Call for Papers
Technologies are changing at a rapid pace and their impact is far-reaching. Pressures toward digital transformation and
investments in the development of new technologies are all contributing to the deployment of emerging technologies in a variety
of organizational processes and industries from healthcare to IT to transportation. Technologies such as artificial intelligence,
data analytics, robotics, digital platforms, blockchain, and 3D printing affect many parts of the organization, forging new
interdependencies within and between units, and outside the traditional boundaries of the organization (e.g., Bailey et al.,
2022). Alongside these transformations, we see the beginnings of much-needed conversations about the intersection between
technology, equity, and inclusion in the workplace. This sub-theme solicits papers that highlight both the inherent biases
and promising solutions that emerging technologies bring.
Emerging technologies bring with them a range of issues related to equity and inclusion in the workplace, such as excluding or increasing scrutiny of marginalized groups populations, exacerbating social biases, and taking power away from workers. Video cameras, electronic trackers, and other types of visual surveillance can make workers, especially those lower-skilled or from marginalized groups, feel dehumanized and bereft of dignity (Anteby & Chan, 2018). The rise of algorithmic management and its surveillance-based infrastructure can reify existing social biases that threaten efforts around equity and inclusion in the workplace (von Krogh, 2018; Kellogg et al., 2020). AI and machine learning algorithms are often trained with data taken from more privileged populations (e.g. white Americans), thus disadvantaging others, such as when facial recognition subjects workers of color to more scrutiny and make it harder for them to gain and maintain access to work systems (Ferrer et al., 2021). Toiling under the “tyranny of the algorithm” workers are often subject to inhumane production conditions, with minimum means of recourse (Lehdonvirta, 2018). On platforms like Uber, work is segmented into smaller and smaller chunks, making workers interchangeable, and thus, expendable (Cameron & Rahman, 2022, Cameron, 2022).
At the same time, research and anecdotal evidence show that new technologies can bring (or are perceived to bring) positive effects for the “good life” to traditionally marginalized groups, suggesting potential benefits for equity and inclusion. At the most basic level, digital platforms provide work opportunities for individuals who face challenges in securing traditional employment (Ravenelle, 2019), especially in areas like the Global South where unemployment rates are high. Accordingly, opportunities provided by digital labor platforms such as Upwork or Mechanical Turk may be perceived differently by workers from the Global South versus the Global North (Elbanna & Idowu, 2021; Wood et al., 2019). Indeed, the same platforms can become an occasion to restructure work identities and identify entrepreneurial opportunities, in particular for marginalized workers (Bellesia et al., 2019). New communication technologies can be deployed to facilitate the inclusion of individuals with disabilities, e.g. neurodiverse workers (Austin & Pisano, 2017) or to overcome status differences, e.g. between more fluent and less fluent speakers of the lingua franca of a global organization. The pervasiveness of body cameras worn by service personnel (e.g. the police) may act as a psychological deterrent against egregious behavior and protect personnel from erroneous claims of misbehavior (Patil & Bernstein, 2021). Finally, AI can be used to decrease stress and improve wellbeing in the workplace, for example by reducing pandemic PTSD (Peralta, 2021).
In this sub-theme, we invite papers that grapple with issues of how emerging technologies, their design, and use within organizations intersect with questions of equity and inclusion. We encourage empirical and theoretical papers with a variety of theoretical lenses (e.g., critical organization theory, critical race theory, socio-materiality, social identity theory, process theory, attachment theory, labor process theory, etc.) and methodological approaches. Here are some example topics that would fit with this sub-theme:
How are new technologies changing practices around equity and inclusion for workers, managers, and organizations, especially digitally-enabled organizing?
When and how is bias reinforced or overcome through the use of new technology in organizations? For example, how are social biases towards minorities and workers with disabilities affected by emerging technologies?
How are algorithmic decision-making systems being incorporated into bureaucratic processes? How do they obscure or reify race/ethnicity, national culture, gender and ability?
How do existing data-driven interventions, for example, in health, worker wellbeing, information distribution, and policing, address, ignore, and remake constructed categories like race/ethnicity, national culture, gender, age, and ability?
How do algorithmic systems operate through, as, or against domination and oppression? What are the practices of resistance or refusal used by targeted groups? How do different cultural backgrounds affect the perception of new technologies and with what consequences?
How do 3D printing, robotics, and other technology of Industry 5.0 change individual competences and roles and with what implications for marginalized workers?
- Anteby, M., & Chan, C.K. (2018): “A self-fulfilling cycle of coercive surveillance: Workers’ invisibility practices and managerial justification.” Organization Science, 29 (2), 247–263.
- Austin, R.D., & Pisano, G.P. (2017): “Neurodiversity as a competitive advantage.” Harvard Business Review, 95 (3), 96–103.
- Bailey, D., Faraj, S., Hinds P., Leonardi, P., & von Krogh, G. (2022): “We are all theorists of technology now: A relational view of emerging technology and organizing.” Organization Science, 33 (1), 1–18.
- Bellesia, F., Mattarelli, E., Bertolotti, F., & Sobrero, M. (2019): “Platforms as entrepreneurial incubators? How online labor markets shape work identity.” Journal of Managerial Psychology, 34 (4), 246–268.
- Cameron, L. (2022): “‘Making Out’ While Driving: The Relational and Efficiency Game in the Gig Economy.” Organization Science, 33 (1), 231–252.
- Cameron, L., & Rahman, H. (2022): “Expanding the Locus of Control: The Co-Constitution of Control and Resistance in the Gig Economy.” Organization Science, 33 (1), 38–58.
- Elbanna, A., & Idowu, A. (2021): “Crowdwork, digital liminality and the enactment of culturally recognised alternatives to Western precarity: beyond epistemological terra nullius.” European Journal of Information Systems, 31 (1), 1–17.
- Ferrer, X., van Nuenen, T., Such, J.M., Coté, M., & Criado, N. (2021): “Bias and Discrimination in AI: a cross-disciplinary perspective.” IEEE Technology and Society Magazine, 40 (2), 72–80.
- Kellogg, K.C., Valentine, M.A., & Christin, A. (2020): “Algorithms at Work: The New Contested Terrain of Control.” Academy of Management Annals, 14 (1), 366–410.
- Lehdonvirta, V. (2018): “Flexibility in the gig economy: managing time on three online piecework platforms.” New Technology, Work and Employment, 33 (1), 13–29.
- Patil, S.V., & Bernstein, E.S. (2021): “Uncovering the Mitigating Psychological Response to Monitoring Technologies: Police Body Cameras Not Only Constrain but Also Depolarize.” Organization Science, 33 (2), 541–570.
- Peralta, P. (2021): “How AI can ensure every employees’ mental health needs are met.” Benefit News, July 6, 2021; available at: https://www.benefitnews.com/news/artificial-intelligence-may-be-the-key-to-better-mental-health-strategies-for-employers.
- Ravenelle, A.J. (2019): Hustle and Gig. Oakland: University of California Press.
- von Krogh, G. (2018): “Artificial Intelligence in organizations: New opportunities for phenomenon- based theorizing.” Academy of Management Discoveries, 4 (4), 404–409.
- Wood, A.J., Graham M., Lehdonvirta V., & Hjorth I.(2019): “Good gig, bad gig: autonomy and algorithmic control in the global gig economy.” Work, Employment and Society, 33 (1), 56–75.