Socio-technical design and the control of variances
The development of the socio-technical approach offered a new paradigm of work design and development1. It is an approach which is under-pinned by a number of principles2:
Work systems focus: rather than focus on individual jobs, the approach regards the whole work system as the basic unit of analysis
Work group focus: rather than the centrality of the individual job holder, the approach treats the work group as being central
Internal work group regulation: the work group seek to manage itself rather than be externally regulated by a supervisor
Redundancy of functions: where the individuals develop multiple skills and increase the overall capability of the work group
Discretionary work roles: where work group members undertake their work with a high degree of discretion (autonomy) rather than follow a fixed set of rules
Individual-technology complementarity: individual and group roles are a complementary part of the work process rather than an extension of it
Variety increasing: where individual and group roles have high task variety rather than being limited to narrow, low discretion roles
In summary this means that those best placed within an organisation and who directly manage a work process are expected and are equipped to problem solve and correct for most of the variances in the work process. From a task point of view this means role holders having the ability to stretch the boundaries of their jobs into related and similar ones both alongside them and above them. This therefore requires the redesign of jobs as a near everyday process to accommodate changes in skills and the associated changes in accountabilities and responsibilities. For every change that is made to either share a task, delete, or modify a task, there is a need to undertake at least a basic risk assessment as to any impact on the health and safety of everyone involved, the process itself, the data and information captured.
Where the task level approach can help here – and where a database like O*NET can be used – is to further qualify those tasks being changed and modified. Using the O*NET database (or its equivalent in Europe, ESCO and CEDEFOP Task Database) helps to indicate the nature of the task in terms of its level (degree of education, training and experience required to be proficient), nature, and linkages to related tools and technology. By going into this greater level of detail ensures that the full ramifications of the changes are identified and acted upon. We also start to identify those tasks which remain “owned” by an occupation, and those which are shared across a team (and across set of shift teams).
It is also possible to identify those tasks which will be impacted by changes in the levels of automation available to process data. This is commonly referred to as artificial intelligence (AI) with the application of routine algorithms to make a series of data sorting and decision-making tasks. The occupations impacted can range from clerical and administrative roles through to those making financial support decision (mortgage and loan eligibility assessment) through to the driving of vehicles. Numerous studies have explored these issues3 and show the versatility of the task level data in exploring the impacts of potential and actual technology, together with exploring the bridging structures to support impacted job holders. Even in these more extreme circumstances there often remains a role which must handle the non-compliant cases i.e. variances are difficult to prescribe and predict.
Exploring the impact on technology on work and how this might be managed is made possible by working with task-level data. It is then possible to piece together re-configured roles and identify those which are potentially totally replaceable. The same is true also for viewing the growth and development of the ‘green economy’4 where the range of new work tasks vary across the occupations affected.
Notes:Davis, L.E. & Taylor, J.C. (eds)(1977) Design of Jobs. Penguin, Harmondsworth. Note the diagram, ‘A genealogy of job design’ on pages 14-15
Trist, E. (1981) The evolution of socio-technical systems: a conceptual framework and an action research program. Ontario Ministry of Government Service. Issues in the Quality of Working Life. Occasional Paper No 2
Vermeulan, B., Kesselhut, J., Pyka, A. and Saviotti, P.P. (2018) “The impact of automation on employment: just the usual structural change?”, Sustainability, 10 (1), 1-27; McKinsey (2017) A future that works: automation, employment and productivity. McKinsey Global Institute. 148 pages, and Jobs lost, jobs gained: workforce transition in a time of automation. 160 pages. The future of women at work. Transitions in the age of automation. 168 pages
CEDEFOP (2019) Skills for Green Jobs. European synthesis report. CEDEFOP. 104 pages