Humans and Technology: Forms of Conjoined Agency in Organizations

Abstract

Organizations are increasingly deploying technologies that have the ability to parse through large amounts of data, acquire skills and knowledge, and operate autonomously. These technologies diverge from prior technologies in their capacity to exercise intentionality over protocol development and/or action selection in the practice of organizational routines, thereby affecting organizations in new and distinct ways. In this article, we categorize four forms of conjoined agency between humans and technologies: conjoined agency with assisting technologies, conjoined agency with arresting technologies, conjoined agency with augmenting technologies, and conjoined agency with automating technologies. We then theorize on the different ways in which these forms of conjoined agency impact a routine's change at a particular moment in time as well as a routine's responsiveness to feedback over time. In doing so, we elaborate on how organizations may evolve in varied and diverse ways based on the form(s) of conjoined agency they deploy in their organizational design choices.

Revising the ‘science of the organisation’: theorising AI agency and actorhood

“…Historically, the notion of agency has been viewed as a strictly human capacity (Sørensen & Ziemke, 2007). However, as AI becomes more advanced, discussions are emerging as to how to conceptualise AI ageny and its intentionality (Johnson & Verdicchio, 2019;Murray et al, 2020;Noorman & Johnson, 2014). Our starting point is that technology is not only embedded, shaped and informed by socio-organisational forces but also influences those forces (Fleming, 2019;Orlikowski, 2000;Orlikowski & Robey, 1991;Weißenfels et al, 2016), drawing us to sociomateriality as an initial and rich theoretical lens through which to interrogate AI agency as it describes the 'space in which the social and the material become entangled' (Orlikowski, 2009).…”

“…Orlikowski's (2009) earlier work assists in the theorisation of AI agency, by describing how technology is the result of continuous interactions between human actors, actions, choices, social histories and institutional contexts; therefore, its materiality is socially defined and produced and only relevant to people engaging with it (Orlikowski, 2009). She highlighted that technology will produce certain identifiable impacts on organisations (Orlikowski, 2009), which definitely applies to AI and its influence on firms (Murray et al, 2020). Orlikowski and Robey (1991) argued that information technology has social and material properties; it is constructed by human actions and objectified by institutionalisation.…”

Constructing Well-Being in Organizations: First Empirical Results on Job Crafting, Personality Traits, and Insight

“…In recent times, many advancements and changes have influenced work environments due to an ever-more-high-tech world with fewer boundaries and more globalized market systems [ 1 ]. New technologies have also caused work to intermingle more frequently with personal life [ 34 , 35 , 36 ], representing a challenge for competitive organizations [ 37 ]. Such uncertainty and loss of boundaries require empowered employees to face feelings of discouragement, bewilderment, and stress [ 38 ].…”

When Knowledge Work and Analytical Technologies Collide: The Practices and Consequences of Black Boxing Algorithmic Technologies

“…The benefits of these tools therefore may be accompanied by considerable risk for expertise and knowledge outputs (e.g., Nelson and Irwin, 2014;Bechky, 2020). Research has shown that to help offset these risks, knowledge workers strive to develop deep understanding of the inner workings of their technologies to maintain their expertise and ensure consistent quality in their work.…”

“…This study contributes to research that examines how knowledge workers respond to technologies that may threaten their expertise. Prior literature has found that workers may avoid these new technologies or may reframe their expertise to accommodate the technologies (e.g., Nelson and Irwin, 2014;Lifshitz-Assaf, 2018;Bechky, 2020). Yet all of this work assumes that knowledge workers are aware of how a tool might challenge expertise, and none considers what happens when workers lack such awareness.…”