5/5 (1)

 

Many organizations apply scrum methodology into their project processes, as it has proven to be very efficient. In our other course, we have seen that system and application projects, scrum is a generally used method.

One of the characteristics of scrum working is that autonomous and self-reporting teams, rather than hierarchical structured teams, are working in time-based sprints (Rigby, Sutherland & Takeuchi, 2016). The mismatch hypothesis from evolutionary psychology can explain why the current society wants to take more distance from strong hierarchical organizations, and would prefer structures without leaders (Li, Vugt, & Colarelli, 2018). However, the conditions of scrum do not take into account how the employees may react to this new kind of working, especially the implementation of sprints – short periods of time dedicated to finish a particular process goal. In sprints, the team discusses what has to be done within the agreed time framework and everybody gets specific tasks within, so responsibilities. Herein, social control plays a crucial part. Barker (1993), pointed out in his research about Self-Managing Teams that high levels of social control is a causal factor of particular kind of great stress, because it is linked to team commitment and social factors clinging to that. Garton (2017) claims that organizational practices are the area that should improve in order to decrease burnouts. Three main organizational characteristics were found when he looked into organizations with high rates of burnouts: excessive collaboration, weak time management disciplines, and a tendency to overload the most capable with too much work.

Furthermore, agile methodology focuses on optimized speed, quality and stakeholder satisfaction, which can bring undesirable and unforeseen side effects such as heightened pressure on the teams (Hager & Protzman, 2017). Hager, vice president strategic initiatives at Macro Solutions and Protzman, Program manager at Macro Solutions (2017), warn that the fast sprints with deadlines even can make employees fatigue or burnout in a faster pace than in traditional waterfall teams. Laanti (2013) compares sprints to ‘project crisis mode’, where employees have clear tasks and prioritized deadlines under pressure. Traditionally, after such a period employees need some rest; however with scrum sprints the focus is on a constant pace so they are not able to fluctuate in workload pressure. However, this research also found that agile working increases empowerment, which reduces stress levels; nonetheless, this is not primarily based on effects of sprints. Kropp, Meier and Biddle (2016) found contrasting results, where professionals responded that agile practices are not beneficial and lead to more stress and could cause more overwork. Substantial support has been found that overwork is not neutral, and that it can be hurtful for the individual and thus for the organization. Moreover, the stress that is caused by overwork can lead to various kinds of health problems; mentally and physically. This is bad for the individuals’ lives, but also for the companies’ bottom line due to balance sheet costs (Green Carmichael, 2016).

Burn outs is a rapidly growing problem in Western countries, only in the Netherlands the economy suffers around 10 billion a year because of employees forced to stay at home due to a burn-out (Bakker, 2018). Matser (2018) claims persistent stress is damaging brains. There has been much research on how to prevent and treat a burn out (Hakanen and Bakker, 2017). The three characteristics of burn-outs are conceptualized by Maslach, Schaufeli and Leiter (p. 402, 2001) as “exhaustion, cynicism/depersonalisation, and reduced professional efficacy”. In this research, they lay out limited time constraint, role conflict/ambiguity and lack of social support as the three core influences on a burnout.

Taken together all the information regarding sprints and ‘scrum’ working and the rising amount of people suffering from burn outs; do you think that the implementations of “sprints” lead to higher chance of getting burned out in organizations nowadays?

 

References used and suggested research:
Bakker, A. B., & Costa, P. L. (2014). Chronic job burnout and daily functioning: A theoretical     analysis. Burnout Research, 1(3), 112–119. https://doi.org/10.1016/J.BURN.2014.04.003

Chambliss, D.F., Schutt R.K. (2006) Chapter 5: Causation and Experimental Design in the book  Making Sense of the Social World: Methods of Investigation

Garton, E. (2017). Employee Burnout Is a Problem with the Company, Not the Person. Harvard Business Review, 1. Retrieved fromhttps://hbr.org/2017/04/employee-burnout-is-a-   problem-with-the-company-not-the-person

Green Carmichael, S. (2016). The Research Is Clear: Long Hours Backfire for People and for Companies. Harvard Business Review, 2015–2017. Retrieved from https://hbr.org/2015/08/the-research-is-clear-long-hours-backfire-for-people-and-      for-companies

Hakanen, J. J., & Bakker, A. B. (2017). Born and bred to burn out: A life-course view and reflections on job burnout. Journal of occupational health psychology,        22(3), 354.

James R. Barker. Tightening the iron cage: Concertive control in self-managing teams. Administrative Science Quarterly, 38(3):408–437, 1993

Kropp, M., Meier, A., & Biddle, R. (2016, November). Agile practices, collaboration and experience. In International Conference on Product-Focused Software Process Improvement (pp. 416-431). Springer, Cham.

Laanti, M. (2013, January). Agile and Wellbeing–Stress, Empowerment, and Performance in Scrum and Kanban Teams. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 4761-4770). IEEE.

Leiter, M. P., & Maslach, C. (2003). Areas of worklife: A structured approach to organizational predictors of job burnout. Research in Occupational Stress          and Well Being. https://doi.org/10.1016/S1479-3555(03)03003-8

Leslie Kwoh. (2013). When Job Fatigue Hits the CEO – WSJ. Retrieved November 16, 2018,fromhttps://www.wsj.com/articles/SB10001424127887323687604578469124008524696?mod=WSJ_mgmt_LeadStoryCollection

 

Maslach, C. ;, Schaufeli, W. B. ;, & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology (Vol. 52). Retrieved from https://search-proquest-com.eur.idm.oclc.org/docview/205845280?accountid=13598

Rigby, D. K., Sutherland, J., & Takeuchi, H. (2016). Master the process that’s transforming management:: Interaction. Harvard business review, 94(7), 2.

 

5/5 (5)

 

Every time when a professional sector stands in the beginning of new technology, discussions arise among employees whether this technology will disrupt how they operate and whether certain career paths become redundant. There is no exception for the legal ecosystem, where they are facing the smart information technology such as artificial intelligence, transforming the legal ecosystem in various ways. In a study of McKinsey & Company about the possible automation of 800 professions, they found that robots are able to replace 23 per cent of the jobs currently held by legal officers (Johnson, 2017).

A study of Deloitte (2018) suggests that by 2037 the execution of 100,000 legal roles (In the United Kingdom) will be automated. In addition, they expect that law firms will experience a “tipping point” for new talent strategy by 2020 and advise all law firms to prepare themselves for a technology-transforming wave. Meaning, they advise that legal firms should focus on attracting more IT specialised people instead of only focusing on only legal employees. Moreover, Deloitte (2018) points out the fact that employees in the legal segment should put away their fear of failure that can be brought upon by AI, and that they should develop internal AI practices. Often, AI enhances what humans do and take up legal activities, resulting in enlarging the freedom to take up more complex tasks such as negotiating deals, appearing in court and advising their clients. In many disciplines is has been shown that AI frees the hands of specialists, allowing them to focus on deeper thinking they never got time for before (source documentary about AI in medicine). Law is an evidence-based industry; lawyers have to explain how and why they use certain arguments. Thus, the information that is used to base arguments on, is crucial and needs to be verified, so the decision-making process should be provided in a transparent matter (Banavar, 2016).

Law firms are transitioning; their strategies are more and more focusing on implementing or exploring the use of AI systems, for large firms (>1000 employees) this is even more than 90%, a study of US legal managing partners showed (Clay & Seeger, 2017). Marchant (2017) listed the AI application in legal practices: First of all, Technology Assisted Review (TAR) pioneered in legal practice as AI application, and is 50 times more efficient in document review than humans. Then, Legal Analytics (big data, algorithms and AI) are able to make predictions trends they extract from data. In addition, Practice Management Assistants such as the software ROSS, RAVN or Kira focus on specific legal areas and become ‘expert systems’. Another software is Legal bots, which are interactive online systems where customers can be helped with questions or situations. Lastly, algorithms (a very basic form of AI) is helping Legal Decision Making; Microsoft and U.S. Legal Services Corporation are making machine learning portals together, which can be beneficial for people who cannot afford legal help.

Clearly, legal firms are investing more and more into information technology software. How will this influence the employees that work within legal firms? As current research suggests, legal officers will distribute their work differently in the future, focusing on other deeper and more complicated tasks. More basic procedures will be processed quicker, so it will increase overall efficiency. In addition, decision-making is aided by intelligent information systems, analyzing an extensive amount of information in seconds. This is aligned to prior research in information technology area, which has been mostly directed at how the efficiency can be improved on how a user makes a particular decision, next to improving the decision-effectiveness (Pearson & Shim, 1995).

 

References:

Banavar, G. (2016). What It Will Take for Us to Trust AI. Harvard Business Review [Online]. Retrieved January 16, 2019, from https://hbr.org/2016/11/what-it-will-take-for-us-to-trust-ai

David Johnson, Find Out If a Robot Will Take Your Job, Time (Apr. 19, 2017),  http://time.com/4742543/ robots-jobs-machines-work/

J.M. Pearson, J.P. Shim, An empirical investigation into DSS 1187 structures and environments, Decision Support Systems 13 1188 (1995) 141 – 158