for this study by discussing relevant literature from the software quality, social cognitive theory (SCT) and downsizing research streams. The conceptual model and propositions are presented in the next section. The final section concludes with a discussion on the implications of this research. RESEARCH MOTIVATION, PREMISES AND QUESTIONS
Traditionally, software quality performance was evaluated from a technical perspective where the software artefact was evaluated in terms of desirable product characteristics such as functionality, usability, reliability, maintainability, efficiency and portability (Rai & Song, 1998; Tassey, 2002). Software quality performance was thus evaluated in terms of product quality. The popularity of Total Quality Management (TQM) principles in the manufacturing sector highlighted the need to also focus on the process used to create and deliver the software artefact (Ravichandran & Rai, 1999; Slaughter et al., 2006). The focus of software quality performance hence shifted from a purely technical perspective to an organizational perspective to include the software development process, as an efficient and mature process can influence the quality of the product emerging from the process (Harter & Slaughter, 2003). Software quality performance was thus expanded to include both process efficiency and product quality, and from an organizational perspective, is defined as the degree to which the objectives of software product quality and software development process efficiency (also referred to as software development process quality) are met by the systems development organization (Ravichandran & Rai, 2000). People are integral to business processes including the software development process (Hill et al., 2006). Our principal premise is that organizational actions such as downsizing disrupt
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
5
the ability of employees to optimally contribute to the software development process. As a result, process efficiency is affected with a consequent detrimental effect on software product quality. But what is downsizing, and how does it affect organizations? Organizational downsizing is an intentional workforce reduction strategy implemented through hiring freezes, lay-offs, induced attrition, and/or work reduction (Freeman & Cameron, 1993). The primary motivation for downsizing is to reduce costs to achieve competitive advantage. However, several studies have suggested that downsizing does not always ensure the realization of the perceived benefits. For example, studies have argued that downsizing severs relationships and destroys a firm’s existing network (Shah, 2000), damages learning and memory networks (Fisher & White, 2000), hinders innovation (Dougherty & Bowman, 1995), and negatively impacts creativity (Amabile & Conti, 1999). A study of over 3000 companies over 15 years found that on average, organizations that downsized exhibited lower profitability than stable organizations, while stock market performance of these organizations were no better than that of stable organizations (Slocum et al., 1999). Also, Cascio (1993) found that only 21% of downsizing companies reported satisfactory improvements in their shareholders’ return on investment. Taken together, the effects of downsizing appear to be more detrimental than beneficial. Skills shortage on account of downsizing and its effect on software development is overt, and this study does not address this issue. Instead, our focus is on the covert dangers of downsizing and their potential impact on software professionals’ work productivity with consequent effect on software quality performance. We build our arguments and model using the tenets of SCT (Bandura, 1977a; 1986), which posits a triadic reciprocal relationship among an individual’s environmental factors, personal factors (e.g. attitudes, cognition, affect and conation) and behaviour. We posit that downsizing, an environmental factor, affects software professionals’ personal factors with consequent impact on their ability to work effectively to develop quality software artefacts (a manifest behaviour). Specifically, using SCT, we argue that social network destruction because of downsizing, an environmental factor, affects two major cognitive (personal) factors: self-efficacy and outcome expectations of software professionals. These cognitive factors, in turn, affect these software professionals’ job-related behaviour with consequent impact on the software development process efficiency and product quality as shown in Figure 1. Self-efficacy is the belief about one’s ability to produce designated levels of performance (behaviour manifestation) (Bandura, 1977a). Outcome expectation refers to the fact that individuals undertake behaviours that they perceive will result in favourable outcomes independent of skills possessed (Bandura, 1977a). Prior studies have found that self-efficacy and outcome expectation perceptions are key influencers of behaviour undertaken (Bandura, 1977b; 1997; Mathers, 2005; Compeau et al., 2006), and on the actual performance attainments of the individual with respect to the behaviour (Barling & Beattie, 1983; Compeau & Higgins, 1995a; Venkatesh et al., 2003; Compeau et al., 2006). Fisher & White (2000), from a social network perspective, argued that the non-linear effects of downsizing intensify dramatically as the size of the network grows. They conclude that
© 2009 Blackwell Publishing Ltd, Information Systems Journal
6
P J Ambrose & A Chiravuri
Personal Factors Environmental Factors
Downsizing
Social Networks Destruction
Software Professionals’ Cognitive Factors * Self-efficacy * Outcome Expectations
Behavior (Manifestations) Software Quality Performance Development Process Efficiency
Software Product Quality
Figure 1. Overarching research model.
‘Downsizing – or any restructuring that involves broad-based personnel reduction or movement – may seriously damage the learning capacity of organizations’ (p. 249). Organizational social networks provide both formal and informal inputs for software development (Harrison & Coplien, 1996), and their disruption can be detrimental to software development. This not only hinders the availability of business inputs for software development but also reduces the quality of business input given that social network destruction hinders the learning capacity of the network. However, while Fisher and White argued that downsizing affects learning capacity, they did not elaborate on its specific impact on different organizational areas such as IS. Additionally, studies on the impact of downsizing in the IS research domain are lacking, especially in the area of software quality. Our research aims to develop a better understanding of how and why downsizing affects the IS function’s ability to develop software of good quality. To do so, we delve into behavioural research streams for theoretical support. Accordingly, we address the following research questions: 1 What is an overarching theoretical framework that can be used to interpret the effects of downsizing on software quality performance? We propose SCT as an appropriate theoretical framework and discuss relevant SCT concepts such as self-efficacy and outcome expectations. 2 How does downsizing impact the ability of organizations to develop quality software? We present and draw on relevant literature on downsizing to explicate how downsizing destroys social networks in organizations and consequently affects the attitudes, behaviours, and performance of software professionals. 3 What is the relevant perspective of software quality to understand the impact of downsizing on software quality performance? We draw upon the organizational perspective of software quality rooted in TQM principles to specify the impact of downsizing on software quality performance. 4 What is the nomological model linking organizational downsizing and software quality performance? We present our conceptual model linking the relevant environmental, cognitive and behavioural factors discussed.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
7
THEORETICAL FOUNDATION
Software quality The literature on software quality can be classified into four categories: (1) software quality measurement and control; (2) development infrastructure; (3) software process management; and (4) participative design (Ravichandran & Rai, 2000). The first two categories are technical in nature where the focus is on software metrics, quality control tools and techniques, and development infrastructure, which includes development tools and methods such as Computer Aided Software Engineering (CASE) tools. This technical or engineering approach focuses on the software product that is developed. The latter two categories are more organizational in nature with the primary motivation being managing the software development process, and there is a focus on issues such as development process management and innovation, and stakeholder involvement and participation in software development. We highlight salient developments in the previously stated categories pertinent to our research. Consumers’ product choice typically involves customers maximizing a utility function containing multiple parameters, of which product quality is one (Basu & Hastak, 1990). Similarly, software customers choose software by maximizing a utility function containing software quality as a parameter (Tassey, 2002). Because customers evaluate software in terms of desirable attributes of the software artefact, the traditional focus of software quality research has been on software product quality (Ravichandran & Rai, 1999). Software quality performance, from a product quality perspective, has been defined as a bundle of attributes for which the customer holds a positive value (Tassey, 2002). Seminal work in identifying a set of attributes that helps determine the quality of the software artefact can be traced to McCall et al. (1977). They developed a model that contained 11 specific product quality attributes divided into three categories. Table 1 presents and describes these attributes as specified in McCall et al. McCall et al.’s model was further elaborated by Boehm (1978) to include additional quality attributes while maintaining the same three categories. Further legitimacy was given to software quality performance evaluated in terms of Table 1. McCall et al.’s (1977) software quality attributes Category
Attribute
Description
Product operation
Correctness Reliability Integrity Usability Efficiency Maintainability Flexibility Testability Interoperability Reusability Portability
Ability to perform its required function and meets customer needs Ability to perform its function with required precision Ability to withstand accidental and intentional attacks Ease of learning, operating, preparing inputs and interpreting outputs Computing power required by the software to perform its function Ease of locating and fixing errors Ease of changing the software Ease of identifying if the software is performing its intended function Ease of integrating with other systems Ease of using parts or entire software in other applications Ease of migrating from one platform to another
Product revision
Product transition
© 2009 Blackwell Publishing Ltd, Information Systems Journal
8
P J Ambrose & A Chiravuri
product quality when the International Organization for Standardization (ISO) adopted ISO 9126 as the standard for software product quality (ISO, 1991). ISO 9126 elaborated McCall et al.’s and Boehm’s work but also deviated from this prior body of work in certain aspects such as in the product operation category, where the ISO model specified this category as four different categories (Tassey, 2002). The ISO standards are based on functionality, reliability, usability, efficiency, maintainability, and portability and are widely accepted as defining characteristics of software product quality (Tassey, 2002). Evaluating software quality performance in terms of specific characteristics of a system such as flexibility, maintainability, reusability, integration, consistency, usability, reliability, functionality, efficiency and portability has been advocated by other software practitioners and researchers as well (e.g. Dunn, 1988; Humphrey, 1989; Yourdon, 1992; Austin, 2001; Budgen, 2003). These characteristics tend to focus on the engineering aspects of software development that ultimately affect user (customer) satisfaction. This traditional focus on product quality did not substantially help reduce either the software development cycle time or the quality of new software products (Harter et al., 2000; Mahanti & Antony, 2005). The focus of software quality management soon shifted to paying closer attention to the software development processes, as efficient and mature or effective processes help produce quality products, an idea borrowed from the manufacturing sector (Rai & Song, 1998; Ravichandran & Rai, 1999; Slaughter et al., 2006). The software development process refers to activities, methods, practices and transformations that are used to develop and maintain software (Paulk et al., 1993). Specifically, the process approach to software quality management posits that the quality of a system is very dependent on the quality of the process used to acquire, develop, and maintain the system (Harter et al., 2000) and is based on TQM principles (Deming, 1986; Juran, 1992) from manufacturing. Managing the systems development process was identified as critical for timely development of quality software (Arthur, 1997). As a result, both in research and practice, software quality has evolved from a technical perspective that focused on product quality to a more organizational perspective that focused on managing the software development process to enhance software quality performance (Ravichandran & Rai, 2000; Agrawal & Chari, 2007). A key contributor to the organizational or process approach to software quality management is the Software Engineering Institute (SEI) at Carnegie Mellon University. the SEI, in the mid-1980s, developed the Capability Maturity Model (CMM), since replaced by the Capability Maturity Model Integration (CMMI), to manage and improve processes. The core tenet of CMM (summarized from http://www.sei.cmu.edu/cmm/) is that process, people and technology are the major determinants of product cost, schedule and quality, as shown in Figure 2. Per CMM, technology can contribute to efficiency, while people and processes towards effectiveness, and all three components are necessary to ensure both efficiency and effectiveness. The CMM approach focuses on process improvement and has a series of process models, which are structured collections of practices that describe the characteristics of effective processes. The practices are typically those proven by experience to be effective. However, CMM is oriented towards a waterfall process of software development where the focus is on planning, analysis, and design and is not designed to address the needs of iterative development where the focus
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
9
Figure 2. CMM core components.
is on the evolving software artefact (Royce, 2002). To address this drawback, the SEI in the early 1990s developed the CMMI where the models are better aligned with iterative development principles. But the focus is still on process improvement, with people playing an important role in ensuring software quality performance. Other significant research on the organizational or process approach to software quality management has identified and empirically substantiated four key organizational constructs – top management leadership, sophisticated management infrastructure, process management efficacy, and stakeholder participation – as being instrumental to software quality performance (e.g. Heemstra & Kusters, 2002; also see Ravichandran & Rai, 2000, for further literature and empirical support). These antecedents of software quality performance are based on the classic leadership-structure-process-outcome model of organizations (Ravichandran & Rai, 2000). Top management leadership, especially through transformational leaders, is important because leadership helps promote practices and behaviours that lead to superior quality performance (Jalote, 2000). Studies have also emphasized top management leadership as a critical factor in creating an overall culture of quality and for ideal allocation of resources (Vitharana & Mone, 1998; Parzinger & Nath, 2000; Wynekoop & Walz, 2000). Management infrastructure sophistication emerges when a quality-conscious management vision is embodied in the policies and structures of the organization (Isaac et al., 2004). Here, the skill and knowledge management of employees are important in developing and maintaining such a level of sophistication. Process management efficacy is achieved through formalization of design methods and reusability, facts-based management, and process controls (Ravichandran & Rai, 2000). Here the aim is to incorporate practices that eliminate waste through continuous improvement. Finally, stakeholder participation through involvement (cognitive state) and participation (behavioural outcome) that includes key stakeholders such as users, vendors, and programmer/analysts is essential as these stakeholders help maintain quality practices and provide input for development (Ravichandran & Rai, 2000). SCT Originating in social psychology, SCT explains how people acquire and maintain certain behavioural patterns, and provides the basis for intervention strategies (Bandura et al., 1977;
© 2009 Blackwell Publishing Ltd, Information Systems Journal
10
P J Ambrose & A Chiravuri
Personal Factors
Environment
Behavior Figure 3. Bandura’s triadic reciprocality.
Bandura, 1986; 1997). SCT is used in a wide range of research disciplines to understand behaviour, and consequently, performance (Lane et al., 2004). The mid-1980s and early 1990s saw its adoption and establishment in IS research as well (Hill et al., 1985; Hill & Smith, 1987). The dynamic interplay among an individual’s social and physical environments, his or her personal factors, and the behaviour exhibited has been theoretically addressed and empirically substantiated using Bandura’s SCT (Bandura, 1977a; 1978; 1986; Bandura et al., 1977). Bandura presented this interplay as a ‘triadic reciprocality’ where he propounded that (a) an individual’s personal factors (such as attitudes, beliefs, cognitions, affect and conation) influence the choice of environments and are influenced reciprocally by the environment; (b) environment influences behaviour and, in turn, is affected by behaviour; and (c) personal factors influence behaviour and, in turn, are influenced by behaviour as shown in Figure 3. A central premise of SCT is that two sets of personal factors involving cognition – self-efficacy and outcome expectations – guide behaviour.
Self-efficacy An important construct in SCT is self-efficacy (Bandura, 1986; Gardner & Rozell, 2000; Compeau et al., 2006). According to Bandura (1986), self-efficacy is ‘people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of performance. It is concerned not with the skills one has but with judgments of what one can do with whatever skills one possesses’ (p. 391). Self-efficacy reflects an individual’s futureoriented belief as to what he or she can accomplish (Compeau et al., 2006). Stajkovic & Luthans (1998) further elaborate self-efficacy as ‘an individual’s convictions about his/her abilities to mobilize motivation, cognitive resources, and courses of action needed to successfully execute a specific task within a given context’. Hence, self-efficacy is different from cognitive measures of competence, which measure what one knows (Kraiger et al., 1993; Compeau et al., 2006). A person’s self-efficacy estimations are an important predictor of their subsequent performance (Bandura, 1977a). Studies of self-efficacy within organizational settings have shown that it is consistently related to task performance (Gardner & Rozell, 2000). Self-efficacy has also been positively linked to faculty research productivity (Taylor et al.,
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
11
1984), adoption of high-technology products (Hill & Smith, 1987), performance in software training (Webster & Martocchio, 1992; 1993), and learning and achievement (Wood & Locke, 1987; Gravill et al., 2006; Hasan, 2006). A key antecedent of self-efficacy is encouragement from others (Marakas et al., 1998; Compeau et al., 2006). Encouragement from others influences self-efficacy if it is perceived as credible (Bandura, 1986; Compeau & Higgins, 1995b). Self-efficacy also arises from four categories of experience: enactive mastery, vicarious experience, verbal persuasion and physiological or emotional arousal (Bandura, 1977a; 1982). Enactive mastery or prior experience has the largest influence because it involves direct and repeated experience with the task. Successful performance of a task raises performance expectations; repeated failures lower them, especially during the early stages of learning. Vicarious experience (modelling) is achieved by observing someone similar to oneself performing the task. When someone similar to oneself successfully completes a task, particularly if his or her abilities and resources are similar to one’s own, confidence in one’s capacity to perform the task is typically raised (Manz & Sims, 1981). However, because vicarious learning relies on social comparison processes, it is a less dependable source of efficacy information than direct experience (Gardner & Rozell, 2000). Verbal persuasion involves a credible teacher or peer convincingly arguing that one can successfully perform the task (Stone et al., 1996). This source is commonly used because of its ready availability and ease of use. Although verbal persuasion can raise efficacy beliefs, it is less durable than enactive mastery and modelling (Bandura, 1982; Gist, 1987). Finally, physiological arousal impacts self-efficacy as the individual uses information about his or her physiological state in assessing performance capability (Gardner & Rozell, 2000). Gardner and Rozell further explain that persons in such states may view their arousal as debilitating and hence feel more vulnerable to failure. Self-efficacy is measured along three dimensions: magnitude, strength and generality (Compeau & Higgins, 1995b; Compeau et al., 2006). Magnitude corresponds to the level of task difficulty that one believes one can achieve. It is measured by asking respondents to indicate dichotomously (yes or no) whether they are capable of performing a task at several levels and by summing the positive responses (Bandura, 1986). Individuals with a high magnitude of self-efficacy might judge themselves as capable of operating with less support and assistance than those with lower judgements of self-efficacy (Compeau & Higgins, 1995b). Strength is the confidence that an individual has regarding his or her ability to perform the tasks. It is measured by asking subjects to indicate their confidence for each affirmative response, or in some cases, all performance levels, and summing these ratings together (Gist & Mitchell, 1992). Finally, generality is the extent to which self-efficacy is limited to a specific domain of activity, as opposed to multiple domains (Bandura et al., 1977).
Outcome expectations Another important construct from SCT is outcome expectations (Compeau et al., 2006). This refers to a person’s estimate that a given behaviour will lead to certain outcomes that in turn
© 2009 Blackwell Publishing Ltd, Information Systems Journal
12
P J Ambrose & A Chiravuri
could influence actual performance (Bandura, 1977b). It essentially reflects what one believes will occur if he or she were to complete a particular action (Compeau et al., 2006). Outcome expectations is a distinct construct in spite of its similarity to and close relationship with self-efficacy. Bandura differentiated between self-efficacy and outcome expectations because individuals may believe that a certain behaviour will result in a specific outcome; however, they may not believe that they are capable of performing the behaviour required for the outcome to occur (Resnick et al., 2004). Bandura suggested that outcome judgements are based largely on the individual’s self-efficacy expectations. The types of outcomes people anticipate generally depend on their judgements of how well they will be able to perform the behaviour. That is, those individuals who consider themselves to be highly efficacious will expect favourable outcomes. Outcome expectations hence can refer to the fact that individuals undertake behaviours that they perceive would result in favourable outcomes independent of skills possessed (Bandura, 1977a). Outcome expectations can, however, be dissociated from self-efficacy expectations. This occurs when either no action will result in a specific outcome or the outcome is loosely linked to the level or quality of the performance. Outcome expectations, the perceived likely consequence of an action, has two dimensions, namely performance-related expectations and personal expectations (Compeau et al., 1999). Compeau et al. (1999) distinguish these dimensions as follows: performance outcome expectations are concerned with the efficiency and effectiveness aspects of job performance, while personal outcome expectations deal with expectations on personal rewards and status in the organization. Both dimensions are influenced by selfefficacy perceptions (Compeau et al., 1999), and hence, the antecedents of self-efficacy have an indirect influence on outcome expectations. Also, similar to self-efficacy, the encouragement of others influences outcome expectations in that if others in the individual’s reference group encourage the use of a computing technology, then the individual’s judgements about the likely consequences of the behaviour will be affected (Compeau & Higgins, 1995b). The concept of outcome expectations has been used in IS research to understand technology usage. For example, the usefulness construct in the Technology Acceptance Model introduced by Davis (1989; Davis et al., 1989), and since widely used to understand technology acceptance and adoption, is based on outcome expectations (Venkatesh et al., 2003). Computer self-efficacy (an adaptation of self-efficacy to the computer usage context) research has used the outcome expectations construct as a complementary construct in understanding computer usage. Outcome expectations influence an individual’s reaction to computing technology and have been found to have a significant influence on usage behaviour (Compeau & Higgins, 1995b). SCT provides a theoretical framework for understanding human behaviour. SCT posits a triadic relationship among people’s attitudes, beliefs, cognition, environment and behaviour. We now discuss downsizing to build our thesis that this environmental factor can negatively affect a software developer’s personal factors and work-related behaviour, and consequently, performance.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
13
Downsizing Freeman & Cameron (1993) describe downsizing as an intentional management action involving a reduction in workforce through a combination of organizational actions such as hiring freezes, lay-offs, induced attrition, which are designed to improve a company’s competitive position. Downsizing does not include the discharge of individuals for cause or individual departures via normal retirement or resignations. Although organizations may get smaller through headcount reduction strategies such as lay-offs, attrition and early retirements, downsizing may also occur by reducing work (not just personnel) and by eliminating functions, hierarchy levels or units. In addition, it may also occur through cost-containment strategies that simplify processes such as paperwork, IS or sign off policies. Over the years, downsizing has gained strategic legitimacy as a reorganization strategy (McKinley et al., 1995). The three common types of downsizing strategies that firms adopt are workforce reduction, organization redesign and the systemic strategy (Cameron et al., 1991). While workforce reduction is a short-term implementation aimed at headcount reduction, organization redesign is a moderateterm implementation aimed at organization change. Finally, the systemic strategy is a longterm implementation aimed at culture change. Motivation for downsizing There are various perspectives on why organizations downsize, namely economic, institutional, socio-cognitive and organizational decline (Freeman & Cameron, 1993; Mone et al., 1998; McKinley et al., 2000). The economic perspective on downsizing based on the economic/rational paradigm argues that firms downsize for perceived economic benefits that stem from a search for productivity and efficiency (McKinley et al., 2000). Here, the benefits of downsizing have been interpreted in terms of both current and future financial performance outcomes that include increased earnings, higher stock price, lower overheads and increases in productivity (Cascio, 1993). However, a consistent positive link between downsizing and financial performance has not been established (McKinley et al., 1995; 2000). The institutional perspective, based on the sociological paradigm, argues that firms downsize to gain legitimacy and reduce uncertainty (McKinley et al., 1995). This perspective draws its theoretical foundations from neoinstitutional theory (DiMaggio & Powell, 1983), which specifies that institutional rules (norms shared by members of a society) determine organizational forms and processes (McKinley et al., 1995). Per this perspective, widespread downsizing led to its gaining the status of an institutional norm, and hence firms downsize to gain legitimacy (McKinley et al., 2000). Alternatives to institutional norms were considered unthinkable, and conformation to institutional norms was considered to reduce uncertainty in decisionmaking (McKinley et al., 2000). It is argued that downsizing decisions are driven by social conventions that define what is ‘good’ and ‘bad’ and that three social forces, namely constraining, cloning and learning, determine these rules (McKinley et al., 1995). These social forces are summarized from McKinley et al. (1995) as follows. Constraining social forces are those that demand that organizations adhere to the norms or rules that define legitimate structures and management activities. An example of this would be
© 2009 Blackwell Publishing Ltd, Information Systems Journal
14
P J Ambrose & A Chiravuri
corporations following downsizing to appear lean, where the need to appear lean is an institutional rule. Thus, firms undertake actions as a means to conform to social constraints. Cloning social forces are those that demand that organizations imitate the actions of the most prestigious, visible members of their industry. Organizations mimic the behaviour of other organizations in an environment of extreme uncertainty to be part of the group. Finally, learning social forces are those that are developed through the management practices taught in universities or professional development associations. The socio-cognitive perspective, based on the socio-cognitive paradigm, argues that managers’ decisions to downsize are based on mental models, shared with other managers, that define downsizing as effective and ethical (McKinley & Mone, 1998). These mental models or schemas are socially constructed and reified and seem to emerge among managers across different industries. The socio-cognitive perspective provides a micro-level understanding of how institutional rules, as detailed in the institutional perspective, are formed. Finally, the organizational decline perspective propounds that downsizing is the only alternative to companies that are on the path to demise and non-existence because of erosion in their resource base (van Witteloostuijn, 1998; McKinley et al., 2000; Carmeli & Schaubroeck, 2006). Organizational decline is considered to be different from downsizing, given their differences in the motivation for size reduction (van Witteloostuijn, 1998).
Negative impacts of downsizing Although downsizing appears to be an effective solution, regardless of the perspective, it has not proven to be consistently effectual. Even if downsizing is accompanied by restructuring, the results have been less than positive and in most cases did not meet strategic and financial objectives (Marks, 2006). For example, it has been pointed out that the reorganization of the workplace into canonical groups can disrupt highly functional, non-canonical and therefore invisible communities (Brown & Duguid, 1991). Similarly, another study found that restructuring at General Motors destroyed informal networks that were critical to formal operational networks (Keller, 1989). Lay-offs moderate the relationship between high-involvement work practices and productivity, and a negative relationship between high-involvement work practices and productivity exists in workplaces with higher lay-off rates (Zatzick & Iverson, 2006). Furthermore, there are numerous studies that have elaborated the ill effects of downsizing. During downsizing, firms suffer a deterioration of trust (Buch & Aldridge, 1991; Hurley, 2006) and communication (Dougherty & Bowman, 1995). Creativity and most creativity-supporting aspects of the work environment were also found to decline significantly during the downsizing (Amabile & Conti, 1999). On the effects of downsizing on product innovation, it was found that downsizing severed relationships with senior managerial sponsors, thereby leaving many innovations unsupported (Dougherty & Bowman, 1995). Downsizing has also been argued to be a high-risk strategy in a learning organization (Fisher & White, 2000). Thus, downsizing carries with it considerable risks – both economic and non-economic. The impact of social network destruction on account of downsizing and its implication for software quality performance is further elaborated in the next discussion.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
15
Social networks Organizational social networks play a critical role in transporting information and facilitating work duties (Shah, 2000). There are two different mechanisms of influence, namely cohesion and structural equivalence, that social networks exert (Burt, 1987). The cohesion research states that people are influenced by their direct ties or friends (Galaskiewicz & Wasserman, 1989) because of the frequency, intensity and proximity of interaction (Burt, 1987). Restricted information exposure and conformity pressures within cohesive groups also influence cohesive actors (Levine & Moreland, 1990). On the other hand, the structural equivalence research states that people are influenced by others occupying the same position in the social structure that the individuals themselves occupy. According to Burt (1987), ‘Structurally equivalent people occupy the same position in the social structure and so are proximate to the extent that they have the same patterns of relations with occupants of other positions (p. 1291). . . . More generally, structural equivalence predicts that two people identically positioned in the flow on influential communication will use each other as a frame of reference for subjective judgments and so make similar judgments even if they have no direct communication with each other (p. 1293)’. Structurally equivalent actors share a similar pattern of relationships and therefore an inherent rivalry exists because one actor can be substituted for the other. There is support for both these concepts as mechanisms of influence in the literature. More importantly, these actors do not merely have influence when they are present; their absence also has an important effect on individual behaviour (Shah, 2000). According to Shah, the loss of cohesive actors (friends) impacts a survivor’s attitudes and behaviours. Dissatisfaction also sets in from greater lay-off exposure and social isolation and is further intensified by the loss of social contacts. Therefore, we argue that downsizing resulting in the loss of actors impacts a software professional’s attitudes and behaviours. More specifically, attitudes per SCT impact other personal factors such as cognitive, conative and affective factors, which in turn impact behaviour. Shah (2000) also talks of two types of networks: instrumental and expressive. Instrumental networks provide advice, information, expertise, resources and political access. In contrast, expressive networks provide friendship and social support (Ibarra, 1993). Downsizing results in the destruction of networks that include both instrumental and expressive. A software professional not only loses his or her sources of information but also his or her social support, which in turn affects his or her attitude, cognition and behaviour, and consequently, performance. In addition, the learning capacity and memory of his or her information sources is affected, resulting in unsatisfactory inputs for software development.
Summary of theoretical foundation The organizational perspective of software quality management, also referred to as the process approach because of the popularity of CMM and CMMI, emphasizes the importance of people and processes (Harter et al., 2000; Ravichandran & Rai, 2000). People’s actions and behaviour can enhance or diminish the quality of the software artefact. SCT (Bandura, 1977a)
© 2009 Blackwell Publishing Ltd, Information Systems Journal
16
P J Ambrose & A Chiravuri
provides a theoretical foundation for understanding how people’s behaviour can affect software quality performance. Per SCT, the environment affects attitudes and behaviour, and hence, a downsizing environment can affect software professionals’ attitudes, particularly their cognitive factors, such as self-efficacy and outcome expectations. Downsizing research indicates that organizational downsizing destroys social networks that support people in organizations (Fisher & White, 2000; Shah, 2000). Organizational downsizing, including IS downsizing, can destroy social networks that support software professionals both formally and informally in their work. As a result, opportunities for factors such as vicarious learning or verbal persuasion diminish. It can also potentially affect other key antecedents of self-efficacy and outcome expectations, which ultimately manifest as lower productivity (a behaviour) of software professionals with consequent impact on software process and product quality. Further software quality management research indicates top management leadership, sophisticated management infrastructure, process management efficacy and stakeholder participation as salient antecedents to software quality performance (Ravichandran & Rai, 2000). Downsizing affects these key determinants of software quality performance either directly or indirectly as follows. Effective leaders are personally involved in quality-planning activities, take ownership of responsibilities for quality performance and provide support for quality initiatives (Ravichandran & Rai, 2000), and their loss would impact quality performance. In addition, the loss of other organizational personnel who provide inputs for these leaders to be effective could further affect quality. Loss of personnel would entail readjustment to structural changes that impact organizational processes and would hinder the development and maintenance of management infrastructure sophistication and reduce process management efficacy, as organizational personnel are imperative in maintaining processes and controls. More importantly, loss of personnel would hamper stakeholder participation in software development. Stakeholder inputs for software development do not reflect individual and isolated nuggets of stakeholder knowledge but rather the collective knowledge that rests in stakeholder or organizational social networks. Also, these stakeholders, in various capacities, help provide encouragement, support and opportunities to enhance work-related experience. Hence, destruction of such social networks would affect the quality of stakeholder input and support. We now discuss the conceptual model developed using these and other relevant variables presented earlier.
CONCEPTUAL MODEL
Figure 4 presents the conceptual model, which summarizes the literature synthesis. Table 2 presents the propositions. The model first details the environmental influencers on software professionals. Here, it proposes that downsizing is detrimental to social networks in organizations and that this, in turn, negatively affects the key antecedents of self-efficacy and outcome expectations. These antecedents, in turn, impact software professionals’ self efficacy and outcome expectations. It is hypothesized that not all SCT antecedents will have an equal impact on self-efficacy and outcome expectations. For example, enactive mastery or the
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
Environmental
Personal
Organizational Downsizing
P3
17
Behavioral
P1 Formal & Informal Social Network Destruction
P2
Antecedents of Self Efficacy & Outcome Expectations
Self-Efficacy
P6 Software Quality Performance
Encouragement Enactive Mastery
P9 Antecedents of Software Development Process Quality Top Management Leadership Management Infrastructure Sophistication
P5
Vicarious Experience Outcome Expectations
Verbal persuasion
Software P8 Development Process Efficiency
Software Product Quality
P7
Physiological Arousal
P4
Process Management Efficacy Stakeholder Participation
P10
Figure 4. Conceptual model – impact of downsizing on software quality performance.
software professional’s prior domain experience will be affected to a lesser degree in the near term, as such expertise is built over time, and downsizing may not immediately impact experience. But in the long term, this too can be affected, as the ability to further expand domain expertise can be disadvantaged when appropriate social networks to gain such experience is lost. Software professionals’ judgements of their capabilities to organize and execute the necessary actions for software development (self-efficacy) and their estimation that a given behaviour will lead to certain outcomes (outcome expectations) are modelled to impact software development process efficiency. The model also proposes that downsizing directly impacts the antecedents of software development process efficiency and its consequent impact on software product quality. Empirical support for propositions P1, P3–P5, P8 and P10 exists, although some of that support is in different contexts. Each of these propositions is restricted to one of the three research streams presented in this paper, and empirical support exists within that stream. For example, support for P1 exists in the downsizing literature (e.g. Shah, 2000). Similarly support for P3–P5 exists in SCT research (e.g Stajkovic & Luthans, 1998; Compeau et al., 2006). Finally, support for P8 and P10 exists in the software quality research stream (e.g. Ravichandran & Rai, 1999; 2000). However, propositions P2, P6, P7 and P9 bridge the literature streams presented in this paper, and direct empirical support does not currently exist in IS
© 2009 Blackwell Publishing Ltd, Information Systems Journal
18
P J Ambrose & A Chiravuri
Table 2. Research propositions Proposition 1: Downsizing negatively influences formal and informal social networks in organizations. Proposition 2: Organizational social network destruction negatively influences the antecedents of a software professional’s self-efficacy and outcome expectations, namely encouragement from others, enactive mastery, vicarious experience, verbal persuasion and physiological arousal. Proposition 3: The lower the encouragement from others, enactive mastery, vicarious experience, verbal persuasion and physiological arousal, the lower the self-efficacy of a software professional. Proposition 4: The lower the encouragement from others, enactive mastery, vicarious experience, verbal persuasion and physiological arousal, the lower the outcome expectations of a software professional. Proposition 5: The lower the self-efficacy, the lower the outcome expectations of software professionals. Proposition 6: The lower the self-efficacy of software professionals, the lower the software development process efficiency. Proposition 7: The lower the outcome expectations of software professionals, the lower the software development process efficiency. Proposition 8: The lower the software development process efficiency, the lower the software product quality. Proposition 9: Downsizing negatively influences the antecedents of software development process efficiency, namely top management leadership, management infrastructure sophistication, process management efficacy and stakeholder participation. Proposition 10: Inadequate top management leadership, management infrastructure sophistication, process management efficacy and stakeholder participation reduce software development process efficiency.
literature. Prior research on software quality has not synthesized these divergent yet mutually influencing literature streams and hence this empirical lacuna. However, empirical support for these propositions exists in other research contexts. For example, Chong & Lopez (2005) found a direct positive relationship between social networks and psychological factors (P2) such as self-efficacy in an empirical study of women undergoing substance abuse treatment. Orwa (2004) and Ozgen (2003) found a similar relationship between entrepreneurial selfefficacy and social networks. The positive relationship between self-efficacy and outcome expectations, and performance (P6, P7) has been empirically substantiated in a wide range of disciplines (see Lane et al., 2004 for a literature review). Further, the specialized computer self-efficacy research stream (derived from the general self-efficacy construct presented in this paper), which includes outcome expectations in a computer usage construct, has established a positive relationship between cognitive factors and performance (see Compeau et al., 2006 for a detailed literature review). Also, Chilton et al. (2005), in a study of software developers, found evidence for a link among a developer’s environment, cognitive factors and productivity, albeit from a different theoretical lens (cognitive style and person–environment fit theories). Finally, Armstrong-Stassen et al. (2005), in a non-software development context, found a positive relationship between downsizing and quality management practices, from an organizational perspective (P9). The earlier divergent and piecemeal yet relevant empirical support indicates a general face validity of the research model proposed in this paper. While empirical support exists for the theoretical model albeit piecemeal and in other contexts, it is imperative that the model be tested as a whole in the context of software development in a downsizing environment. We propose that our model be tested through a
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
19
survey of software developers who would be our unit of analysis and key respondent. We suggest the respondents be chosen from both firms that have downsized and not downsized in the last 24 months. Data would be collected through a self-administered close-ended survey containing construct measurement items where the respondents would mark their agreement and disagreement with the statements on a 5- or 7-point scale. Measurement items would be developed from existing items in the literature, namely from Compeau & Higgins (1995b) for SCT measures, D’Aveni (1989) and Shah (2000) for downsizing measures, and Ravichandran & Rai (1999; 2000) for software quality performance measures. We further recommend the use of the Partial Least Squares (PLS) technique to evaluate the model. PLS, a second-generation analytical technique, has a higher power than first-generation techniques such as multiple regression (Chin et al., 2003). Also, PLS is better suited in theory development situations (Chin et al., 2003), as is our research. PLS can help test both the measurement and the structural models. It is possible that the data need to be augmented by further feedback from human resources (HR) personnel and senior IS managers, and we recommend that this be considered during the research design. DISCUSSION AND CONCLUSION
Organizational downsizing can be detrimental to the software development process; however, should downsizing be avoided at all costs? Are there situations where downsizing is unavoidable in spite of the detrimental effects? The various perspectives on the motivation for downsizing, namely economic, institutional (constraining, cloning and learning), sociocognitive and organizational decline, presented earlier provide a platform to address these questions. The organizational decline perspective addresses strategies when it is determined that an organization is no longer viable and is on the path to eventual demise (van Witteloostuijn, 1998). Here, downsizing is inevitable, and a phased downsizing would help the organization fulfil its obligations before it shuts down. The decline phase can last from several months to a few years. During this phase, IS are required for the functioning of organizations. The appropriate IS strategy in such situations will be to outsource any significant developmental activities. However, the remaining three perspectives offer insights into why organizations downsize, and consequently offer directions for IS management. The most common reason for downsizing is for economic efficiencies. HRs are expensive, and downsizing is an attractive opportunity to cut costs. But as discussed earlier, downsizing, in many instances, has failed to produce the strategized economic benefits in the long run. It is imperative that IS management provide its input when organizations downsize for economic reasons. IS management needs to compute the economic impact of software quality performance on account of such downsizing. This would help better evaluate the costs and benefits of downsizing for economic reasons. The nomological model of the impact of downsizing on software quality performance developed in this research can help organize and justify IS management’s arguments. IS management needs to be aware that there could be other reasons for downsizing, such as organizational norms or rules (constraining), industry leader imitation (cloning), training and
© 2009 Blackwell Publishing Ltd, Information Systems Journal
20
P J Ambrose & A Chiravuri
education (learning), and shared beliefs with other managers (socio-cognitive). Unless there is a compelling reason that downsizing is essential in a particular context, the previously given motivations do not constitute a valid reason for downsizing (McKinley & Mone, 1998). Again, it is imperative that IS management be aware of these motivations and appropriately question and establish the legitimacy of downsizing initiatives. The nomological model presented in this research can help IS management present their case to the organization on the ill effects of downsizing. However, IS management should also look at downsizing from the overall impact it has on the organization. If it is clearly established that downsizing is the best alternative in a given set of circumstances, then IS management needs to look at alternative IS strategies to counter the ill effects of downsizing on software quality performance. These strategies will be context specific and can include options such as increased outsourcing, training, change of management and knowledge management, to name a few. Further, IS and general management need to be aware of other specific ill effects of downsizing with respect to software quality performance, irrespective of whether the organization pursues downsizing initiatives or not, to achieve the overall interests of the organization. These are now discussed. Even though downsizing is often accompanied by restructuring (Isaac et al., 2004), the results have been less than positive. Restructuring consumes valuable organizational resources such as time and money. Consequently, rebuilding the organizational leadership, structure and processes to optimal levels may not be feasible in the short to mid-term. Downsizing not only destroys the formal and informal networks that are so important for employee support (Keller, 1989) but also leads to lower morale, commitment, and work effort by employees who survive the downsizing (McKinley et al., 1995), because of lack of encouragement and verbal persuasion, and reduced opportunities for learning and experiential development. The destruction of social networks that play a critical role in transporting information and facilitating work duties (Shah, 2000) reduces their ability to act as mechanisms of influence. As a result, the software professional loses his or her sources of information and his or her social support, and these, in turn, affect his or her self-efficacy and outcome expectations. Furthermore, the loss of social contacts intensifies the dissatisfaction that sets in from greater lay-off exposure and social isolation. This impacts a survivor’s attitudes and behaviours. Therefore, as noted in our nomological model, we argue that downsizing affects a software professional’s self-efficacy and outcome expectations. We further argue that software quality performance will suffer on two counts. One, the destruction of social networks will reduce the number of available domain experts. This, in turn, impacts the planning and analysis stages of the software development cycle because the software developers have few experts to work with for the requirements elicitation, thereby leading to a less optimal investigation of the problem. Two, because self-efficacy and outcome expectations are predictors of future performance, the lower the self-efficacy and outcome expectations of software developers, the lower the subsequent performance. This will impact the design and implementation phases whose success is largely dependent on the performance of software professionals. Therefore, downsizing, low self-efficacy and low outcome expectations can negatively impact the delivered systems quality performance.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
21
Downsizing indirectly influences the ability of the software to meet or exceed customer expectations during the use of the system. Software quality is not only dependent on the initial performance of the software but also on its maintenance. Low self-efficacy and outcome expectations that are caused by downsizing result in low performance of software personnel. Consequently, this can lead to more product defects materializing during use. This, in turn, can cause more rework, which is not only expensive but can also result in person-hours being wasted. The low level of performance of the software personnel can also mean revisiting the defects and result in suboptimal maintenance. Downsizing also influences the post-implementation stage of the software development cycle, which is not explicitly covered in our conceptual model. For example, downsizing can reduce the number of direct and indirect users of the software artefact. This can lead to the software artefact not being used to its fullest potential. Further, downsizing can disrupt the internal support for the software developed when sponsors of projects and champions of innovation are laid off (Dougherty & Bowman, 1995). Dougherty & Bowman (1995) found that once a sponsor of a project was laid off because of downsizing, the projects were either left adrift or made vulnerable to dismantling by new managers. Similarly, loss of champions is especially detrimental because these highly motivated persons are the first adopters of a new technology; they test the new technologies, take care of the initial glitches and drive project implementation (Al-Mashari & Zairi, 1999; Paar & Shanks, 2000; Willcocks & Sykes, 2000). The project champion also plays a fundamental role in change management efforts throughout the implementation life cycle (Brown & Vessey, 1999). Additionally, the destruction of networks because of downsizing results in the loss of an informal support mechanism for the software professional. This can create an impersonal work environment for the software professional. Although firms cannot prevent all the unwanted effects arising from downsizing, it is possible for them to mitigate some of the negative effects arising from the environmental factors, as mentioned in our nomological model, by engaging in thoughtful planning, auditing their human resources, facilitating open discussion with their stakeholders and going about the exercise in a phased manner. Specific strategies to alleviate any potential negative consequences are presented below. First, firms need to set clear objectives in measurable terms and monitor their resources and other assets carefully when engaging in downsizing so that these can be used as a means of control to ensure that the restructuring that follows downsizing does not end up as a drain on a firm’s resources through project delays and increased spending. Second, monitoring human assets is critical during downsizing, and it is recommended that firms conduct a human resources audit. Far too often, firms lay off invaluable human assets such as champions and domain experts during restructuring to lower headcounts and cut costs but find themselves spending a lot more to re-employ them later, sometimes as consultants. Auditing human resources before downsizing will allow firms to identify and retain human assets such as leaders, champions, and domain experts and enable and enhance system quality in areas such as design and maintenance. In addition, an audit will ensure that a firm does not lose its innovativeness and competitive advantage by ensuring the retention of its human assets. Third, firms that engage in downsizing in a phased manner give a software professional or
© 2009 Blackwell Publishing Ltd, Information Systems Journal
22
P J Ambrose & A Chiravuri
‘survivors’ sufficient time to build new networks, both formal and informal, resulting in less detrimental effects arising from issues of morale and commitment. In short, while the effects of downsizing are detrimental to a firm and software professionals, the actions recommended earlier could result in retaining leadership, facilitating networks and structures, and improving process efficacy, thereby mitigating the effects of environmental factors on the personal factors of software professionals, as laid out in our conceptual model. To conclude, this paper argues that downsizing has unintended effects on the software development process because of its negative effects on the self-efficacy and outcome expectations of a software professional. While downsizing is inevitable in a state of organizational decline, it must be reconsidered as a strategy at other times. Organizations need to have a larger perspective and examine the impact of downsizing on software development. In particular, downsizing affects the software process efficiency by its impact on a software professional’s self-efficacy and outcome expectations. Software managers should realize that both formal and informal networks are important for software development quality and must carefully evaluate downsizing before it is used as a means to increase economic efficiencies. A detailed evaluation may reveal that not only do the perceived benefits never materialize, but also that the quality of processes and products may suffer, leading to long-term damage to the firm. Some limitations are that this study concerns itself only with the impact of downsizing on individual-level constructs. Future studies can examine the issue of the impact of downsizing on organizational-level constructs. Future research can empirically test the conceptual model proposed in this paper. Also, the model does not specify recursive relationships among environmental, cognitive and behavioural factors. Including these recursive relationships would dilute the main argument proposed in this paper, and model parsimony would also be lost. We believe that the arguments laid out in this study do offer additional insights into why software errors occur, with the hope that it leads to a more carefully planned downsizing of IS and other organizational professionals.
REFERENCES Agrawal, M. & Chari, K. (2007) Software effort, quality, and cycle time: a study of CMM level 5 projects. IEEE Transactions on Software Engineering, 33, 145–156. Ahuja, M.K. (2002) Women in the information technology profession: a literature review, synthesis and research agenda. European Journal of Information Systems, 11, 20–34. Al-Mashari, M. & Zairi, M. (1999) BPR implementation process: an analysis of key success and failure factors. Business Process Management Journal, 5, 87–107. Aladwani, A.M. (2002) An integrated performance model of information systems projects. Journal of Management Information Systems, 19, 185–210.
Amabile, T.M. & Conti, R. (1999) Changes in the work environment for creativity during downsizing. Academy of Management Journal, 42, 630–640. Ang, S. & Slaughter, S. (2004) Turnover of information technology professionals: the effects of internal labor market strategies. Database for Advances in Information Systems, 35, 11–27. Armstrong-Stassen, M., Reavley, M. & Ghanam, D. (2005) Organizational downsizing and its perceived impact on quality management practices. The International Journal of Quality & Reliability Management, 22, 950–967. Arthur, L.J. (1997) Quantum improvements in software system quality. Communications of the ACM, 40, 46–52.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
Austin, R.D. (2001) The effects of time pressure on quality in software development: an agency model. Information Systems Research, 12, 195–207. Bandura, A. (1977a) Self efficacy: toward a unifying theory of behavioral change. Psychological Review, 84, 191– 215. Bandura, A. (1977b) Social Learning Theory. PrenticeHall, Englewood Cliffs, NJ, USA. Bandura, A. (1978) Reflections on self-efficacy. Advances in Behavioral Research and Therapy, 1, 237–269. Bandura, A. (1982) Self efficacy mechanism in human agency. American Psychologist, 37, 122–147. Bandura, A. (1986) Self Efficacy, Social Foundations for Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ, USA. Bandura, A. (1997) Self Efficacy: The Exercise of Control. W. H. Freeman, New York, NY, USA. Bandura, A., Adams, N.E. & Beyer, J. (1977) Cognitive processes mediating behavioral change. Journal of Personality and Social Psychology, 35, 125–139. Barling, J. & Beattie, R. (1983) Self-efficacy beliefs and sales performance. Journal of Organizational Behavior Management, 5, 41–51. Basili, V.R. & Caldiera, G. (1995) Improve software quality by reusing knowledge and experience. Sloan Management Review, 37, 55–64. Basu, A.K. & Hastak, M. (1990) Multiattribute judgments under uncertainty: a conjoint measurement approach. Advances in Consumer Research, 17, 554–562. Benson, G.S. (2006) Employee development, commitment and intention to turnover: a test of ‘employability’ policies in action. Human Resource Management Journal, 16, 173–192. Boehm, B.W. (1978) Characteristics of Software Quality. North-Holland Pub. Co., Amsterdam, the Netherlands. Bowen, P.L., Heales, J. & Vongphakdi, M.T. (2002) Reliability factors in business software: volatility, requirements and end-users. Information Systems Journal, 12, 185–213. Brown, C.V. & Vessey, I. (1999) ERP implementation approaches: toward a contingency framework. In the proceedings of the 20th International Conference on Information Systems, Charlotte, NC, USA, pp. 411–416. Brown, J.S. & Duguid, P. (1991) Organizational learning and communities-of-practice: toward a unified view of working, learning, and innovating. Organization Science, 2, 40–57. Buch, K. & Aldridge, J. (1991) O.D. under conditions of organization decline. Organization Development Journal, 9, 1–5.
23
Budgen, D. (2003) Software Design. Addison-Wesley, Harlow, UK; New York, USA. Burt, R.S. (1987) Social contagion and innovation: cohesion versus structural equivalence. American Journal of Sociology, 92, 1287–1335. Cameron, K.S., Freeman, S.J. & Mishra, A.K. (1991) Best practices in white-collar downsizing: managing contradictions. Academy of Management Executive, 5, 57–73. Carmeli, A. & Schaubroeck, J. (2006) Top management team behavioral integration, decision quality, and organizational decline. Leadership Quarterly, 17, 441–453. Cascio, W.F. (1993) Downsizing: what do we know? What have we learned? Academy of Management Executive, 7, 95–104. Chilton, M.A., Hardgrave, B. & Armstrong, D.J. (2005) Person-job cognitive style fit for software developers: the effect on strain and performance. Journal of Management Information Systems, 22, 193–226. Chin, W.W., Marcolin, B.L. & Newsted, P.R. (2003) A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/ adoption study. Information Systems Research, 14, 189–217. Chong, J. & Lopez, D. (2005) Social networks, support, and psychosocial functioning among American Indian women in treatment. American Indian and Alaska Native Mental Health Research (Online), 12, 62–85. Compeau, D., Higgins, C.A. & Huff, S. (1999) Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23, 145– 158. Compeau, D., Gravill, J., Haggerty, N. & Kelley, H. (2006) Computer self-efficacy: a review. In: Human–Computer Interaction and Management Information Systems – Foundations, Vol. 4, Zhang, P. & Galletta, D. (eds.), pp. 225–261. M.E. Sharpe, Inc., Armonk, NY, USA. Compeau, D.R. & Higgins, C.A. (1995a) Application of social cognitive theory to training for computer skills. Information Systems Research, 6, 118–143. Compeau, D.R. & Higgins, C.A. (1995b) Computer selfefficacy: development of a measure and initial test. MIS Quarterly, 19, 189–211. Cossick, K.L., Byrd, T.A. & Zmud, R.W. (1992) A synthesis of research on requirements analysis and knowledge acquisition techniques. MIS Quarterly, 16, 117–138. Costabile, M.F., Fogli, D., Lanzilotti, R., Mussio, P. & Piccinno, A. (2006) Supporting work practice through end-user development environments. Journal of Organizational & End User Computing, 18, 43–65.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
24
P J Ambrose & A Chiravuri
Cusumano, M.A. & Selby, R.W. (1997) How Microsoft builds software. Communications of the ACM, 40, 53–61. D’Aveni, R.A. (1989) Dependability and organization bankruptcy: an application of agency and prospect theory. Management Science, 35, 1120–1138. Davis, F.D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 318–340. Davis, F.D., Bagozzi, R.P. & Warshaw, P.R. (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982– 1003. De Meuse, K.P., Bergmann, T.J., Vanderheiden, P.A. & Roraff, C.E. (2004) New evidence regarding organizational downsizing and a firm’s financial performance: a long-term analysis. Journal of Managerial Issues, 16, 155–177. Deming, W.E. (1986) Out of the Crisis. Massachusetts Institute of Technology, Center for Advanced Engineering Study, Cambridge, MA, USA. DiMaggio, P.J. & Powell, W.W. (1983) The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160. Dougherty, D. & Bowman, E.H. (1995) The effects of organizational downsizing on product innovation. California Management Review, 37, 3–44. Dunn, R.H. (1988) Software quality assurance: a management perspective. Quality Progress, 21, 52–56. Ferneley, E.H. (2007) Covert end user development: a study of success. Journal of Organizational & End User Computing, 19, 62–71. Fisher, S.R. & White, M.A. (2000) Downsizing in a learning organization: are there hidden costs? Academy of Management Review, 25, 244–251. Freeman, S.J. & Cameron, K.S. (1993) Organizational downsizing: a convergence and reorientation framework. Organization Science, 4, 10–29. Fulbright, R. & Routh, R.L. (2004) How information technology professionals keep from being outsourced or offshored. In the proceedings of the 5th Conference on Information Technology Education, Salt Lake City, UT, USA, pp. 188–193. Galaskiewicz, J. & Wasserman, S. (1989) Mimetic processes within an interorganizational field: an empirical test. Administrative Science Quarterly, 34, 454–479. Gardner, W.L. & Rozell, E.J. (2000) Computer efficacy: determinants, consequences, and malleability. Journal of High Technology Management Research, 11, 109–136.
Gist, M.E. (1987) Self-efficacy: implications for organizational behavior and human resource management. Academy of Management Review, 12, 472–485. Gist, M.E. & Mitchell, T.B. (1992) Self-efficacy: a theoretical analysis of its determinants and malleability. Academy of Management Review, 17, 183–211. Granger, M.J., Dick, G., Luftman, J., Slyke, C.V. & Watson, R.T. (2007) Information systems enrollments: can they be increased? Communications of the Association for Information Systems, 20, 649–659. Gravill, J.I., Compeau, D.R. & Marcolin, B.L. (2006) Experience effects on the accuracy of self-assessed user competence. Information & Management, 43, 378–394. Guerrera, F., Knight, R. & Waters, R. (2006) Wipro warns of US skill shortage information technology. Financial Times, 26 October 2006, p. 26. Haines, C.G., CarneyI, D. & Foreman, J. (1997) Component-Based Software Development/COTS Integration. Software Engineering Institute, Carnegie Mellon University, Pittsburg, PA, USA. Harrison, N.B. & Coplien, J.O. (1996) Patterns of productive software organizations. Bell Labs Technical Journal, 1, 138–145. Harter, D.E. & Slaughter, S.A. (2003) Quality improvement and infrastructure activity costs in software development: a longitudinal analysis. Management Science, 49, 784–800. Harter, D.E., Krishnan, M.S. & Slaughter, S.A. (2000) Effects of process maturity on quality, cycle time, and effort in software product development. Management Science, 46, 451–466. Hasan, B. (2006) Effectiveness of computer training: the role of multilevel computer self-efficacy. Journal of Organizational & End User Computing, 18, 50–68. Hayes, F. (2002) IT transformed. Computerworld, 36, 2 September 2002, 54. Heemstra, F.J. & Kusters, R.J. (2002) Soft factors affecting software quality. Software Quality Professional, 5, 20–30. Hill, C., Yates, R., Jones, C. & Kogan, S.L. (2006) Beyond predictable workflows: enhancing productivity in artful business processes. IBM Systems Journal, 45, 663–682. Hill, T. & Smith, N.D. (1987) Role of efficacy expectations in predicting the decision to use advanced technologies: the case of computers. Journal of Applied Psychology, 72, 307–313. Hill, T., Smith, N.D. & Mann, M.F. (1985) Communicating innovations: convincing computer phobics to adopt innovative technologies. In the proceedings of the Advances in Consumer Research, Provo, UT, USA, pp. 419–422.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
Humphrey, W.S. (1989) Managing the Software Process. Addison-Wesley, Reading, MA, USA. Hurley, R.F. (2006) The decision to trust. Harvard Business Review, 84, 55–62. Ibarra, H. (1993) Network centrality, power, and innovation involvement: determinants of technical and administrative roles. Academy of Management Journal, 36, 471. InformationWeek (2005) Behind the numbers: IT midlife crisis. InformationWeek, 1061, 24 October 2005, 94. Isaac, G., Rajendran, C. & Anantharaman, R.N. (2004) A holistic framework for TQM in the software industry: a confirmatory factor analysis approach. The Quality Management Journal, 11, 35–60. ISO (1991) Information Technology Software Product Evaluation: Quality Characteristics and Guidelines for Their Use, ISO 9126. International Organization for Standardization, Geneva, Switzerland. Jalote, P. (2000) CMM in Practice: Processes for Executing Software Projects at Infosys. Addison-Wesley, Reading, MA, USA. Juran, J.M. (1992) Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services. Free Press, New York, USA. Keller, M. (1989) Rude Awakening: The Rise, Fall, and Struggle for Recovery of General Motors. Morrow, New York, USA. Kessler, M. (2005) Fewer students major in computer; some fear shortage of U.S. workers, more outsourcing. USA Today, 23 May 2005, p. B1. Kraiger, K., Ford, J.K. & Salas, E. (1993) Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78, 311–328. Krueger, C.W. (2006) New methods in software product line practice. Communications of the ACM, 49, 37– 40. Lane, J., Lane, A.M. & Kyprianou, A. (2004) Self-efficacy, self-esteem and their impact on academic performance. Social Behavior & Personality: An International Journal, 32, 247–256. Lawrence Pfleeger, S. & Mertz, N. (1995) Executive mentoring: what makes it work? Communications of the ACM, 38, 63–73. Levine, J.M. & Moreland, R.L. (1990) Progress in small group research. Annual Review of Psychology, 41, 585– 634. Mahanti, R. & Antony, J. (2005) Confluence of six sigma, simulation and software development. Managerial Auditing Journal, 20, 739–752.
25
Manz, C.C. & Sims, H.P. (1981) Vicarious learning: the influence of modeling on organizational behavior. The Academy of Management Review, 6, 105–113. Marakas, G.M., Yi, M.Y. & Johnson, R.D. (1998) The multilevel and multifaceted character of computer selfefficacy: toward clarification of the construct and an integrative framework for research. Information Systems Research, 9, 126–163. Marks, M.L. (2006) Workplace recovery after mergers, acquisitions, and downsizings: facilitating individual adaptation to major organizational transitions. Organizational Dynamics, 35, 384–399. Mathers, J.F. (2005) An exploratory investigation of the interrelationships of experiential learning, outcome expectations, self-efficacy, and goals in clinical practica students. PhD dissertation, Department of Educational Studies, Purdue University, West Lafayette, IN, USA. McCall, J., Richards, P. & Walters, G. (1977) Factors in Software Quality, NTIS AD-A049-014, 015, 055. National Technical Information Service, US Department of Commerce, Springfield, VA, USA. McKinley, W. & Mone, M.A. (1998) Some ideological foundations of organizational downsizing. Journal of Management Inquiry, 7, 198–212. McKinley, W., Sanchez, C.M. & Schick, A.G. (1995) Organizational downsizing: constraining, cloning, learning. Academy of Management Executive, 9, 32–42. McKinley, W., Zhao, J. & Rust, K.G. (2000) A sociocognitive interpretation of organizational downsizing. Academy of Management Review, 25, 227–243. Molinsky, A. & Margolis, J. (2006) The emotional tightrope of downsizing: hidden challenges for leaders and their organizations. Organizational Dynamics, 35, 145–159. Mone, M.A., McKinley, W. & Barker III, V.L. (1998) Organizational decline and innovation: a contingency framework. Academy of Management Review, 23, 115–132. Niederman, F. & Brancheu, J.C. (1991) Information systems management issues for the 1990s. MIS Quarterly, 15, 475–500. Orwa, B.O. (2004) An examination of factors influencing entrepreneurial opportunity identification process. PhD dissertation, Human Resource Education, University of Illinois at Urbana-Champaign, IL, USA. Ozgen, E. (2003) Entrepreneurial opportunity recognition: information flow, social and cognitive perspectives. Ph.D dissertation, Lally School of Management and Technology, Rensselaer Polytechnic Institute, New York, USA. Paar, A. & Shanks, G. (2000) A model of ERP project implementation. Journal of Information Technology, 15, 289–303.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
26
P J Ambrose & A Chiravuri
Parzinger, M.J. & Nath, R. (2000) A study of the relationships between total quality management implementation factors and software quality. Total Quality Management, 11, 353–371. Paulk, M.C., Curtis, B., Chrissis, M.B. & Weber, C.V. (1993) Capability Maturity Model, Version 1.1. IEEE Software, 10, 18–27. Rai, A. & Song, H. (1998) Software quality assurance: an analytical survey and research prioritization. Journal of Systems & Software, 40, 67–83. Ranganathan, C. & Samant, K. (2006) Information technology personnel layoffs in US organizations: an exploratory investigation. Information & Management, 43, 239–250. Ravichandran, T. & Rai, A. (1999) Total quality management in information systems development: key constructs and relationships. Journal of Management Information Systems, 16, 119–155. Ravichandran, T. & Rai, A. (2000) Quality management in systems development: an organizational system perspective. MIS Quarterly, 24, 381–415. Resnick, B., Luisi, D., Vogel, A. & Junaleepa, P. (2004) Reliability and validity of the self-efficacy for exercise and outcome expectations for exercise scales with minority older adults. Journal of Nursing Measurement, 12, 235–247. Rondeau, P.J., Vonderembse, M.A. & Ragu-Nathan, T.S. (2002) Investigating the level of end-user development and involvement among time-based competitors. Decision Sciences, 33, 149–160. Royce, W. (2002) CMM vs. CMMI: from conventional to modern software management. Rational Edge. [WWW document]. URL http://download.boulder.ibm.com/ ibmdl/pub/software/dw/rationaledge/feb02/ ConventionalToModernFeb02.pdf Shah, P.P. (2000) Network destruction: the structural implications of downsizing. Academy of Management Journal, 43, 101–112. Slaughter, S.A., Levine, L., Ramesh, B., Pries-Heje, J. & Baskerville, R. (2006) Aligning software processes with strategy. MIS Quarterly, 30, 891–918. Slocum, J.W. Jr, Morris, J.R., Cascio, W.F. & Young, C.E. (1999) Downsizing after all these years: questions and answers about who did it, how many did it, and who benefited from it. Organizational Dynamics, 27, 78–87. Stajkovic, A.D. & Luthans, F. (1998) Self-efficacy and work-related performance: a meta-analysis. Psychological Bulletin, 124, 240–261. Stone, D.N., Arunachalam, V. & Chandler, J.S. (1996) An empirical investigation of knowledge, skill, self-efficacy
and computer anxiety in accounting education. Issues in Accounting Education, 11, 345–376. Sutcliffe, A. & Mehandjiev, N. (2004) End-user development. Communications of the ACM, 47, 31– 32. Tassey, G. (2002) The Economic Impacts of Inadequate Infrastructure for Software Testing. National Institute of Standards and Technology – US Department of Commerce, Gaithersberg, MD, USA. [WWW document]. URL http://www.nist.gov/director/prog-ofc/report02-3.pdf (last accessed 29 April 2007). Taylor, M.S., Locke, E.A., Lee, C. & Gist, M.E. (1984) Type a behavior and faculty research productivity: what are the mechanisms? Organizational Behavior & Human Performance, 34, 402–418. Uchitelle, L. (2006) The Disposable American: Layoffs and Their Consequences. Knopf, New York, USA. Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D. (2003) User acceptance of information technology: toward a unified view. MIS Quarterly, 27, 425–478. Vitharana, P. & Mone, M.A. (1998) Critical factors of software quality management. In the proceedings of the 4th Americas Conference on Information Systems, Baltimore, MD, USA, pp. 906–908. Webster, J. & Martocchio, J.J. (1992) Microcomputer playfulness: development of a measure with workplace implications. MIS Quarterly, 16, 201–226. Webster, J. & Martocchio, J.J. (1993) Turning work into play: implications for microcomputer software training. Journal of Management, 19, 127–146. Weiss, T.R. (2005) Tech job cuts up 18%. Computerworld, 39, 7 November 2005, 46. Willcocks, L.P. & Sykes, R. (2000) The role of the CIO and IT function in ERP. Communications of the ACM, 43, 32–38. van Witteloostuijn, A. (1998) Bridging behavioral and economic theories of decline: organizational intertia, strategic, and chronic failure. Management Science, 44, 501– 519. Wood, R.E. & Locke, E.A. (1987) The relation of selfefficacy and grade goals to academic performance. Educational and Psychological Measurement, 47, 1013– 1024. Wynekoop, J.L. & Walz, D.B. (2000) Investigating traits of top performing software developers. Information Technology & People, 13, 186–195. Yourdon, E. (1992) Decline & Fall of the American Programmer. Yourdon Press, Englewood Cliffs, NJ, USA. Zatzick, C.D. & Iverson, R.D. (2006) High-involvement management and workforce reduction: competitive
© 2009 Blackwell Publishing Ltd, Information Systems Journal
Potential effects of downsizing on software quality performance
advantage or disadvantage? Academy of Management Journal, 49, 999–1015.
Biographies Paul J. Ambrose is an Assistant Professor of Management Computer Systems in the Department of Information Technology/Business Education, University of Wisconsin – Whitewater. He received his PhD in Business Administration with a specialization in Management Information Systems from Southern Illinois University at Carbondale. Prior to a career in academia, he has worked in managerial positions in India and Hong Kong. Dr Ambrose has published in journals such as the IEEE Transactions on Engineering Management, Communications of the ACM, International Journal of Operations and Production Management, Logistics Information Management, and in
27
several leading IS conferences. His research interests include the use and impact of internet technologies in knowledge-intensive work processes such as clinical decision-making, eBusiness, and the evaluation of IS success. Ananth Chiravuri holds a PhD in Management Science with a specialization in MIS from the University of Wisconsin – Milwaukee. Presently, he is an Assistant Professor of Management Information Systems at the American University of Sharjah. His current research interests are in the areas of knowledge management, IS downsizing, and e-commerce. He has published over 13 papers in refereed journals and conferences in the USA including Hawaii International Conference on System Sciences (HICSS) and is the recipient of many research grants. In the past, his teaching has been recognized with various awards. He has also served as a reviewer for various IS journals and conferences.
© 2009 Blackwell Publishing Ltd, Information Systems Journal
