Digital transformation in operations management: Fundamental change through agency reversal

Angelopoulos, Spyros; Bendoly, Elliot; Fransoo, JC Jan; Hoberg, Kai; Ou, Carol Xiaojuan; Tenhiälä, Antti

doi:10.1002/joom.1271

Cited by 20 publications

(16 citation statements)

References 68 publications

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“…This lens suggests that the challenges like opaque algorithmic recommendations (Lebovitz et al, 2022; Waardenburg et al, 2021), hidden epistemic assumptions within algorithms (Anthony, 2018; Lebovitz et al, 2021), stem from failures to understand people's work and how it shapes knowledge. This insight is key as organizations increasingly adopt algorithms to automate decision‐making amidst continuous digital transformation (Angelopoulos et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Prominent examples include ML and RPA algorithms. The ability to learn and, potentially, act, autonomously expands the spectrum of roles that algorithmic solutions play in organizations (Angelopoulos et al, 2023), offering a wider range of possibilities to improve knowledge work.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Our research contributes to the wider debate on the digital transformation of work, giving scholars and practitioners a better understanding of how organizations create and sustain operational improvements with algorithms in knowledge work involving skilled decision‐making (Angelopoulos et al, 2023). Specifically, we make three key contributions.…”

Section: Introductionmentioning

confidence: 96%

“…The digital transformation of organizations is reshaping the nature of knowledge work—a type of work that requires human expertise to make meaningful decisions that add value for organizations (Bechky, 2006; Davenport, 2015; Staats et al, 2011; Starbuck, 1992). Key to this transformation is adopting “modern algorithmic solutions” (Angelopoulos et al, 2023), formally defined as systems that autonomously learn from data and mimic human decision‐making (Angelopoulos et al, 2023; Bailey et al, 2019; Bailey & Barley, 2020). Examples include machine learning (ML) for predictive modeling, as well as generative artificial intelligence (AI) systems like Claude and ChatGPT.…”

Section: Introductionmentioning

confidence: 99%

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Using algorithms to improve knowledge work

Amaya,

Holweg

2024

J of Ops Management

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We explore how organizations leverage algorithms to improve knowledge work in contexts where the tasks require skilled work, as distinct from routine tasks that have traditionally been the focus of academic enquiry. Drawing on a multiple‐case study of four business areas in a multinational energy firm undergoing a digital transformation, we find that contrary to what the literature predicts, tasks that require skilled work can also benefit from the adoption of algorithmic solutions. To benefit, business areas engaged in two distinct pathways for transforming knowledge work. The first focuses on automating a specific task, replacing human activity with algorithms in a single task. The second involves re‐engineering an entire process, whereby sequences of steps adjacent to the task at hand are redesigned on integration of an algorithm. We find that these pathways have different effects on the ability to improve knowledge work, suggesting that alignment between the task and the pathway chosen is crucial to realizing any improvement. We also find that the ability to sustain any improvement depends on the adjustment of the knowledge regime—the practices and structures that sanction knowledge. Building on these findings, we propose a general process model for the adoption of algorithmic solutions in knowledge work. In the wider context of the future of work debate, our findings challenge the prevailing notion that a task's skill requirements determine the extent to which knowledge work can be improved by algorithmic solutions.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using algorithms to improve knowledge work

Amaya,

Holweg

2024

J of Ops Management

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“…Digital transformations across industries often require fundamental changes in digital business processes, products, services, or business models enabled by new software or information technology (IT) systems (Angelopoulos et al, 2023; Bharadwaj et al, 2013; Davison & Ou, 2017; Holmström et al, 2019), as customers expect new features on an almost continuous basis. Managing software development projects requires careful attention to achieve the twin objectives of high software quality and high levels of job satisfaction for IT professionals.…”

Section: Introductionmentioning

confidence: 99%

How deadline orientation and architectural modularity influence software quality and job satisfaction

Kude

Foerderer

Mithas

et al. 2023

J of Ops Management

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The implementation of digital transformation programs requires careful allocation of software developers to a variety of digital products and services with different levels of modularity. This paper investigates how deadline orientation (an individual‐level preference of developers for completing work close to deadlines) and architectural modularity (a characteristic of products) influence central outcomes in software development. We argue that architectural modularity positively interacts with deadline orientation to influence software quality and the job satisfaction of developers. Our empirical analyses, using rare and high‐quality data from 131 software developers and 29 product owners working at a captive software development center in India of a leading global software firm, confirm our hypotheses. We contribute to the literature on software development by showing that the fit between the technological characteristics of the software product (i.e., architectural modularity) and people factors (i.e., the temporal work style preferences of developers) plays an important role in shaping both software quality and job satisfaction. Our study has wider implications for the literature on software development, temporal work styles, and architectural modularity. It is instructive for practitioners tasked with hiring or allocating software developers for software products with varying technological characteristics in their digital transformation efforts.

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