Changing Contours of Work. Stephen Sweet
often have developed very different types of interaction styles that lead to a poorer fit between their cultural tool kits and jobs. A young worker who grew up in an inner-city neighborhood may have a very difficult time “reading” the signals given off by the responses of a more privileged client and may also feel uncomfortable in the encounter. And the client may not necessarily treat the young worker in the same way as someone perceived as being more similar in habits and disposition. These types of interpersonal dynamics present new challenges in fitting workers into the new opportunity structures, as the work is not only about mastering technical skills, but also social skills.
In theory, at least, interactive skills would seem to be resistant to routinization and simplification. However, as in the old economy, employers have attempted just that, and with considerable degrees of success. Perhaps the most familiar example of routinized interaction can be seen in the fast-food industry. As Robin Leidner (1993) documented in her pioneering study of interactive service work, McDonald’s has worked out very specific guidelines not just for the production of the food their stores sell to customers, but also for structuring the interaction between workers and customers. Frontline service workers in the stores are given very specific scripts to follow and are instructed (and their performance is monitored) regarding what to say and how to say it. Nor is the management of interaction confined to employees, as McDonald’s has succeeded in finding ways to encourage customers to behave in predictable ways; for example, by physically arranging the store to encourage customers to behave in prescribed ways (line up here, pick up your food there, don’t linger too long, dispose of your trash on the way out), as well as limiting or scripting menu choices (“I will have value meal number seven”). As has frequently been noted, the experience of patronizing a McDonald’s restaurant is more or less the same, no matter where it is located, as a result of this careful routinization. Call center work is another type of interactive service work that has been successfully routinized and elaborately managed. Employees must follow carefully developed scripts, and calls are monitored for “quality assurance.” Employees of call centers located abroad undergo accent modification programs so that their English will not be too foreign to callers; they are also asked to become familiar with aspects of Western culture (such as sports) so that they can interact more effectively with callers (Belanger and Edwards 2013).
Like routinization in manufacturing, interaction constraints can be experienced as oppressive and intrusive by both workers and customers. Much of the early research on scripted interaction speculated about the possibility that following scripts, and interacting with others according to externally imposed routines, rather than one’s own reaction to the situation, can breed feelings of inauthenticity and psychological distress (Wharton 1999). But efforts to make interaction more routine and predictable can actually help employees do their jobs, with the result that they sometimes embrace the protocols and actively collaborate with employers in imposing routines. Hochschild’s (1983) classic study of flight attendants, for example, describes the various routines and scripts airline employees are trained to use during commercial flights. She points out that some of the reason for this training is to promote the corporate brand and to ensure that employees project the company’s desired image. However, it is also the case that some of the routines help flight attendants perform difficult tasks: creating a reassuring environment that calms potentially panicky passengers, coping with angry customers when in flight (when there are limited options for discipline), and so on. Far from resisting or resenting routines, flight attendants adopt them willingly and make use of them voluntarily. More recent research on nurses finds that burnout is common, in part, because of the emotional turmoil the work involves. In this context, scripts and routines that help nurses manage interactions with patients might help to reduce emotional fatigue (Erickson and Grove 2007).
For better or worse, like work in the old economy, interactive service work is subject to the pressure of routinization. Once work is routinized, there exists the potential to replace people with machines. Can this be done within the service sector? Clearly this is possible. Consider, for example, that inserting a credit card into the pump at a self-serve gas station is now the preferred means of payment, an act that eliminates the need to interact with the service station attendant. And it is possible to apply technologies to more sophisticated types of service encounters. However, there also appear to be limits. For example, one study of an attempt to implement an automated help desk found that computers have the capacity to respond to commonly experienced problems or needs of clients. However, what did prove to be an obstacle was that callers could not be controlled in the same ways that they are at a McDonald’s restaurant. For example, callers to help desks typically did not know how to describe their problem in ways that the help desk computer could understand. Lacking technical sophistication themselves, callers often provided vague, incomplete, or even inaccurate descriptions of the problem they were experiencing, with the result that the computer was unable to ask many follow-up questions and lacked the capacity to respond appropriately (Head 2003). Similarly, employers have found it difficult or impossible to reduce their reliance on the care work provided by nurses, whose interaction with patients, as well as the various physical tasks they perform, remain essential to effective health care (Clawson and Gerstel 2014). So, while employers continue to attempt to apply old-economy techniques to the organization of work tasks, some of the complex services needed in the new economy may prove unsuited to those approaches.
High-Tech Work
The new economy is also frequently described as requiring a more educated workforce. According to this view, the “knowledge economy” or the “information economy” depends on a large and growing supply of highly educated, technically sophisticated employees, rather than the low-skill workers needed by mass production industry. An important element of this high-skill workforce is the so-called STEM (science, technology, engineering, mathematics) workforce; policy makers argue that economic growth increasingly depends on an adequate supply of workers trained in these areas, individuals who can design and maintain new technologies.
STEM jobs have, in fact, grown significantly. These trends need to be interpreted carefully, however, because opportunities for work in some STEM occupations have not been expanding. While there are many more jobs for workers with computer science training, for example, the same cannot be said for physicists, chemists, or even certain kinds of engineers, where demand has grown more slowly, or not at all. Nor has the expansion of STEM employment been steady; on the contrary, since World War II, it has been marked by a boom-bust cycle, as periods of high demand are followed by an overproduction of graduates, layoffs, and falling demand (e.g., what happened after the Vietnam War or the more recent dot.com bust of the early twenty-first century). In some instances, exaggerated predictions of actual or looming shortages of qualified STEM workers can be linked to advocates with political agendas at stake (Salzman, Kuehn, and Lowell 2013; Teitelbaum 2014). So, while the growth of STEM employment is real and likely to continue, it will likely remain a relatively small, if increasingly important, part of the contemporary economy.
As the STEM workforce has grown, so have hopes that the new economy will create large numbers of well-paying, secure, challenging jobs. The success of enterprises such as Apple, Microsoft, and Facebook fuels enthusiastic visions of high-tech workers deeply engaged with creative work, making pioneering breakthroughs, and transforming knowledge into lucrative business opportunities. The demand for engineers, research scientists, and computer professionals, and the reality of success stories, indicate that these hopes are not unfounded. However, there is reason not to jump to the conclusion that the low-skill mass production economy has been fully replaced by an economy dominated by autonomous, creative STEM workers.
First, it should be remembered that not all jobs in high-technology, science-based sectors are well-paid, high-skill jobs. The computer industry employs many professionals but also needs relatively unskilled workers to assemble, package, and transport its products. Even sophisticated artificial intelligence systems such as search engines or the technologies that make businesses such as Uber and Lyft possible rely on badly paid workers (sometimes referred to as “ghost workers” since they are hidden from view) who do the things the technology can’t—such as screening out “adult” content from searches or checking that your Uber driver is actually the person Uber screened and has determined to be an acceptable driver (Gray and Suri 2019). Similarly, while health