Contested Automation: Double Indeterminacy,Union Power,and Job Quality in Call Centers

Linking Double Indeterminacy in the Labor Process to Worker Contestation Over Job Quality

This article draws on labor process theory, complemented by power resources theory, to provide insights on how unions mediate the impacts of automating technologies on job quality. We begin by unpacking how automating technologies impact job quality across different aspects of the labor process. Inspired by Marx (1867/1976), a central premise of Braverman's (1974) work was that capitalism affords management the capacity to purchase workers’ labor power and not labor itself. This labor power is a commodity unlike any other as its productive value depends on management's ability to extract value by exerting control over the labor process. The legacy of Braverman's work is that it placed the labor process front and center as an “object of class politics” (Thompson & Laaser, 2021, p. 143), wrapped in his original theorization of how managerial control and technology degrade work over time. Although his work left gaps in understanding the dynamics of labor control and resistance, it prompted deeper analysis of these aspects of the labor process in later waves of theory and debate (Thompson & Newsome, 2004).

We build on these later waves of labor process theory which interpret technological change through the lens of a “duality” between control and resistance at the point of production (Noble, 1978; Wilkinson, 1983). Labor process scholars suggest that technologies are essential components of modern “workplace regimes”, as they embed stringent mechanisms of worker control which are then contested by labor actors at the point of production (Burawoy, 1979; Dupuis, 2025; Steinhoff, 2024; Wood, 2025). In the context of digitalization, new configurations of “technical control” (Edwards, 1979)–enabled by algorithms and AI technologies–are reshaping structured antagonisms in the workplace (Edwards, 1986; Edwards & Hodder, 2022; Kellogg et al., 2020). In response, workers and unions can and do resist management's use of these technologies to intensify or control work effort (Cameron & Rahman, 2022; Kellogg et al., 2020; Krzywdzinski et al., 2024). In addition, these actors also resist the displacing effects of automating algorithms and AI on work (Cornfield, 1987; De Stefano, 2019; Doellgast et al., 2026b). We contend that both types of resistance must be examined together, and that doing so facilitates a better understanding of union struggles for better working conditions under digitalization.

Thus, our analysis focuses on how structured antagonisms in automation play out across the two indeterminacies (Smith, 2006) in the labor process. The term indeterminacy reflects that capitalists–as purchasers of labor power–continuously engage in efforts to minimize uncertainties in how much work can be extracted from workers. The first indeterminacy relates to work effort. It reflects the reality that workers do not necessarily exert their full capacity to work. Rather, capitalists employ mechanisms of control to ensure that workers scale up their efforts in production. Work-effort bargaining thereby involves labor-management negotiations over the amount of work effort that will be realized, as well as any necessary exchanges to achieve this realization (e.g., higher wages for greater effort).

The second indeterminacy pertains to work mobility. According to Smith (2006), workers have mobility power to move between jobs and use it to threaten employers with exit from their position and to bargain for improvements in working conditions. In this sense, exit is not merely opportunistic behavior but a form of resistance and expression of conflict between workers and management (Alberti & Sacchetto, 2024). The notion of mobility power hinges on a similar logic to that of research on “structural power” (Greer, 2024; Wright, 2000), which is that some workers can leverage power from market demand for their skills and experience while others cannot.

While covering neglected aspects of the labor process, Smith's conceptualization of mobility power merits an extension. First, it provides a very narrow and one-sided understanding of this form of power. In contrast to the power of workers’ exit threats, employers possess a symmetrical ability to push workers out of the organization through terminations, layoffs, and by making the work climate undesirable (Gallie et al., 2017). As a result, workers’ mobility power is not merely a function of their ability to exit the organization, but also employers’ ability to discharge them from it.

Second, his conceptualization facilitates a narrow understanding of worker agency in shaping outcomes. Prior studies treat the contestation of mobility power between workers and employers as individual and informal (Alberti & Sacchetto, 2024; Gerber & Krzywdzinkski, 2019; Heiland, 2022); broadening our conceptualization of worker mobility allows us to capture how unions influence this indeterminacy. Workers can collectively resist harmful staffing practices within their organizations (Hodson, 1995). For example, a study by Sexton (1982) captured the role of “feather-bedding” provisions within collective agreements in restricting hospitals from understaffing nurses. In settings where automating technologies simplify, routinize, or eliminate work tasks and systems, hence rendering workers more expendable (Herr, 2024), the issue is not merely how technologies impact workers’ threat of exit. On the contrary, it also concerns creating rules to reduce uncertainty over whether workers remain employed in the organization. Theorizing mobility power in this way conceives it as a contested indeterminacy subject to the influence of workers’ individual and collective actions.

The above theorization has significant implications for understanding how automation impacts job quality. First, tensions surrounding the impacts of automating algorithms and AI on work effort are effectively tensions over the intensification of work (i.e., pace, volume, and complexity of tasks) (Green, 2006). Automating technologies may eliminate some jobs. However, they often leave higher volumes of intensive work tasks for those whose jobs have not been eliminated (Antón et al., 2023; Berg et al., 2023). This is especially problematic in many modern workplaces where stringent algorithmic control over work processes is common and workers must cope with performance metrics that are both draconian and unrealistic (O’Brady & Doellgast, 2021; Woodcock, 2016). Work intensification is often the subject of work effort bargaining, as workers often resist machine-driven increases in workflow and pressures from task complexification (Krzywdzinski et al., 2024; Lei & Kim, 2024; Wood, 2021). Second, tensions over the impacts of automating technologies on work mobility are effectively tensions over job security. The displacing effects of automating algorithms and AI are well documented (Acemoglu & Restrepo, 2019; Baldwin, 2019; Ford, 2015; Rifkin, 1995; Wajcman, 2017). These threats are particularly acute for vulnerable workers, such as those in outsourced or precarious positions (Vertesi et al., 2020). Although automation-driven job insecurity significantly erodes job quality (Liu, 2023; Yam et al., 2023), it is not uncontestable. Recent research suggests that unions can mitigate the negative effects of job insecurity by strengthening employment protections (Haapanala et al., 2023; Nissim & Simon, 2021).

These tensions have broader implications for job quality, since work intensification and job insecurity are often interrelated with other job quality problems like poor job satisfaction or stress and emotional exhaustion (e.g., Dengler & Gundert, 2021; Gallie et al., 2017; Glavin et al., 2011; Heaney et al., 1994; Liu, 2023; McClure, 2018; Nazareno & Schiff, 2021). Thus, the above insights tie labor process debates on technology to current discussions on the quality of work, by showing how labor politics across two indeterminacies in production matter to different job quality outcomes.

Power Resources and the Temporal Dimension

The previous section established that automating technologies can disrupt job quality across two indeterminacies in the labor process. The current section highlights the role of union power in contesting bad outcomes within either indeterminacy. Power resources are those that “social actors” can mobilize to “make, receive, or resist change in accordance with their own interests” (Arnholtz & Refslund, 2024). A key contention of this theoretical approach is that unions have multiple power resources at their disposal and that their influence over working life is scaled up when they can strategically mobilize them in combination with one another (Doellgast et al., 2018; Meardi, 2024; Schmalz et al., 2018). Rarely are single power resources (e.g., associational, institutional) alone sufficient for promoting better social outcomes (Doellgast et al., 2018), including in instances of technological change (Doellgast et al., 2026a). Here we highlight the importance of how power resources are mobilized to shape outcomes across different indeterminacies in the labor process, which create challenges for the effective regulation of digital technology use and job quality.

Research corroborates the relevance of union power to outcomes from technological change across each indeterminacy. First, emerging research suggests that unions or works councils can mobilize power resources to protect workers from work intensification driven by technological change (Doellgast et al., 2023; Doellgast et al., 2026a; Lloyd & Payne, 2023; Pulignano et al., 2025; Rego, 2022). Institutional power resources often play a particularly important role. For example, Doellgast et al. (2023) show that when workers possess strong codetermination rights, works councils can proactively intervene in AI-driven technological change and constrain the use of digital tools for intensified worker monitoring. They often leverage these institutions alongside discursive power resources in which the benefits of ethical AI use for worker trust, company reputations, and organizational performance are strategically communicated to employers (Doellgast et al., 2026a). Dupuis' (2025) study of aluminum factories in Canada demonstrates that collective bargaining provisions negotiated in earlier rounds can be mobilized to preserve frontline workers’ autonomy in response to algorithmic decision-making systems. Where institutional power is weaker, such as in contexts with weak collective bargaining language, research points to the importance of associational and ideational resources. In such a context, Riordan et al.'s (2025) study of the hotel industry highlights how union leaders draw on associational and ideational power resources to mobilize member support and informally negotiate work rules with managers to address shortfalls in previously codified rights and agreements.

Second, researchers also suggest that unions can help protect workers from technology-driven job loss, although doing so involves negotiating over distinct sets of challenges in the workplace (Doellgast et al., 2026a; Pulignano et al., 2025). In their analysis of Hollywood Writers’ strikes, Grohmann et al. (2025) showed how discursive power involving the use of slogans, media outreach, and appealing to fans helped writers combat the threats of generative AI on jobs in the industry, ultimately contributing to the establishment of new bargaining provisions (institutional power) that restrict AI use. Thomas and Turnbull's (2025) research on the attempted creation of a digitalized remote tower center by Highlands and Islands Airports Limited (HIAL) in Scottland demonstrated how unions can mobilize various power resources (structural, associational, institutional, coalitional and ideational) to challenge the adoption of remote digital job killing infrastructure. Garneau et al. (2023) show that unions often find opportunities to “frame”, and hence mobilize discursive power, in hopes of building power in resisting automation. However, they recognize that unions in decentralized bargaining contexts face constraints compared to those with access to stronger centralized bargaining institutions. Thus, a dominant finding from literature on the impacts of union power within either indeterminacy stresses the importance of mobilizing combinations of power resources and tailoring mobilization efforts to the unique issues and contexts of the site of production.

This study departs from previous research in two ways. First, we provide insights on how the details of the labor process impact worker struggles for better work under digitalization. Subtle differences in the labor process often have important implications for how workers resist the impacts of technological change. Emerging scholarship has begun to capture how labor control regimes, which are shaped by power relations and industry contexts, matter to job quality (Cornfield, 1987; Heiland, 2022; Pulignano et al., 2025). Pulignano et al. (2025) show that the distinctiveness of digital “production regimes”, alongside union capacities to leverage power resources to confront issues within those regimes, can lead to variegated outcomes for worker control and the quality of work. Certain production regimes and combinations of power resources can impact whether unions pursue narrow agendas to enhance job security versus broader goals of worker wellbeing.

We build on these insights but suggest that greater analytical attention be paid to the different indeterminacies within a given production regime. As shown earlier, digital technologies reshape control in distinctive ways across these indeterminacies, generating different risks to job quality and provoking different forms of union intervention. A closer examination of how power resources are mobilized across both indeterminacies is needed to understand the full breadth of union impacts on technology use and their consequences for workers.

Second, we build on insights from those who call for a “temporal perspective” in analyzing struggles over technology. Power resources can become crystallized into institutional forms—such as laws, technologies, or collective bargaining arrangements—through past mobilization (Doellgast, 2018; Korpi, 1983; Schmalz et al., 2018). A small number of studies suggest that the temporal dimension is critical to understanding how unions respond to digitalization. They emphasize the historical nature of these struggles (Grohmann et al., 2025), and how the strategic mobilization of power resources at one point in time can impact efforts to shape technology adoption in another (Thomas & Turnbull, 2025). We build on these insights by highlighting the importance of historical struggles across different facets of the labor process, showing how struggles that build worker power and control over more facets at one point in time can empower workers in another.

Theoretical Framework

Drawing from the theorization above, we contend that: 1) unions must engage in both work effort and mobility bargaining to effectively resist the harms of automating technologies on job quality; and 2) this resistance entails the strategic mobilization of power resources across different indeterminacies and time periods.

This leads us to our framework in Figure 1. The starting point of our framework is automating technologies. A technology automates by using “machines and computers to substitute for human labor in a widening range of tasks and industrial processes” (Acemoglu & Restrepo, 2018). Many technologies have automating properties, ranging from algorithms which contain “step-by-step, distributed and nominally objective procedures” (Fourcade & Healy, 2017) to AI used to simulate human intelligence. We then suggest that automating technologies can impact both work effort and work mobility, which in turn have consequences for work intensification and job insecurity respectively. When technologies automate certain tasks, this can increase work effort and intensification for those whose jobs have not been eliminated, as they must complete complex tasks that could not be automated (Nazareno & Schiff, 2021). Furthermore, newly implemented technologies are often imperfect and create errors that must be fixed by workers.

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Figure 1.

Framework.

Automating technologies also alter work mobility and hence increase job insecurity through the elimination of work. Whereas traditional research on worker mobility focused on worker capacities to exit the organization (Li, 2022; Smith, 2006), we focus on their ability to remain within the organization. That is, automating technologies can lead to job loss when they serve as effective substitutes for the work done by existing workers. However, not all managers have full discretion to discharge workers that have been displaced by technology, due to restrictions imposed by collective bargaining and legislation.

We propose that each indeterminacy is subject to negotiation and contestation by unions. Union actions serve to curtail the negative effects of automation on work effort when they constrain management's ability to make work faster and more difficult. In this study, we focus on collective agreement provisions that constrain management's use of discipline to enforce performance targets and expectations. Union actions serve to curtail the negative effects of automation on work mobility when they constrain management's ability to discharge workers. Thus, we focus on collective agreements that do this. For our purposes, union actions to negotiate and leverage collective agreement provisions that provide workers with greater control over work effort represent work effort bargaining. Union actions to negotiate and leverage collective agreement provisions that provide workers with greater control over employment within their organization represent work mobility bargaining.

Union actions in both work effort and mobility bargaining draw on power resources. Here we suggest that there is a temporal dimension to these struggles. We examine institutional power resources in the form of codified collective agreements that reflect compromises from the past which set rules that still have bearing in the present (O’Brady, 2024; Schmalz et al., 2018). We also examine associational power, which reflects the “organisational properties that enable and express unity of action among workers to make, receive or resist change in accordance with their interests” (Ibsen, 2024). These properties include internal and mobilizing infrastructures, as well as information sharing practices. Associational power both reinforces and is reinforced by institutional power (Doellgast et al., 2018), as it serves to build institutional power resources and ensure they are effectively mobilized when needed.

Thus, we argue that work effort and mobility bargaining reflect the intertemporal use of complementary power resources. When unions use associational power resources to establish rules governing the labor process, these rules can later function as institutional power resources when new technologies disrupt work. When unions use associational power to enforce rules, they strengthen them by showing that violations carry sanctions. This dynamic points to the importance of union “articulation” over time (Levesque and Murray, 2010). Institutional power is not static; it is shaped by strategic decisions over time and ongoing efforts in building and mobilizing associational power resources. Thus, it is the accumulated strategic mobilization of power resources that helps to build institutions that govern the labor process. These decisions respond to opportunities to both build and defend institutional power and are often impacted by shifting configurations in other types of power.

The Contested Impacts of Automation on Work Effort and Intensification

This section examines the contested ways that automating self-service technologies impacted work effort and hence intensification. Interviewees described how the implementation of self-service forms on websites, IVR systems, and chatbots intensified work tasks. This intensification was driven by errors made by AI and the use of protocols to encourage customer use of self-service (script use and deliberately prolonged wait times), both of which created barriers to efficient call resolution and heightened fears that workers may be disciplined for not meeting performance goals. Responding to these problems, worker representatives mobilized collective agreements as institutional power resources, enforced with associational power resources, to protect workers from being disciplined for not meeting performance goals.

Interviewees commonly reported increased work effort and intensity from automation-related service disruptions. They provide convenience for customers by providing them with potentially efficient service interactions but led to a rise in complaints about technology-related errors. One set of complaints were that AI chatbots and IVR systems made errors in interpreting customer queries. One worker criticized their employers’ chatbots: “the chatbot is very limited in the questions it can answer. Often after two or three trials it will allow the customer to connect to agent” (Worker, C Telecom 1, March 2021). These interpretation problems were a major source of customer frustration. One manager described how these delays and frustrations escalated worker-customer conflict before the call even began, leaving the customer “ticked off” and creating further work for agents to de-escalate (Manager, US Telecom 1, December 2021).

Call routing technologies also provoked more negative customer relations when chat-bots or the IVR connect customers to the wrong departments. Often, “folks are getting sales calls that shouldn't be getting them, or the sales folks that are getting calls that are technical assistance, that should be getting sales calls” (Worker, US Telecom 2, April 2021). Thus, workers are made to take on a more challenging role in correcting these errors and managing resulting customer frustrations. Under circumstances where service quality is frequently linked to workers’ compensation and disciplinary measures, it is ultimately the workers who bear the consequences of these disruptions. One worker described how customers would complain when the automated system did not understand their reason for calling. Often, these systems would begin “resetting all their equipment”, causing service disruptions for customers who had no issues with the equipment in the first place (Worker and Union Representative, US Telecom 5, May 2021). Similarly:

A lot of our members that have been trained with the job know the support group that they need to go to, but they're stopped and blocked by a chat bot before they can even get there. And then depending on the words that they use, the chat bot determines if they should continue to a live representative or not. So that's going to cause delays on the call, delays on the work. It's going to slow productivity down in a lot of scenarios because in some of these work groups, if you're needing to chat with back office for fiber, if you're needing to chat with repair for copper, or if you're needing to chat on the mobility side, if you don't say the word that the chat bot [has] built an algorithm for, you're going to be sent to the wrong department, you're going to have to start over again and your customer is now waiting. (Worker, US Telecom 1, March 2023)

While errors from automating technologies extended wait times, workers also accused their employers of deliberately extending customer wait times to encourage self-service. One worker representative compared it to having “10 lanes of self-help” and “one person checking groceries out” with a live cashier in a grocery store (Union Representative, US Telecom 3, June 2022). Customers often received automated messages encouraging them to use self-service systems while on hold for agents. Additionally, employers have been reducing the contact points for customers to reach agents. One worker representative complained that customers “are going to be forced to stop writing to me because they are abolishing our email addresses” (Union Representative, C Telecom 1, March 2021). Others indicated how contact information for customer service is decreasingly available on company websites. Customers were often searching for agents, but “you just keep getting the [chatbot] and you're losing your mind” (Union Representative, US Telecom 5, July 2022). Thus, workers and worker representatives often reported complaints of customer frustrations over not reaching live agents.

Alongside customer driven frustrations over wait times, workers complained of new scripting protocols that require them to encourage customers’ use of self-service technologies. Workers that did not encourage self-service could be penalized. As one worker illustrated:

“When they listened to the calls they got, they got scored, that they explained to the customer that self-service was available. I remember fighting it because I thought that's ridiculous. But anyway. And if you didn't mention it, then you got you got, you know, docked on your matrix.” (Worker, C Telecom 1, March 2021)

A large number of qualitative comments in the survey corroborated the above findings. Of the 414 comments that were collected, there 91 negative comments on automating technologies (out of a total of 130 comments on automating technologies in general). 62 (68%) of them referred to increased work intensity caused by errors in self-service technologies. One worker stated: “call trees and chat bots have made my job harder & more stressful—often misroutes piss customers off, so it is harder to talk to them and sell services. Internal chat bots waste my time and often don’t help, leaving me without assistance or info [sic].” Moreover, 14 (15%) comments emphasized that these errors were particularly demanding because rigid management practices restricted workers’ autonomy to address them and shifted the risks of errors onto employes. One worker noted that “either misrouted or routed calls make it harder to make numbers or goals.” Another mentioned the high chance of receiving low feedback from angry customers: “because you cannot give them what they want, and they are angry with [the company], but take it out on you, the agent.” Taken together, the central issue for workers was that these technologies disrupted service delivery, and that these disruptions affected how they performed on metrics and could even lead to discipline.

Union locals varied in the strength of their contract language and the strength of associational power resources available to enforce that language. First, some union locals have bargaining language which required that they be consulted on the introduction of new digital technologies. Half of the studied bargaining units have collective agreement language requiring that managers provide them with notice (ranging from 4 weeks to six months) that major technological changes are forthcoming. Three of the agreements go further by requiring employers to consult with unions on how these technologies will be implemented. In C Telecom 1:

“The company agrees to provide as long as possible notice of at least four weeks to the unions, before the introduction and implementation of this technological change. They are obliged to notify me a month in advance to tell me [the worker representative], it is coming, such automation. Or this tool will come and then, consequences, the impacts that it will have in the team.” (Union Representative, C Telecom 1, March 2021)

Many union representatives acknowledged that service quality and employee outcomes could be significantly improved if automating technologies were made more effective. Thus, some union representatives saw their role as advocating for the effective implementation of such technologies. They would routinely gather information on technology errors and communicate them to management. Speaking of a technology that automates problem resolution by following algorithmic decisions but reduces service quality by restricting worker discretion, one worker representative stated:

“Ok, we understand you have this tool to make things more efficient, but you need to have a way for employees and for the union to appeal when it's not right. So, if it's telling us to do something wrong, you need to have a mechanism in place to say, ‘a process that you're telling us to do is wrong’. So, we fought to get that done, but it took a lot of fights, I think, before they backed down for sure.” (Union Representative, C Telecom 1, March 2021)

In sum, the implementation of automating technologies intensified work by creating errors that required fixing and through new work protocols that encourage self-service. They intensified work effort because: 1) agents now had to de-escalate conflicts with customers frustrated by delays in service delivery; 2) they automated simple service interactions, leaving agents with higher shares of complex service interactions; and 3) agents had new obligations to follow scripts on reminding customers of self-service tools. As a result, workers were burdened by additional tasks associated with these new processes, as well as greater work demands involved in handling customers frustrated by service delivery disruptions. None of the unions had the power to stop the implementation of these technologies. However, they varied in their ability to leverage associational power resources to enforce institutional power resources (collective agreements) that protect workers from harmful discipline. This included provisions that protect workers from discipline based on monitored calls, lessening fears of punishment for mistakes made during periods of heightened customer demands. The mere presence of these provisions was insufficient; they needed to be enforced with associational power resources.

The Contested Impacts of Automation on Work Mobility and Job Insecurity

This section examines the contested ways that automating self-service technologies impacted worker mobility and hence job security. Interviewees described how automating technologies have threatened the number of jobs within call centers by facilitating customer self-service, routinizing work, eliminating tasks, and making it easier to outsource work. However, this was subject to contestation, particularly by leveraging collective bargaining language as institutional power resources, and leveraging associational power resources to enforce the language effectively. Worker representatives in locals who successfully negotiated strong outsourcing restrictions, layoff provisions, and bumping rights in the past could leverage this language to protect workers’ job security in the present.

Self-service options were the most cited causes of automation-driven job losses in the call centers. In the initial phases, company websites and cellphone applications were used to perform simple functions such as fulfilling basic orders. From the early 2010s till today, these applications have expanded to provide a wide range of services to fill orders as well as answer customer queries. According to one worker, if a customer wants to add a “a programming change on their TV, if they want to add a long-distance plan, disconnect, connect anything like that, basically, they can do an entirely self-serve, order. And they also have like, you can submit complaints through web forms” (Worker, C Telecom 1, March 2021). In describing the impacts of these technologies on job loss, another worker described how there is:

“No need to contact us directly, [customers] can go on a website, they can fill in a request for self-serve or depending on what they need. And this has really downsized a lot of our work… Most of it is webpage. So that's had a dramatic, you know, impact on our membership.” (Worker and Union Representative, C Telecom 1, February 2021)

Overall, the broad set of self-service technologies described above are reducing demand for call center workers. A key path for job loss was attrition by means of retirements and “voluntary severance packages”. This has been described as using “stealth” to shrink the unionized workforce without the need for obvious and dramatic mass layoffs (Worker and Union Representative, C Telecom 2, February 2021). In other instances, it has caused entire call centers to close, leaving workers without jobs. Workers in some of the studied call centers were offered to relocate to retain their jobs, but such a relocation comes with personal costs, especially those with families or other mobility restrictions. Automation's threats to job security occur alongside similar threats from outsourcing, as uses of outsourced vendors has increased over time in most call centers.

The negative impact of these technologies on job loss depended on union capacities to leverage institutional power to limit automation-related job losses. First, unions leveraged formal bargaining language to provide strong protective measures against potential layoffs due to automation. For example, C Telecom 3 had a strong agreement prohibiting layoffs resulting from technological change, stipulating that “no regular employees who attain two years of regular service will lose their employment as a result of technological change.” Although C Telecom 3 was the only workplace with explicit no-layoff language directly connected to technological change, most of the studied workplaces were protected by clauses shielding workers from layoffs caused by outsourcing.

Collective agreement provisions that prohibit employers from laying off workers while outsourcing also protected workers from automation-related layoffs. Every employer in the study engaged in some level of outsourcing. Thus, employers could not justify any layoffs so long as they continued to outsource work. This includes layoffs resulting from automation-related efficiencies. Any such layoff could be subjected to a grievance, as employers would need to stop outsourcing before they could lay off workers for other reasons. According to worker at US Telecom 2, “the lack of investment in infrastructure has, ironically, worked in favor of employees regarding automation, as the contract language protects them from contractors” (Worker and Union Representative, US Telecom 2, March 2021). Similarly, a union president at C Telecom 1 described how “you can't come in saying ‘I've automated, so I'll cut.’ You don't have the right…there have been no job cuts lately” (Union Representative, C Telecom 1, March 2021). However, at US Telecom 5, which lacked language protecting workers from layoffs, the situation was notably different:

“We've had a huge decline. We've really been impacted by layoffs. It's been really interesting because our members, for some reason, have deflected the blame to the union rather than the company. Because we haven't been able to, you know, stop automation or, you know, save their jobs. Again. So that's been really difficult to deal with. We're still trying to figure out how to handle that.” (Union Representative, US Telecom 5, May 2021)

The presence of the above contract clauses was insufficient for maximizing their usefulness as power resources. They were best leveraged where local union staff were proactive in identifying and policing violations in the collective agreement. For example, several US locals identified how employers were implementing new technologies without providing union leaders with “notice”. They acknowledged the importance of retaining strong lines of communication with workers to identify violations, while ensuring that stewards were well trained to handle grievances. Overall, the goal of many locals was to demonstrate their organizational strength to management, leaving them fearful of union reprisals should they violate negotiated agreements.

Our survey results support these findings by showing that job protection provisions reduce workers’ perceptions of job insecurity associated with self-service automation. In particular, we examined watermark provisions, which require employers to maintain a minimum ratio or number of calls within bargaining units. For example, in US Telecom 3-Unit A, one provision specifies that call centers in the bargaining unit must “handle at least 83% of all calls originating in the [bargaining unit]'s footprint.” Workers without watermark provisions consistently reported higher perceived layoff risks from new technologies. For chat-bots, 68.2% of workers without provisions considered layoffs somewhat or very likely, compared to 58.5% among those with provisions. A similar pattern appeared for automation overall: 65.4% of workers without provisions considered layoffs somewhat or very likely, compared to 53.8% of those with provisions. For self-service applications, concerns were high in both groups, but again the non-provision group reported a higher level of perceived risk, with 72.3% rating layoffs somewhat or very likely compared to 68.2% in the provision group. These results suggest that even though this provision is designed to prevent outsourcing from leading to job cuts, it also helps to preserve call volumes in contexts where self-service automation takes over calls.

In sum, the implementation of self-service technologies was associated with greater fears of job loss as they reduced demand for live agents. However, unions diverged in the strength of job protection provisions that restrict layoffs and provide bumping rights. These provisions were a source of institutional power and helped union officials constrain employee layoffs from automation. Requirements to provide notice before the adoption of new technologies also provided unions with a window to negotiate their effects on job security with management. However, as illustrated above, these agreements were best enforced by union locals that developed associational power resources (i.e., membership communications and grievance training) needed to effectively enforce contract language.