While Mumby (Reference Mumby2019) chronicles effects of shifting to post-Fordist work, there are twists in neoliberal philosophy that open new avenues for inquiry and practice for industrial and organizational (I-O) psychologists. Neoliberal attitudes and behavior must have an enabling platform to be credible. Just as Fordist work was built on the industrial revolution and its ultimate realization in the production line, post-Fordist work rests atop the unrelenting pace of technological advances. Two forces in developed countries have undermined the value of human capital in routine work: automation and cheaper foreign labor. As more work becomes automated, the economics of production shift increasingly from human labor to capital investment in equipment. Thus, the human capital developed through a worker’s experience in and knowledge of the production process plummets. Where routine work has not been automated, it has shifted to cheaper labor in developing countries. However, the gloomy effects of post-Fordist work are not equal for all forms of labor.
The dynamics of neoliberalism in post-Fordist work play out in more complex ways for many areas of nonroutine work. While advancements in artificial intelligence and other knowledge-related technologies have automated some areas of knowledge-intense work, the displacement of human capital in cognitively complex work has not been achieved. Engineers, lawyers, doctors, programmers, and other knowledge workers have been aided, but not replaced, by knowledge technologies. To explore the post-Fordist, neoliberal ethos of knowledge workers, I will use examples from software organizations.
Employment risk in knowledge-intense work
Although not replaced by knowledge-based technologies, the displacement of knowledge workers in developed countries has grown with the global expansion of communication technologies. Employers have outsourced work to less expensive labor in developing countries with educated populations. This shift has been enabled by the cheap transportation of knowledge-based products like software through communication networks, making proximity virtual rather than physical. Although I-O psychologists have studied distributed teams and Computer Supported Cooperative Work (Priest, Stagl, Klien, & Salas, Reference Priest, Stagl, Klien, Salas, Bowers, Salas and Jentsch2006), most of the research on globally distributed teams and their supporting technologies has been conducted in the software engineering community (Sengupta, Chandra, & Sinha, Reference Sengupta, Chandra and Sinha2006).
As demand has grown in some areas of knowledge work such as software development, a market-based dynamic is reshaping the economics of outsourcing. Turnover rates have steadily grown among knowledge workers in countries with large outsourcing industries as salaries rise to attract talent from a limited supply of qualified candidates. Rising salaries in developing countries suggest movement toward a global value for knowledge workers in some skill areas. In this regard, it is the rising value of human capital that was previously undervalued in the global market that voids the social contract from labor’s perspective, motivating frequent movement between employers.
Rising salaries are not the only dynamic motivating mobility. In skill areas with rapid technological change such as software development, the value of human capital decays as a technology declines in use. Thus, knowledge workers seek opportunities that expose them to newer technologies and applications (Hall, Sharp, Beecham, Baddoo, & Robinson, Reference Hall, Sharp, Beecham, Baddoo and Robinson2008), corroborating neoliberalism’s focus on the entrepreneurial individual seeking to advance the value or their personal capital.
The high rate of annual turnover in outsourcers of knowledge-based work, frequently hovering from 20% up to 50% in IT (Vorhauser-Smith, Reference Vorhauser-Smith2012), inserts a new factor into the economics of outsourcing, the economic value of knowledge. The return on investment for outsourcing software work usually begins in the second year after a period of learning a business application. If the staff assigned to an outsourced project turns over every two to three years on average, the outsourced work is perpetually on a learning curve, dissipating the value of cheaper labor. In fact, Smite and Solingen (Reference Smite and Solingen2016) produced evidence that several factors, turnover being the largest among them, can produce a negative return for outsourcing software work.
The effect of turnover on the economics of outsourcing highlight the key source of economic value in knowledge work, the accumulation of knowledge. Many companies are starting to insource knowledge work that had been outsourced because of three factors:
1. The inability of outsourcers to retain staff beyond the learning curve
2. The impact of physical proximity on the rate and depth of knowledge development
3. The loss of business-specific knowledge transferred to the outsourcer
Thus, the employment risk created by outsourcing is mitigated by the greater value of in-house knowledge workers who, if they are retained, develop a large virtual repository of knowledge about the business and how to instantiate it in business systems. The extensive value of sustaining and growing this knowledge base motivates organizations to reinstate a lighter social contract with employees who are willing to continue learning about the business and develop innovative business systems. This dynamic restructures the ownership of capital in some areas of neoliberal, post-Fordist work.
Knowledge as capital
In knowledge-intense industries, the value of an enterprise is in the knowledge it can apply to its products and services. This knowledge is at best only partially owned by the owners of the enterprise. Rather it is primarily owned by labor, and its growth is their asset until it can be transferred into automated systems. While neoliberalism embraces the conceptual redistribution of capital into the capabilities of individuals, the nature of this capital has only been studied superficially. For instance, Curtis, Krasner, and Iscoe (Reference Curtis, Krasner and Iscoe1988) found that the thin spread of business domain knowledge across a development team was a critical weakness in many system development projects.
To fully characterize this conceptual redistribution of capital, we need better measurement of the value of knowledge and skill by a combination of I-O and cognitive psychologists. Much of the existing research has been performed in the business management community (Jana, Reference Jana2016) without the concepts and measurement innovations that psychologists can develop. Some questions psychologists should address include how professional knowledge should be measured in individuals, teams, and as a collective resource across an enterprise. How is subject matter expertise typically distributed across successful projects and organizations? What are the most important factors affecting the ability to translate expert knowledge into performance and results? What is the typical rate of learning during periods of rapid technological change? What are the characteristics of value transfer as human capital is transformed into enterprise capital through the creation of patents, trade secrets, automated business systems, reusable assets, and other products of intellectual labor? These questions are key to human capital management and the economics of knowledge-intense industries.
The network and its data
In Fordist times under the reign of Taylorism, communication among employees was limited. More recently, at least in knowledge-intense work, it is technology more than management philosophy that has changed communication in the workplace. The expansion of network-based communication potentially makes the entire organization a workgroup. Knowledge and work products are rapidly shared across internal networks, as well as globally through the internet. For instance, YouTube, Reddit, and Stack Overflow have become primary sources of continuing education, creating a global professional community of software developers in frequent communication.
The growth of a professional community mitigates employment risk in several ways. First, it provides an expansive network through which to explore future opportunities. Second, it strengthens a source of work identity tied more to a profession than to an employer. Third, crowdsourcing through the internet has become a legitimate alternative to regular employment. Research questions involve the following: How does interaction through the internet affect self-identification? Does expanded access to a professional community exacerbate neoliberalism’s identification of self with work, or does the internet mitigate this effect by providing expanded access to sources of identification beyond work and profession?
For I-O psychologists, the intranet can be a revealing source of performance data. For instance, who are the “go-to” employees others communicate with to solve problems, learn techniques, or acquire reusable work products? Managers have opinions, but the record of network traffic has evidence—evidence that could be a rich source of research data for I-O psychologists to study behavior and performance in modern organizations.
The software engineering community already has developed a rich literature studying behavior from online activity in networks and work artifacts stored in online repositories (Tan & Hindle, Reference Tan and Hindle2019). Microsoft has been a leader in this research, studying online behavior and work artifacts to aid and improve the performance, tools, and methods of software developers (Bosu, Greiler, & Bird, Reference Bosu, Greiler, Bird, Di Penta, Pinzger, Robbes, Kamei and Ying2015). The trove of behavioral data available online opens vast new opportunities for I-O psychologists, often in cooperation with other disciplines, to produce evidence-based recommendations for improving work organization and processes, automated tools and aids, and business decisions.
Conclusion
I share Mumby’s (Reference Mumby2019) concern that the post-Fordist, neoliberal world suffers great inequalities. Yet many of the factors creating them were already in operation by the time work was shifting to post-Fordism (Piketty, Reference Piketty2014). Further, the risks of post-Fordist employment are not evenly spread across the employment spectrum. Rather they appear greatest for those who perform routine work, especially in manufacturing. As the cognitive complexity of work increases, so do the factors that can mitigate employment risk. For nonroutine, cognitively complex work such as software development, technological advances, especially those in communication, have created an ecosystem that expands the context and sociality of work in ways that mitigate much of its employment risk. In addition, a significant capital asset of the firm is in the knowledge and skill of its labor. The more cognitively complex the work, the slower the transfer of capital from the knowledge of individuals into assets of the enterprise. As Narayana Murthy (Reference Murthy2013), former CEO of Infosys, said, “Our assets walk out of the door each evening.”
Schumpeter’s (Reference Schumpeter2008) “creative destruction” has continually remade work since the invention of stone tools, revising the economic value of various skills. Within this creative destruction are new technologies, work methods, and opportunities that reduce employment risk for at least some of the workforce. Thus, Mumby’s (Reference Mumby2019) gloomy assessment of post-Fordist, neoliberal capitalism is not universally experienced. And certainly not for I-O psychologists, who can find new opportunities for research and contribution in the post-Fordist workplace.
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While Mumby (Reference Mumby2019) chronicles effects of shifting to post-Fordist work, there are twists in neoliberal philosophy that open new avenues for inquiry and practice for industrial and organizational (I-O) psychologists. Neoliberal attitudes and behavior must have an enabling platform to be credible. Just as Fordist work was built on the industrial revolution and its ultimate realization in the production line, post-Fordist work rests atop the unrelenting pace of technological advances. Two forces in developed countries have undermined the value of human capital in routine work: automation and cheaper foreign labor. As more work becomes automated, the economics of production shift increasingly from human labor to capital investment in equipment. Thus, the human capital developed through a worker’s experience in and knowledge of the production process plummets. Where routine work has not been automated, it has shifted to cheaper labor in developing countries. However, the gloomy effects of post-Fordist work are not equal for all forms of labor.
The dynamics of neoliberalism in post-Fordist work play out in more complex ways for many areas of nonroutine work. While advancements in artificial intelligence and other knowledge-related technologies have automated some areas of knowledge-intense work, the displacement of human capital in cognitively complex work has not been achieved. Engineers, lawyers, doctors, programmers, and other knowledge workers have been aided, but not replaced, by knowledge technologies. To explore the post-Fordist, neoliberal ethos of knowledge workers, I will use examples from software organizations.
Employment risk in knowledge-intense work
Although not replaced by knowledge-based technologies, the displacement of knowledge workers in developed countries has grown with the global expansion of communication technologies. Employers have outsourced work to less expensive labor in developing countries with educated populations. This shift has been enabled by the cheap transportation of knowledge-based products like software through communication networks, making proximity virtual rather than physical. Although I-O psychologists have studied distributed teams and Computer Supported Cooperative Work (Priest, Stagl, Klien, & Salas, Reference Priest, Stagl, Klien, Salas, Bowers, Salas and Jentsch2006), most of the research on globally distributed teams and their supporting technologies has been conducted in the software engineering community (Sengupta, Chandra, & Sinha, Reference Sengupta, Chandra and Sinha2006).
As demand has grown in some areas of knowledge work such as software development, a market-based dynamic is reshaping the economics of outsourcing. Turnover rates have steadily grown among knowledge workers in countries with large outsourcing industries as salaries rise to attract talent from a limited supply of qualified candidates. Rising salaries in developing countries suggest movement toward a global value for knowledge workers in some skill areas. In this regard, it is the rising value of human capital that was previously undervalued in the global market that voids the social contract from labor’s perspective, motivating frequent movement between employers.
Rising salaries are not the only dynamic motivating mobility. In skill areas with rapid technological change such as software development, the value of human capital decays as a technology declines in use. Thus, knowledge workers seek opportunities that expose them to newer technologies and applications (Hall, Sharp, Beecham, Baddoo, & Robinson, Reference Hall, Sharp, Beecham, Baddoo and Robinson2008), corroborating neoliberalism’s focus on the entrepreneurial individual seeking to advance the value or their personal capital.
The high rate of annual turnover in outsourcers of knowledge-based work, frequently hovering from 20% up to 50% in IT (Vorhauser-Smith, Reference Vorhauser-Smith2012), inserts a new factor into the economics of outsourcing, the economic value of knowledge. The return on investment for outsourcing software work usually begins in the second year after a period of learning a business application. If the staff assigned to an outsourced project turns over every two to three years on average, the outsourced work is perpetually on a learning curve, dissipating the value of cheaper labor. In fact, Smite and Solingen (Reference Smite and Solingen2016) produced evidence that several factors, turnover being the largest among them, can produce a negative return for outsourcing software work.
The effect of turnover on the economics of outsourcing highlight the key source of economic value in knowledge work, the accumulation of knowledge. Many companies are starting to insource knowledge work that had been outsourced because of three factors:
1. The inability of outsourcers to retain staff beyond the learning curve
2. The impact of physical proximity on the rate and depth of knowledge development
3. The loss of business-specific knowledge transferred to the outsourcer
Thus, the employment risk created by outsourcing is mitigated by the greater value of in-house knowledge workers who, if they are retained, develop a large virtual repository of knowledge about the business and how to instantiate it in business systems. The extensive value of sustaining and growing this knowledge base motivates organizations to reinstate a lighter social contract with employees who are willing to continue learning about the business and develop innovative business systems. This dynamic restructures the ownership of capital in some areas of neoliberal, post-Fordist work.
Knowledge as capital
In knowledge-intense industries, the value of an enterprise is in the knowledge it can apply to its products and services. This knowledge is at best only partially owned by the owners of the enterprise. Rather it is primarily owned by labor, and its growth is their asset until it can be transferred into automated systems. While neoliberalism embraces the conceptual redistribution of capital into the capabilities of individuals, the nature of this capital has only been studied superficially. For instance, Curtis, Krasner, and Iscoe (Reference Curtis, Krasner and Iscoe1988) found that the thin spread of business domain knowledge across a development team was a critical weakness in many system development projects.
To fully characterize this conceptual redistribution of capital, we need better measurement of the value of knowledge and skill by a combination of I-O and cognitive psychologists. Much of the existing research has been performed in the business management community (Jana, Reference Jana2016) without the concepts and measurement innovations that psychologists can develop. Some questions psychologists should address include how professional knowledge should be measured in individuals, teams, and as a collective resource across an enterprise. How is subject matter expertise typically distributed across successful projects and organizations? What are the most important factors affecting the ability to translate expert knowledge into performance and results? What is the typical rate of learning during periods of rapid technological change? What are the characteristics of value transfer as human capital is transformed into enterprise capital through the creation of patents, trade secrets, automated business systems, reusable assets, and other products of intellectual labor? These questions are key to human capital management and the economics of knowledge-intense industries.
The network and its data
In Fordist times under the reign of Taylorism, communication among employees was limited. More recently, at least in knowledge-intense work, it is technology more than management philosophy that has changed communication in the workplace. The expansion of network-based communication potentially makes the entire organization a workgroup. Knowledge and work products are rapidly shared across internal networks, as well as globally through the internet. For instance, YouTube, Reddit, and Stack Overflow have become primary sources of continuing education, creating a global professional community of software developers in frequent communication.
The growth of a professional community mitigates employment risk in several ways. First, it provides an expansive network through which to explore future opportunities. Second, it strengthens a source of work identity tied more to a profession than to an employer. Third, crowdsourcing through the internet has become a legitimate alternative to regular employment. Research questions involve the following: How does interaction through the internet affect self-identification? Does expanded access to a professional community exacerbate neoliberalism’s identification of self with work, or does the internet mitigate this effect by providing expanded access to sources of identification beyond work and profession?
For I-O psychologists, the intranet can be a revealing source of performance data. For instance, who are the “go-to” employees others communicate with to solve problems, learn techniques, or acquire reusable work products? Managers have opinions, but the record of network traffic has evidence—evidence that could be a rich source of research data for I-O psychologists to study behavior and performance in modern organizations.
The software engineering community already has developed a rich literature studying behavior from online activity in networks and work artifacts stored in online repositories (Tan & Hindle, Reference Tan and Hindle2019). Microsoft has been a leader in this research, studying online behavior and work artifacts to aid and improve the performance, tools, and methods of software developers (Bosu, Greiler, & Bird, Reference Bosu, Greiler, Bird, Di Penta, Pinzger, Robbes, Kamei and Ying2015). The trove of behavioral data available online opens vast new opportunities for I-O psychologists, often in cooperation with other disciplines, to produce evidence-based recommendations for improving work organization and processes, automated tools and aids, and business decisions.
Conclusion
I share Mumby’s (Reference Mumby2019) concern that the post-Fordist, neoliberal world suffers great inequalities. Yet many of the factors creating them were already in operation by the time work was shifting to post-Fordism (Piketty, Reference Piketty2014). Further, the risks of post-Fordist employment are not evenly spread across the employment spectrum. Rather they appear greatest for those who perform routine work, especially in manufacturing. As the cognitive complexity of work increases, so do the factors that can mitigate employment risk. For nonroutine, cognitively complex work such as software development, technological advances, especially those in communication, have created an ecosystem that expands the context and sociality of work in ways that mitigate much of its employment risk. In addition, a significant capital asset of the firm is in the knowledge and skill of its labor. The more cognitively complex the work, the slower the transfer of capital from the knowledge of individuals into assets of the enterprise. As Narayana Murthy (Reference Murthy2013), former CEO of Infosys, said, “Our assets walk out of the door each evening.”
Schumpeter’s (Reference Schumpeter2008) “creative destruction” has continually remade work since the invention of stone tools, revising the economic value of various skills. Within this creative destruction are new technologies, work methods, and opportunities that reduce employment risk for at least some of the workforce. Thus, Mumby’s (Reference Mumby2019) gloomy assessment of post-Fordist, neoliberal capitalism is not universally experienced. And certainly not for I-O psychologists, who can find new opportunities for research and contribution in the post-Fordist workplace.