Designing IS for Personal Productivity

Designing IS for Personal Productivity

The progressive automation of value creation processes, accelerated innovations in many areas of society, and their transformation into an information and service society are leading to increasing knowledge work [3, 7, 13]. Knowledge work refers to all forms of work characterized by a high knowledge intensity, creativity, and, often, novelty [1, 7]. Examples include project work, research and development activities, and professional services [3, 7].

On the one hand, modern knowledge work benefits from information and communication technology, because it provides tools that users can apply to better meet knowledge work’s requirements [1, 10]. On the other hand, modern Internet-based information and communication technologies often result in an information overload, which is an increasing challenge for knowledge workers [1]. Knowledge workers often regard this overload as such a burden that they increasingly experience information systems, such as e-mail, chat systems, and social media, which they previously considered useful and purposeful, as "impediments" and no longer as "enablers" of productive knowledge work [1, 11, 12]. In addition, the increasing densification of knowledge workers’ labor not only reduces their individual well-being, but might even have dysfunctional effects on their health, the quality of their work outcomes, and on their productivity [10, 11, 13, 14].

In recent years, psychological research, and especially neuroscience research, have identified and corroborated a variety of findings that greatly enhances our understanding of the antecedents, processes, and outcomes of productive knowledge work [2, 4, 5, 6, 7, 8, 9, 10, 13]. These findings stem from a broad variety of different research areas, such as those dealing with motivation, concentration, engagement, creativity, decision-making, learning, and memory [2, 5, 6, 7, 8]. These findings have implications for work organization, leadership, and tools with which to support knowledge workers, many of which have not yet been fully tapped [5, 8, 9]. Specifically, information systems research has not yet embraced the new knowledge obtained from the above research or translated it into actionable recommendations for the design and use of software tools [4]. There is a well-founded expectation that new IS-based solutions relying on recent psychological and neuroscience research results will make a significant contribution to manage current challenges, such as information overload or work intensification, better.

Our aim is therefore to contribute to research and practice by systematically analyzing knowledge work’s current challenges and by developing viable IS-based solutions. These should not only help increase knowledge workers’ productivity, but also increase their well-being. The preliminary research goals are:
•    an analysis of knowledge work’s current challenges on an individual level;
•    a review of current psychological and neuroscientific research in order to identify relevant prior work;
•    developing a software platform as a basis for developing and testing software-based approaches to increase individual productivity and well-being;
•    developing software tools and interventions to enhance individual productivity and well-being;
•    testing/validating interventions and tools through experiments, field studies, and surveys; as well as
•    condensing and integrating the findings and tools into an overarching framework for knowledge workers’ personal productivity and personal well-being.

2022- present (ongoing)

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2.    Jones, E. C., & Chung, C. A. (2006). A methodology for measuring engineering knowledge worker productivity. Engineering Management Journal, 18(1), 32-38.
3.    Sumanth, D. J., Omachonu, V. K., & Beruvides, M. G. (1990). A review of the state–of–the–art research on white–collar/knowledge–worker productivity. International Journal of Technology Management, 5(3), 337-355.
4.    Palvalin, M., Lönnqvist, A., & Vuolle, M. (2013). Analysing the impacts of ICT on knowledge work productivity. Journal of Knowledge Management.
5.    Noruzy, A., Hayat, A. A., Rezazadeh, A., Najafi, S., & Hatami-Shirkouhi, L. (2011). Factors influencing the productivity of knowledge workers: a case study from an Iranian Oil Company. International Journal of Productivity and Quality Management, 8(4), 459-479.
6.    Butt, M. A., Nawaz, F., Hussain, S., Sousa, M. J., Wang, M., Sumbal, M. S., & Shujahat, M. (2019). Individual knowledge management engagement, knowledge-worker productivity, and innovation performance in knowledge-based organizations: the implications for knowledge processes and knowledge-based systems. Computational and Mathematical Organization Theory, 25, 336-356.
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8.    Yusoff, M. Z., Mahmuddin, M., & Ahmad, M. (2017). The effect of knowledge work productivity factors on software development. Journal of Engineering Science and Technology Special, 12(Special Issue), 64-73.
9.    Kianto, A., Shujahat, M., Hussain, S., Nawaz, F., & Ali, M. (2019). The impact of knowledge management on knowledge worker productivity. Baltic Journal of Management, 14(2), 178-197.
10.    Palvalin, M. (2019). What matters for knowledge work productivity?. Employee Relations, 41(1), 209-227.
11.    Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061-1072.
12.    Leshed, G. (2012). Slowing down with personal productivity tools. Interactions, 19(1), 58-63.
13.    Óskarsdóttir, H. G., Oddsson, G. V., Sturluson, J. Þ., & Sæmundsson, R. J. (2022). Towards a holistic framework of knowledge worker productivity. Administrative Sciences, 12(2), 50.
14.    Soto, M., Satterfield, C., Fritz, T., Murphy, G. C., Shepherd, D. C., & Kraft, N. (2021). Observing and predicting knowledge worker stress, focus and awakeness in the wild. International Journal of Human-Computer Studies, 146, 102560.