We review existing system-based solutions to the growing problem of technostress. Based on an analysis of 102 digital applications for stress management, we find several significant limitations in the approaches of these tools and sparse evidence of their effectiveness in dealing with technostress. Thereafter, we propose a blueprint for an autonomous software agent that not only addresses the root of technostress by building user resilience towards technostress, but also generates contextually rich information that system creators and organizations can act upon to be more responsive to the experiences of individual users. The operation of the OARS (Open Affect Responsive Systems) is described with a user story.
vulnerable to technostress as technology continues to embed itself into our everyday lives. This has prompted the creation of digital support tools to help people address this problem. Most of the available tools target general stress management and promote wellbeing by simulating offline relaxationbased interventions. Of the few that specifically target technology as a cause of stress, the majority focus on monitoring and controlling user exposure to their devices. However, this mechanism itself is likely counterproductive. Constant monitoring and abundance of data constitutes a form of surveillance that increases feelings of pressure and triggers more technostress. There is need to switch focus from addressing acute symptoms of technology-induced stress to figuring out ways to address the root of the problem.
We propose a blueprint for a digital support tool that not only addresses the root of technostress by building user resilience towards technostress, but also generates contextually rich information that system creators and organizations can act upon to be more responsive to experiences of individual. We call this class of tools the Open Affect-Responsive Systems (OARS).
stress broadly refer to both immediate and drawn out stress responses attributable to potential or actual technology use (Agogo & Hess 2008). In practice, there are an abundance of digital-based solutions addressing this problem, but we do not have any systematic account on their mechanisms.
We analyzed 102 digital applications that are available on
popular application stores or are referred to in articles about
dealing with technostress. We found seven common
mechanisms among them (Figure 1) that generally follow one of
three approaches: (1) modification of IT features and its use
routines; (2) modification of individual reactions to IT
stressors; and (3) temporary disengagement from IT such as
online/offline venting
Some tools (35%) were created based on widely acknowledged intervention approaches (e.g., cognitive-behavioral therapy (CBT) and mindfulness), while others (36%) didn’t explicitly refer to neither theory nor intervention approach that would evoke confidence in their effectiveness. The majority (70%) were static systems, with pre-programmed responses while others were adaptive (30%), with most of those applying artificial intelligence (AI) at their core (24%). Of that subset, apps applied AI for different purposes - from identifying patterns in users’ emotional state based 2: Simulating offline relaxation intervention (e.g., Pocket Yoga, Colorfil, and Fidget Spinner)
on their interaction with their mobile device to acting as a virtual counsellor and conversational agent.
preter of behavior can lead to increased contact with
technology that may in turn trigger negative affective responses.
At the same time, scholars
OARS are a class of autonomous software agents are designed to drive improvements on the individual user level, system level as well as the organizational level. OARS have a four-stage system architecture (identify, formulate, evaluate and learn) that is iterative and employs AI to learn adaptively. These four stages occur across five subsystems which are independent modules that can be developed separately and in parallel to deliver a fully functional OARS (see Figure 1). Where possible, OARS integrate user feedback (collected as asynchronous pull data, instead of the synchronous push of constant monitoring – although that form of input may be possible as well). Such nudge-based user feedback can be used as labelled training data for constant learning and improvement of the OARS, as well as the development of user phenotypes which can make a personal AI possible. The architecture of OARS supports the application of multiple theoretically supported resilience-building mechanisms to make users less vulnerable to technostress. Based on contemporary stress management literature, we discern three promising mechanisms for delivery via OARS: active stress management (CBT), mindful monitoring (Acceptance and Commitment Therapy (ACT)), and hormesis. Let us here focus on hormesis to instantiate OARS and demonstrate its use.
exposure to low doses of a toxic substance can actually have
a beneficial effect, although exposure to those same toxins
in larger amounts might prove lethal
issues and found to be "at least moderately effective"
prove users' ability to deal with technostress, the system must be capable of delivering periodic low doses of typical IT stressors to users. When users asynchronously indicate they are experiencing an issue with the system (e.g. using a hotkey), OARS can restore system to its normal functioning and provide users with guidance to reframe such situations in the future. If implemented according to this and other design guidelines we propose, such operation of an OARS should increase the preparedness and confidence of users in the face of future unanticipated IT breakdowns. In the following section we offer a descriptive vignette of a user’s experience with the proposed OARS, along with a screenshot of the system prototype in action (Figure 2).
Jane logged onto her computer to complete the months accounts. She had recently installed a new accounting software and was hoping the experience went smoothly. During the installation, she had enabled the OARS add-on that came with the software. Her understanding was that she could press the ctrl-f12 hotkey if the system was not running as desired and her personal AI would drop in to help out. Within a few moments, she noticed the system felt a bit unresponsive. Her attention started to drift from the task at hand and even though she didn’t realize it, her heart began racing slightly as the nerves emerged. At that instant, the faded outline of a small notification window began to gently fade into view at the bottom right of the screen. It caught her eyes and she absent mindedly hit the hotkey while continuing to scroll through the application. A few seconds later, the full notification faded into view with the message “Trying to identify what the issue is…”. She ignored it and continued working. A few short moments later, the notification message changed to “Did that fix the issue for you?”. She paused for a micro-second as if to remind herself of the issue she had previously experienced, then she leaned back into her seat and continued working, the system seemed a little snappier. What Jane didn’t realize at that time was that the OARS had created a temporary processor bottleneck to simulate the slowing down on the processor that happened occasionally during the final computation phase of running the accounts. She would realize this in a few moments when she closed the application and received a final status message from the OARS “These experiences make you unique!”.
In summary, we can view OARS as personalized AI tailored to individual and organizational needs. The system learns from prior experience and improve its capability and user training. We also expect organizational learning to occur that results in a better understanding of the states and needs of its individual members, therefore creating a more desirable and stress-free work environment. This, in turn, would pay back in better performance.