{"id":20716,"date":"2021-06-03T22:38:34","date_gmt":"2021-06-03T16:53:34","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=20716"},"modified":"2021-06-03T22:38:41","modified_gmt":"2021-06-03T16:53:41","slug":"a-better-way-to-introduce-digital-tech-in-the-workplace","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/a-better-way-to-introduce-digital-tech-in-the-workplace\/","title":{"rendered":"A better way to introduce digital tech in the workplace"},"content":{"rendered":"\n

CAMBRIDGE, Mass. — When bringing technologies into the workplace, it pays to be realistic. Often, for instance, bringing new digital technology into an organization does not radically improve a firm\u2019s operations. Despite high-level planning, a more frequent result is the messy process of frontline employees figuring out how they can get tech tools to help them to some degree.<\/p>\n\n\n\n

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That task can easily fall on overburdened workers who have to grapple with getting things done, but don\u2019t always have much voice in an organization. So isn\u2019t there a way to think systematically about implementing digital technology in the workplace?<\/p>\n\n\n\n

MIT Professor Kate Kellogg thinks there is, and calls it \u201cexperimentalist governance of digital technology\u201d: Let different parts of an organization experiment with the technology \u2014 and then centrally remove roadblocks to adopt the best practices that emerge, firm-wide.<\/p>\n\n\n\n

\u201cIf you want to get value out of new digital technology, you need to allow local teams to adapt the technology to their setting,\u201d says Kellogg, the David J. McGrath Jr. Professor of Management and Innovation at the MIT Sloan School of Management. \u201cYou also need to form a central group that\u2019s tracking all these local experiments, and revising processes in response to problems and possibilities. If you just let everyone do everything locally, you\u2019re going to see resistance to the technology, particularly among frontline employees.\u201d<\/p>\n\n\n\n

Kellogg\u2019s perspective comes after she conducted an 18-month close ethnographic study of a teaching hospital, examining many facets of its daily workings \u2014 including things like the integration of technology into everyday medical practices.<\/p>\n\n\n\n

Some of the insights from that organizational research now appear in a paper Kellogg has written, \u201cLocal Adaptation Without Work Intensification: Experimentalist Governance of Digital Technology for Mutually Beneficial Role Reconfiguration in Organizations<\/a>,\u201d recently published online in the journal Organization Science<\/em>.<\/p>\n\n\n\n

In the hospital<\/strong><\/p>\n\n\n\n

Kellogg\u2019s on-the-ground, daily, ethnographic research took place in the primary care unit of an academic hospital in the northeastern U.S., where there were six medical teams, each consisting of seven to nine doctors, and three or four nurses and medical assistants, as well four or five receptionists.<\/p>\n\n\n\n

The primary care group was transitioning to using new digital technology available in the electronic health system to provide clinical decision support, by indicating when patients needed vaccinations, diabetes tests, and pap smears. Previously, certain actions might only have been called for after visits with primary-care doctors. The software made those things part of the preclinical patient routine, as needed.<\/p>\n\n\n\n

In practice, however, implementing the digital technology led to significantly more work for the medical assistants, who were in charge of using the alerts, communicating with patients \u2014 and often assigned even more background work by doctors. When the recommendation provided by the technology was not aligned with a doctor\u2019s individual judgment about when a particular action was needed, the medical assistants would be tasked with finding out more about a patient\u2019s medical history.<\/p>\n\n\n\n

\u201cI was surprised to find that it wasn\u2019t working well,\u201d Kellogg says.<\/p>\n\n\n\n

She adds: \u201cThe promise of these technologies is that they\u2019re going to automate a lot of practices and processes, but they don\u2019t do that perfectly. There often need to be people who fill the gaps between what the technology can do and what\u2019s really required, and oftentimes it\u2019s less-skilled workers who are asked to do that.\u201d<\/p>\n\n\n\n

As such, Kellogg observed, the challenges of using the software were not just technological or logistical, but organizational. The primary-care unit was willing to let its different groups experiment with the software, but the people most affected by it were least-well positioned to demand changes in the hospital\u2019s routines.<\/p>\n\n\n\n

\u201cIt sounds great to have all the local teams doing experimentation, but in practice \u2026 a lot of people are asking frontline workers to do a lot of things, and they [the workers] don\u2019t have any way to push back on that without being seen as complainers,\u201d Kellogg notes.<\/p>\n\n\n\n

Three types of problems<\/strong><\/p>\n\n\n\n

All told, Kellogg identified three types of problems regarding digital technology implementation. The first, which she calls \u201cparticipation problems,\u201d are when lower-ranking employees do not feel comfortable speaking up about workplace issues. The second, \u201cthreshold problems,\u201d involve getting enough people to agree to use the solutions discovered through local experiments for the solutions to become beneficial. The third are \u201cfree rider problems,\u201d when, say, doctors benefit from medical assistants doing a wider range of work tasks, but then don’t follow the proposed guidelines required to free up medical assistant time. <\/p>\n\n\n\n

So, while the digital technology provided some advantages, the hospital still had to take another step in order to use it effectively: form a centralized working group to take advantage of solutions identified in local experiments, while balancing the needs of doctors with realistic expectations for medical assistants.<\/p>\n\n\n\n

\u201cWhat I found was this local adaptation of digital technology needed to be complemented by a central governing body,\u201d Kellogg says. \u201cThe central group could do things like introduce technical training and a new performance evaluation system for medical assistants, and quickly spread locally developed technology solutions, such as reprogrammed code with revised decision support rules.\u201d<\/p>\n\n\n\n

Placing a representative of the hospital\u2019s medical assistants on this kind of governing body, for example, means \u201cthe lower-level medical assistant can speak on behalf of their counterparts, rather than [being perceived as] a resister, now [they\u2019re] being solicited for a valued opinion of what all their colleagues are struggling with,\u201d Kellogg notes.<\/p>\n\n\n\n

Another tactic: Rather than demand all doctors follow the central group\u2019s recommendations, the group obtained \u201cprovisional commitments\u201d from the doctors \u2014 willingness to try the best practices \u2014 and found that to be a more effective way of bringing everyone on board.<\/p>\n\n\n\n

\u201cWhat experimentalist governance is, you allow for all the local experimentation, you come up with solutions, but then you have a central body composed of people from different levels, and you solve participation problems and leverage opportunities that arise during local adaptation,\u201d Kellogg says.<\/p>\n\n\n\n

A bigger picture<\/strong><\/p>\n\n\n\n

Kellogg has long done much of her research through extensive ethnographic work in medical settings. Her 2011 book \u201cChallenging Operations,\u201d for instance, used on-the-ground research to study the controversy of the hours demanded of medical residents. This new paper, for its part, is one product of over 400 sessions Kellogg spent following medical workers around inside the primary care unit.<\/p>\n\n\n\n

\u201cThe holy grail of ethnography is finding a surprise,\u201d says Kellogg. It also requires, she observes, \u201ca diehard focus on the empirical. Let\u2019s get past abstractions and dig into a few concrete examples to really understand the more generalizable challenges and the best practices for addressing them. I was able to learn things that you wouldn\u2019t be able to learn by conducting a survey.\u201d<\/p>\n\n\n\n

For all the public discussion about technology and jobs, then, there is no substitute for a granular understanding of how technology really affects workers. Kellogg says she hopes the concept of experimentalist governance could be used widely to help harness promising-but-imperfect digital technology adoption. It could also apply, she suggests, to banks, law firms, and all kinds of businesses using various forms of enterprise software to streamline processes such as human resources management, customer support, and email marketing.<\/p>\n\n\n\n

\u201cThe bigger picture is, when we engage in digital transformation, we want to encourage experimentation, but we also need some kind of central governance,\u201d Kellogg says. \u201cIt\u2019s a way to solve problems that are being experienced locally and make sure that successful experiments can be diffused. \u2026 A lot of people talk about digital technology as being either good or bad. But neither the technology itself nor the type of work being done dictates its impact. What I\u2019m showing is that organizations need an experimentalist governance process in place to make digital technology beneficial for both managers and workers.\u201d<\/p>\n\n\n\n

\u2014 MIT News<\/em> Office<\/p>\n","protected":false},"excerpt":{"rendered":"

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