Artificial Intelligence and Digital Work: The Sociotechnical Reversal

2023 | HICSS | Citations: 0

Authors: Fischer, L.; Wunderlich, N.; Baskerville, R.

Abstract: A well-designed information system (IS) in the classical view comprises two int ... Expand

Abstract: A well-designed information system (IS) in the classical view comprises two interrelated yet different subsystems; one that represents the technological dimension of work; and one that represents the social dimension. When these subsystems are heralded as equally important, they constitute a sociotechnical whole, producing economic outcomes such as profit and efficiency, plus humanistic outcomes, such as engagement and well-being. We see, increasingly, this classical view becoming obliviated. In this conceptual paper, we reflect upon the role of humans and technology in these changing work environments. While technical aspects from Artificial Intelligence and digital technologies are dominating the social side of work, we suggest a sociotechnical reversal to happen. Whereas this technosocial reality might be well motivated by advances in efficiency and productivity, the effects on well-being and engagement are less well understood. Consequently, we provide a set of theoretically derived principles to guide these changes in the digital workplace. Collapse

Topics: artificial intelligence IT work datafication knowledge work societal evaluation criteria

Methods: theory development

Theories: socio technical theory sociotechnical systems theory

Introduction to Designing for Digital Mini-track

2022 | HICSS | Citations: 0

Authors: Popovic, Ales; Cerne, Matej; Wong, Sut I

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Topics: knowledge work virtual workplace remote work datafication

Methods: literature study survey

Machine Learning for ARUP: Time to Redefine the Ground Truth

2022 | International Conference on Information Systems | Citations: 0

Authors: Stroppiana Tabankov, Sergey

Abstract: Research accepts that ML-based AI tools' accuracy is a defining characteristic ... Expand

Abstract: Research accepts that ML-based AI tools' accuracy is a defining characteristic for AI implementation. Yet, the understanding of accuracy in relation to the "ground truth" remains under-researched, especially the understanding of universally recognized practices for the "ground truth" in specific knowledge domains. This short paper investigates how knowledge workers' expertise can be used effectively to redefine the "ground truth" and produce training datasets conducive to more accurate ML predictions. It approaches the question empirically with a case study of ARUP, a global engineering and consultancy firm that uses various AI tools for its advisory services. The paper highlights how executives often overlook data preparation and the role of knowledge workers during this phase, thus questioning the meaning of "ground truth". It provides valuable insights on how a total and constructive collaboration of stakeholders is essential for organizing existing data, contributing to existing literature on ML implementation and data in general. Collapse

Topics: artificial intelligence knowledge work datafication human resource management

Methods: qualitative interview case study personal interview triangulation

How Digital Business Strategy Shapes Task Complexity in Datafication of Knowledge Work

2022 | HICSS | Citations: 2

Authors: Wunderlich, Nico; Fischer, Louise

Abstract: Traditionally, knowledge workers delivered value from complex job tasks that wer ... Expand

Abstract: Traditionally, knowledge workers delivered value from complex job tasks that were determined by the amount of information they cognitively processed. In organizations practicing digital business strategies, information handling is progressively replaced by data processed for algorithmic decision support. When organizational processes are becoming increasingly data-driven, shifts in the central task component of complexity are expected. In this paper, we draw on a quantitative survey of 150 knowledge-intensive organizations in the U.S., and find that knowledge workers are perceiving increased complexity in their daily tasks, now shaped by the digital work environment. Executing a digital business strategy increases the IT dependency of business processes and emphasizes data as determinant of increased task complexity. Our research contributes to explain consequences of datafication for individual job tasks in knowledge work when organizations strategically strive to gain value from digital technologies and provides building blocks for future job design when designing for digital. Collapse

Topics: knowledge work business process management datafication value creation decision making

Methods: survey structural equation modeling PLS tool mediation analysis cross sectional research

Theories: theory of affordance

Datafication of Knowledge Work: A Framework and Research Agenda

2021 | HICSS | Citations: 2

Authors: Fischer, Louise; Wunderlich, Nico

Abstract: Datafication is moving into the center of value creation in the knowledge intens ... Expand

Abstract: Datafication is moving into the center of value creation in the knowledge intensive organization. Datafication describes the transformation of qualitative behavior and tacit knowledge into quantified actions and codified data. While traditional assumptions on knowledge work highlights individual autonomy in shaping job tasks and fitting abilities for productive work, the consequences of datafication for knowledge work are not yet well understood. We build on the contingency theory of performance as theoretical lens to derive a framework that addresses relevant future research questions in the evolving field of datafication in knowledge work. The proposed multi-level framework considers assumptions and elements from traditional productive knowledge work in combination with determinants of digital workplaces and organizational factors along the lines of data-based value creation and (semi-)automated decision making. For the future, we suggest viewing datafied knowledge work as a socio-technical phenomenon, thereby constituted of human-dominated knowledge work in convergence with technological-dominated algorithmic thinking. Collapse

Topics: datafication knowledge work task productivity decision making value creation

Methods: business process modeling computational algorithm machine learning cross sectional research theory development

Theories: contingency theory

Acting with Inherently Uncertain Data: Practices of Data-Centric Knowing

2021 | Journal of the Association for Information Systems | Citations: 0

Authors: Mikalsen, Marius; Monteiro, Eric

Abstract: Data-driven data science challenges our conceptualization of "data." Significan ... Expand

Abstract: Data-driven data science challenges our conceptualization of "data." Significantly beyond capturing a given phenomenon, data, increasingly, are the phenomenon. Data may be iteratively manipulated algorithmically, undermining the "faithfulness" of data to any originating phenomenon. Crucially, data that are not "faithful" are inherently uncertain, as data risk becoming meaningless symbols. We empirically study how a community of commercially based geoscientists grapples with the phenomenon of offshore oil and gas reservoirs located kilometers below the seabed. The data available about these reservoirs are algorithmically manipulated sensor-based Internet of Things data. Our main contribution is the articulation of three patterns of work practices detailing how inherently uncertain data are woven into consequential work practices: (1) Accumulating is the cumulative process of supporting and triangulating one set of data with supplementary data sets; accumulating captures the conservative approach of backing up existing interpretations of the data.(2) Reframing describes the process where existing interpretations are contested by new data or models; reframing captures that there are limits to how far data can be manipulated. (3) Prospecting, or the cultivation of competing, incompatible data interpretations; while the former two patterns essentially attempt to regulate uncertainty, prospecting is about embracing it. Our concept of datacentric knowing is constituted by these three interwoven, ongoing practices. Collapse

Topics: information system use offshoring word processing organizational change sensemaking

Methods: qualitative interview triangulation case study participant observation design artifact

Theories: theory of bounded rationality

Introduction to Designing for Digital Mini-track

2021 | HICSS | Citations: 0

Authors: Popovic, Ales; Cerne, Matej; Wong, Sut I

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Topics: remote work knowledge work IT supported collaboration datafication decision making

Methods: qualitative interview design science maturity model

Theories: ambidexterity theory