Reference: El Sawy, O.A. and Pavlou, P.A. (2008) IT-enabled business capabilities for turbulent environments, MIS Quarterly Executive 7:3, 139-150.
El Sawy and Pavlou, who have written several articles on the subject of innovation, conclude that strategic advantage requires a “trifecta” of operational, dynamic, and improvisational business capabilities. It’s easy to see why companies need to achieve operational excellence to succeed in almost any environment. El Sawy and Pavlou find, however, that dynamic and improvisational capabilities are necessary for success in turbulent environments, and that in the most turbulent environment, improvisational capabilities are most important. Furthermore, a company’s information technology capabilities need to be aligned and structured properly to support the desired mix of operational vs. dynamic and improvisational capabilities.
Monideepa Tarafdar and I have observed that innovative companies require an ability to achieve and balance operational excellence with strategic vision (Tarafdar, M. and Gordon, S., 2007), a competency we call “ambidexterity,” after O’Reilly and Tushman (2004) and Vinekar et al (2006). In our model, strategic vision relies upon what El Sawy and Pavlou term dynamic and improvisational capabilities, those that allow an organization to respond to the external environment. To this extent, our findings support El Sawy and Pavlou and vice versa.
What I found most interesting in this article is the authors’ division of capabilities for responding to the dynamic environment into two parts – dynamic and improvisational. They define “dynamic capabilities” as those needed to “effectively reconfigure existing operational capabilities to match the changing business environment.” They define “improvisational capabilities” as “the learned ability to spontaneously reconfigure existing resources in real time to build new operational capabilities that better match novel environmental situations.” From this definition, it seems that improvisational capabilities are simply a subset of dynamic capabilities. So, I’m struggling to understand if these really are substantially different capabilities, and if so, whether they require or build upon different information technology capabilities.
El Sawy’s and Pavlou’s model of “dynamic capabilities” includes four dimensions: environment-sensing, learning, knowledge integrating, and coordinating. Three of these dimensions (competencies?) clearly contribute to a firm’s ability to respond to a dynamic environment. Specifically, a firm cannot possibly respond to changes it cannot sense. So, environment sensing is critical. Also, it cannot respond if it cannot learn the skills and capabilities it might need in a changed environment. I’m not sure that coordination is a critical dynamic capability. Although I might not eliminate it as a dynamic capability, it seems more important as an operational capability. That said, operational capabilities, such as for leadership, flexibility, and governance, could also be equally important as dynamic capabilities.
The difficulty in deciding where coordination belongs in a model of business capabilities highlights the problems inherent in building such a model. And, it motivates the question of whether improvisation better understood one of the elements of the business capabilities trifecta or if it is more appropriately classified as a dimension of dynamic capability. If the other dimensions of dynamic capability are, implicitly, non-improvisational – that they are, in some sense, planned – then improvisation is orthogonal to them and would be better classified as a dimension of dynamic capability. Alternatively, if the other dimensions of dynamic capabilities can be achieved in both a planned and improvisational way, then it makes more sense to treat to treat them as both dynamic and improvisational capabilities, in which case the improvisational classification is needed to complete the trifecta.
While either model can work, I think it makes more sense to organize improvisation as a dimension of dynamic capability. For the most part, environment sensing is a planned activity, and the capability is not subject to a great deal of improvisation. Learning and integrating have more opportunities for improvisation, but also, especially at the organizational level, they are capabilities more organized than improvisational. So, I would argue, that ability to improvise is just one more dynamic capability.
What IT capabilities and infrastructure are necessary to support an improvisational capability? The authors provide some answers – I won’t go into them here – but clearly, more research is needed in this area.
O’Reilly, C.A., Tushman, M.L., 2004. Ambidextrous organization. Harvard Business Review 82 (4), 71–81.
Tarafdar, M. and Gordon, S., 2007. Understanding the influence of information systems competencies on process innovation: A resource-based view, Journal of Strategic Information Systems 16, 353-392.
Vinekar, Vishnu, Slinkman, Craig W., Nerur, Sridhar, 2006. Can agile and traditional systems development approaches coexist? An ambidextrous view. Information Systems Management 23 (3), 31–42.
Tuesday, May 19, 2009
Sunday, April 19, 2009
Responding to Disruptive Technology
Reference: Lucas, H.C. and Goh, J.M. (2009) Disruptive technology: How Kodak missed the digital photography revolution, Journal of Strategic Information Systems 18(1), 46-55.
I was drawn to this paper because I study innovation. My research concerns how information technology can be used to improve the innovation process, but I am also interested in understanding how companies can and should respond to innovations in information technologies that could affect the value of their products and services and ultimately their financial health.
The authors propose two extensions to Christensen’s well known treatises on disruptive technologies. The first is the notion that a firm’s response to disruptive technologies is a “struggle between employees who seek to use dynamic capabilities to bring about change, and employees for whom core capabilities have become core rigidities.” In examining Kodak, the authors focus on middle management as being most problematic and resistant to change, being dependent on core competencies that have become core rigidities. The concept of core rigidities is rooted in Christensen’s work and could hardly be considered an extension. The role of dynamic capabilities, however, does not come directly into play in Christensen’s work. Interestingly, Christensen refers to dynamic capabilities in “The Innovator’s Solution” (Christensen & Raynor, 2003, p. 206), but dismisses the concept as an over-broad categorization of organizational processes. Nevertheless, the suggestion that dynamic capabilities can help companies respond to disruptive technologies is not entirely new. For example, the March issue of the Journal of Engineering and Technology Management is devoted to this principle, as reflected in the introductory article, “Research on corporate radical innovation systems - A dynamic capabilities perspective: An introduction,” by Salomo, Gemünden, and Leifer.
The second “extension” proposed by the authors is consideration of the role of organizational culture. The authors argue that if organizational culture promotes hierarchy and maintenance of the status quo, it would impede the change required to react to disruptive technologies. It’s not clear that this is really an extension of Christensen’s work, specifically because Christensen acknowledges the role of culture in creating core rigidities (see, for example, HBS Note 9-399-104, “What is an Organization Culture,” Rev August 2, 2006).
The Kodak case is an interesting one. Implicit in the analysis is that Kodak failed to respond adequately to the digital revolution. But, it’s not clear that Kodak could have done anything more than it did. Prior to the digital revolution, Kodak’s multi-billion dollar revenue stream depended primarily on sales of its film, developer chemicals, and halide paper used to make photographic prints. These sources of revenue were destined to disappear. In the digital world, other sources of revenue exist, but they are largely commoditized, with limited revenue generating capability. What is surprising is that Kodak has, nevertheless, managed to emerge as a viable business, unlike Polaroid, for example. Although it doesn’t have a dominant position, as it did when photography was based solely on film, it reacted quickly to the digital revolution, with early patents on digital photography and acquisition of companies such as Ofoto and Scitex.
I was drawn to this paper because I study innovation. My research concerns how information technology can be used to improve the innovation process, but I am also interested in understanding how companies can and should respond to innovations in information technologies that could affect the value of their products and services and ultimately their financial health.
The authors propose two extensions to Christensen’s well known treatises on disruptive technologies. The first is the notion that a firm’s response to disruptive technologies is a “struggle between employees who seek to use dynamic capabilities to bring about change, and employees for whom core capabilities have become core rigidities.” In examining Kodak, the authors focus on middle management as being most problematic and resistant to change, being dependent on core competencies that have become core rigidities. The concept of core rigidities is rooted in Christensen’s work and could hardly be considered an extension. The role of dynamic capabilities, however, does not come directly into play in Christensen’s work. Interestingly, Christensen refers to dynamic capabilities in “The Innovator’s Solution” (Christensen & Raynor, 2003, p. 206), but dismisses the concept as an over-broad categorization of organizational processes. Nevertheless, the suggestion that dynamic capabilities can help companies respond to disruptive technologies is not entirely new. For example, the March issue of the Journal of Engineering and Technology Management is devoted to this principle, as reflected in the introductory article, “Research on corporate radical innovation systems - A dynamic capabilities perspective: An introduction,” by Salomo, Gemünden, and Leifer.
The second “extension” proposed by the authors is consideration of the role of organizational culture. The authors argue that if organizational culture promotes hierarchy and maintenance of the status quo, it would impede the change required to react to disruptive technologies. It’s not clear that this is really an extension of Christensen’s work, specifically because Christensen acknowledges the role of culture in creating core rigidities (see, for example, HBS Note 9-399-104, “What is an Organization Culture,” Rev August 2, 2006).
The Kodak case is an interesting one. Implicit in the analysis is that Kodak failed to respond adequately to the digital revolution. But, it’s not clear that Kodak could have done anything more than it did. Prior to the digital revolution, Kodak’s multi-billion dollar revenue stream depended primarily on sales of its film, developer chemicals, and halide paper used to make photographic prints. These sources of revenue were destined to disappear. In the digital world, other sources of revenue exist, but they are largely commoditized, with limited revenue generating capability. What is surprising is that Kodak has, nevertheless, managed to emerge as a viable business, unlike Polaroid, for example. Although it doesn’t have a dominant position, as it did when photography was based solely on film, it reacted quickly to the digital revolution, with early patents on digital photography and acquisition of companies such as Ofoto and Scitex.
Wednesday, April 8, 2009
Does Task-Technology Fit Matter?
Reference: Fuller, R.M. and Dennis, A.R. (2009), Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks, Information Systems Research 20(1), 2-17.
This is an important piece of research. It’s not often that new research dispels or substantially modifies well accepted models of how things work. This is one such example. Prior research has supported the intuitive belief that the fit between a technology and the task to which it is applied significantly affects success in performing the task. Presumably, the poorer the fit between a task and the technology used to perform it, the worse the performance of the task. This “Task-Technology Fit” theory was first formalized in MISQ in 1995 (Goodhue) and has been extensively analyzed, developed, and verified by subsequent research. However, as Fuller and Dennis show, the TTF theory is incorrect when applied to a repeated task. It turns out that by the third time the task is repeated, users will have figured out how to adapt the technology and the way the task is accomplished so that there is no significant difference in their performance or their perception of the technology. While poor-fit teams failed to equal the performance of well-fit teams in initial task performance, the differences between them melted away over time, becoming indistinguishable by the third repetition of the task!
This finding, while surprising and counter-intuitive, has some theoretical grounding. It is rooted in Adaptive Structuration Theory (AST) (Desanctis & Poole, 1994). AST holds that in performing a task, people adapt the elements of the tools that they use, the features they select, the rules they apply, and the way that they apply them. This process, called appropriation, allows them to improve their performance over time. Prior research has, indeed, recognized the role of appropriation in explaining the performance of teams using information technology. The “Fit-Appropriation Model,” (FAM) (Dennis et al, 2001) holds that performance is affected by both technology fit and appropriation. But, until now, there was no recognition of the possibility, much less the likelihood, that appropriation would ever dominate over fit, and certainly not in such a short period of time.
This research is limited to a single context, task, and technology, and used students as research subjects. Generalizability still needs to be established. However, assuming that the findings stand up to further scrutiny, they are ground breaking.
This is an important piece of research. It’s not often that new research dispels or substantially modifies well accepted models of how things work. This is one such example. Prior research has supported the intuitive belief that the fit between a technology and the task to which it is applied significantly affects success in performing the task. Presumably, the poorer the fit between a task and the technology used to perform it, the worse the performance of the task. This “Task-Technology Fit” theory was first formalized in MISQ in 1995 (Goodhue) and has been extensively analyzed, developed, and verified by subsequent research. However, as Fuller and Dennis show, the TTF theory is incorrect when applied to a repeated task. It turns out that by the third time the task is repeated, users will have figured out how to adapt the technology and the way the task is accomplished so that there is no significant difference in their performance or their perception of the technology. While poor-fit teams failed to equal the performance of well-fit teams in initial task performance, the differences between them melted away over time, becoming indistinguishable by the third repetition of the task!
This finding, while surprising and counter-intuitive, has some theoretical grounding. It is rooted in Adaptive Structuration Theory (AST) (Desanctis & Poole, 1994). AST holds that in performing a task, people adapt the elements of the tools that they use, the features they select, the rules they apply, and the way that they apply them. This process, called appropriation, allows them to improve their performance over time. Prior research has, indeed, recognized the role of appropriation in explaining the performance of teams using information technology. The “Fit-Appropriation Model,” (FAM) (Dennis et al, 2001) holds that performance is affected by both technology fit and appropriation. But, until now, there was no recognition of the possibility, much less the likelihood, that appropriation would ever dominate over fit, and certainly not in such a short period of time.
This research is limited to a single context, task, and technology, and used students as research subjects. Generalizability still needs to be established. However, assuming that the findings stand up to further scrutiny, they are ground breaking.
Thursday, March 26, 2009
One More Benefit of Good Web Design
Reference: Parboteeah, D.V., Valacich, J.S., and Wells, J.D. (2008) The influence of website characteristics on a consumer's urge to buy impulsively, Information Systems Research 20 (1), 60-78.
It's nice to know that a website's task-relevant cues (ie., appropriate information and good navigation) and mood-relevant cues (ie., visually appeal) increase consumers' urge to buy impulsively. Not that any designer would ever want to create a website deficient in either of these attributes.
While I have some doubts about the relevance of this study, I have little reason to doubt that improving website quality improves outcomes, including impulsive purchasing. The predicted magnitudes are somewhat suspect due to the nature of the experiments. All experiments were performed with students in a classroom setting and in no case were they actually buying anything -- they were simply reporting on their urge to buy. While students might represent the typical demographic of a web purchaser, the setting is anything but typical.
According to the first experiment, which used structural equation modeling, increasing visual appeal by one standard deviation increases urge to buy by .42 standard deviations and increasing information fit to task by one standard deviation increases urge to buy by .29 standard deviations. According to the second study, which used MANOVA to compare sites that had poor task-relevant and mood-relevant clues to sites that had good clues of task-relevance, mood-relevance, or both, impulsiveness increased from 61.1% of participants, to 72.2% with mood-relevant only, to 74.1% with task-relevant only, to 98% with both. Likewise, the magnitude of intended impulse buying increased from $33.89 to $49.17 to $55.56 to $66.39. Of course, this result is heavily dependent on what's being sold and what the potential impulse buys might be. In the experiment, the intended purchase was a $15 cell phone holster and the possible impulse buys were a $60 bag and several $15 accessories.
It's nice to know that a website's task-relevant cues (ie., appropriate information and good navigation) and mood-relevant cues (ie., visually appeal) increase consumers' urge to buy impulsively. Not that any designer would ever want to create a website deficient in either of these attributes.
While I have some doubts about the relevance of this study, I have little reason to doubt that improving website quality improves outcomes, including impulsive purchasing. The predicted magnitudes are somewhat suspect due to the nature of the experiments. All experiments were performed with students in a classroom setting and in no case were they actually buying anything -- they were simply reporting on their urge to buy. While students might represent the typical demographic of a web purchaser, the setting is anything but typical.
According to the first experiment, which used structural equation modeling, increasing visual appeal by one standard deviation increases urge to buy by .42 standard deviations and increasing information fit to task by one standard deviation increases urge to buy by .29 standard deviations. According to the second study, which used MANOVA to compare sites that had poor task-relevant and mood-relevant clues to sites that had good clues of task-relevance, mood-relevance, or both, impulsiveness increased from 61.1% of participants, to 72.2% with mood-relevant only, to 74.1% with task-relevant only, to 98% with both. Likewise, the magnitude of intended impulse buying increased from $33.89 to $49.17 to $55.56 to $66.39. Of course, this result is heavily dependent on what's being sold and what the potential impulse buys might be. In the experiment, the intended purchase was a $15 cell phone holster and the possible impulse buys were a $60 bag and several $15 accessories.
Sunday, March 15, 2009
Yet Another Adoption Model
Reference: Dong, L., Neufeld, D.J., and Higgins, C. (2008). Testing Klein and Sorra's innovation implementation model: An empirical examination, Journal of Engineering and Technology Management 25(4), 237-255.
Despite its title, this article is about the adoption of new information systems, not innovation (except to the extent that new systems can be called innovation). I had previously been unaware of Klein and Sorra’s model (Klein, K.J., Sorra, J.S., 1996, Academy of Management Review), which on its face seems similar to, but not as robust as the UTAUT (Venkatesh et al, 2003, MISQ). The main model finds “implementation effectiveness” to be dependent on “user affective commitment” and “implementation climate.” Implementation climate is, in turn, composed of skills, incentives, and the absence of obstacles. User affective commitment is dependent only on “innovation-values fit.”
To motivate their study, the authors cite the oft-reported research that documents how few companies complete their implementations on time, within budget, and with the promised features and functions. Unfortunately, their model does not address many of the common causes of these failures, such as poor estimates of development costs and time, lack of communication between developers and users, inexperience with the technologies employed, etc. Furthermore, their dependent variable, termed implementation effectiveness, is really just a measure of intention to adopt, as it includes five items that address only the following only the following components: 1) Avoidance, “If I can avoid using the system, I do”; and 2) Endorsement, “I think the system is a waste of time and money for our organization (reverse coded)”.
Contributions of the study include scales to measure implementation climate (5 components, 17 items), innovation values fit (3 components, 13 items), skills (6 items), incentives (2 items), absence of obstacles (3 items), and commitment (4 items). Some of these scales are adaptations from other sources. It is worth noting that the variable “innovation values fit” is similar to the construct of “perceived usefulness” in the TAM and TAM2 models, and to elements of “performance expectancy” and “effort expectancy” in the UTAUT model. It’s components are Fit re Quality, such as “The system maintains data I need to carry out my task,” Fit re Locatibility, such as “The system helps me locate corporate or department data very easily,” and Fit re Flexibility and Cooperation, such as “The system supports the repetitive and predictable work processes.”
The study concludes that “when implementation climate is strong and innovation-values fit is present, an implementation was more likely to succeed than when either climate or fit were weak”.
Despite its title, this article is about the adoption of new information systems, not innovation (except to the extent that new systems can be called innovation). I had previously been unaware of Klein and Sorra’s model (Klein, K.J., Sorra, J.S., 1996, Academy of Management Review), which on its face seems similar to, but not as robust as the UTAUT (Venkatesh et al, 2003, MISQ). The main model finds “implementation effectiveness” to be dependent on “user affective commitment” and “implementation climate.” Implementation climate is, in turn, composed of skills, incentives, and the absence of obstacles. User affective commitment is dependent only on “innovation-values fit.”
To motivate their study, the authors cite the oft-reported research that documents how few companies complete their implementations on time, within budget, and with the promised features and functions. Unfortunately, their model does not address many of the common causes of these failures, such as poor estimates of development costs and time, lack of communication between developers and users, inexperience with the technologies employed, etc. Furthermore, their dependent variable, termed implementation effectiveness, is really just a measure of intention to adopt, as it includes five items that address only the following only the following components: 1) Avoidance, “If I can avoid using the system, I do”; and 2) Endorsement, “I think the system is a waste of time and money for our organization (reverse coded)”.
Contributions of the study include scales to measure implementation climate (5 components, 17 items), innovation values fit (3 components, 13 items), skills (6 items), incentives (2 items), absence of obstacles (3 items), and commitment (4 items). Some of these scales are adaptations from other sources. It is worth noting that the variable “innovation values fit” is similar to the construct of “perceived usefulness” in the TAM and TAM2 models, and to elements of “performance expectancy” and “effort expectancy” in the UTAUT model. It’s components are Fit re Quality, such as “The system maintains data I need to carry out my task,” Fit re Locatibility, such as “The system helps me locate corporate or department data very easily,” and Fit re Flexibility and Cooperation, such as “The system supports the repetitive and predictable work processes.”
The study concludes that “when implementation climate is strong and innovation-values fit is present, an implementation was more likely to succeed than when either climate or fit were weak”.
Friday, March 6, 2009
Adaptation to IT-Induced Change
Reference: Bruque, S., Moyano, J., Eisenberg, J. (2008) Individual Adaptation to IT-Induced Change: The Role of Social Networks. Journal of Management Information Systems 25(3), 177-206.
I was interested in this paper because my current research addresses the role of Web-based social networking on innovation and other organizational outcomes. Existing research on Web-based social networking is quite sparse, probably because the technology is so new. So, when I saw the title of this article, I hoped that it would be relevant to my work. It turns out that this research concerns traditional social networks, not Web-based ones, so its relevance to my own research is not direct. Nevertheless, it seems reasonable to extend the authors' conclusions to individuals' extended (Web-based) networks. Thus, it provides some interesting hypotheses for future research.
Even had there been no relevance to my research, I found this article interesting and refreshing. The highlight for me is to see "adaption to IT-induced change" as the dependent variable rather than the common "adoption of technology." There is a significant difference between adoption and adaption, which the authors describe in some depth. Adoption is a binary variable -- either you adopt or do not. Although one can measure the extent of adoption by counting the number of people in an organization who adopt or fail to adopt, adoption remains binary at the individual level. In practice, many changes force employees to adopt to whatever technology is installed, so the real question is how they adapt to these changes. The authors argue and provide references to support the claim that IT-induced changes are harder to adapt to than most other types of change.
The conclusions of the study are not surprising. Adaptation improves the larger the size of the support network and the greater the strength and density of the informational network. The authors defined these networks to include people outside as well as inside the company. This is a significant departure from most studies and makes me optimistic that the results will extend to Web-based social networks. One disconcerting methodological issue is that subjects were allowed to list only five members of their support network and five members of their informational network. I don't believe that there was any measure of the extent to which these networks overlapped. Of course, Web-based social networks are much larger, although they are probably less "strong" or intense.
A significant contribution of the study is the creation of an instrument to measure individual adaptation to IT-induced change.
I was interested in this paper because my current research addresses the role of Web-based social networking on innovation and other organizational outcomes. Existing research on Web-based social networking is quite sparse, probably because the technology is so new. So, when I saw the title of this article, I hoped that it would be relevant to my work. It turns out that this research concerns traditional social networks, not Web-based ones, so its relevance to my own research is not direct. Nevertheless, it seems reasonable to extend the authors' conclusions to individuals' extended (Web-based) networks. Thus, it provides some interesting hypotheses for future research.
Even had there been no relevance to my research, I found this article interesting and refreshing. The highlight for me is to see "adaption to IT-induced change" as the dependent variable rather than the common "adoption of technology." There is a significant difference between adoption and adaption, which the authors describe in some depth. Adoption is a binary variable -- either you adopt or do not. Although one can measure the extent of adoption by counting the number of people in an organization who adopt or fail to adopt, adoption remains binary at the individual level. In practice, many changes force employees to adopt to whatever technology is installed, so the real question is how they adapt to these changes. The authors argue and provide references to support the claim that IT-induced changes are harder to adapt to than most other types of change.
The conclusions of the study are not surprising. Adaptation improves the larger the size of the support network and the greater the strength and density of the informational network. The authors defined these networks to include people outside as well as inside the company. This is a significant departure from most studies and makes me optimistic that the results will extend to Web-based social networks. One disconcerting methodological issue is that subjects were allowed to list only five members of their support network and five members of their informational network. I don't believe that there was any measure of the extent to which these networks overlapped. Of course, Web-based social networks are much larger, although they are probably less "strong" or intense.
A significant contribution of the study is the creation of an instrument to measure individual adaptation to IT-induced change.
Sunday, February 22, 2009
Predicting 50 Years of Compiler Research -- They Can't Be Serious
Reference: Hall, M., Padua, D., and Pingali, K. (2009). Compiler Research: The Next 50 Years, Communications of the ACM 52:2, 60-67.
I was amused to read the title of the CACM article referenced above. One can't quibble with the tag line -- "research and education in compiler technology is [sic] more important than ever." The article starts out well enough, recounting the past 50 years of compiler advances and noting that in the coming decade, research into compiling for multi-core processing and security and reliability will be major challenges. And it's hard to critique the authors' agenda for the compiler community except that it's rather vanilla and based on current conditions and those easily foreseeable for the near future, such as the need to address parallel architectures. But, it's completely unreasonable to expect that anyone can predict now what our needs will be in 50 years. For example, it seems likely that we will need compilers for quantum computing, yet this possibility is not raised. It's also quite likely, especially if you believe Ray Kurzweil, that by then computers rather than people will be building software, implying an entirely different model for the role of people, if at all, in compiler creation.
I was amused to read the title of the CACM article referenced above. One can't quibble with the tag line -- "research and education in compiler technology is [sic] more important than ever." The article starts out well enough, recounting the past 50 years of compiler advances and noting that in the coming decade, research into compiling for multi-core processing and security and reliability will be major challenges. And it's hard to critique the authors' agenda for the compiler community except that it's rather vanilla and based on current conditions and those easily foreseeable for the near future, such as the need to address parallel architectures. But, it's completely unreasonable to expect that anyone can predict now what our needs will be in 50 years. For example, it seems likely that we will need compilers for quantum computing, yet this possibility is not raised. It's also quite likely, especially if you believe Ray Kurzweil, that by then computers rather than people will be building software, implying an entirely different model for the role of people, if at all, in compiler creation.
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