Machine reengineering: robots and people working smarter together

Shukla, Prashant; Wilson, H. James; Alter, Allan; Lavieri, David

doi:10.1108/sl-09-2017-0089

Cited by 8 publications

(8 citation statements)

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“…to often constitute barriers for effective implementation (Masli et al, 2016). Meso-level responses can be found, e.g., in concepts of human-AI collaboration (Jarrahi, 2018;Shukla et al, 2017), augmentation and conjoined agency (Murray et al, 2021). A range of studies have initiated a discourse on how AI interacts with extant cognitive rules and logics in various managerial areas such as, e.g., managing customers (Huang & Rust, 2018;Libai et al, 2020), strategies (Borges et al, 2021), decision making and data analytics (Duan et al, 2019;, processes (Tarafdar et al, 2019) and operations (Dogru & Keskin, 2020), as well as innovation (Cockburn et al, 2019;Haefner et al, 2021).…”

Section: Management Research As Sensegiver and Sensemaker For Applied...mentioning

confidence: 99%

“…It has shed light on AI applications in industry-for example, in production, marketing (Brock and von Wangenheim, 2019;Tarafdar et al, 2019), or innovation processes (Cockburn et al, 2019;Kakatkar et al, 2020). It has contributed to the broader socio-economic discourse by studying AI's effects of automation on labor shares (Wright and Schultz, 2018) and job specifications (Fleming, 2018;Huang and Rust, 2018), as well as the way humans and AI may work together (Jarrahi, 2018;Shukla et al, 2017). Beyond practical implications, these studies seek to also inform policy discussions on pressing societal issues such as sustainability, or job displacement and the future of work, thus, addressing related socioeconomic landscape trends (Acemoglu & Restrepo, 2018;Mokyr et al, 2015;Floridi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Divergent Transition Pathways for Artificial Intelligence: A Longitudinal and Multi-Level Perspective Using Structural Topic Modeling

Dahlke,

Ebersberger

2023

Preprint

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The potential of artificial intelligence (AI) to constitute a general-purpose technology with diverse algorithmic specifications makes it challenging to assess its overall impact on existing socio-economic regimes. Leveraging the multi-level perspective, we seek to depict the trajectory of micro-, meso-, and macro-level forces and their interactions to characterize AI transition pathways in industry. We treat business and information systems literature as a proxy capturing business practices that relate to factors influencing AI transitions on all three different levels. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related research, longitudinal patterns of topics, and structural changes of topic networks. The results indicate a strong and myopic focus on technological capabilities and efficiency rationales. Topic network structures indicate that transition pathways may diverge between a symbiotic and stabilizing transformation process and a more radical pathway of regime substitution. Based on these findings, we argue that sociotechnical transition pathways may not only occur in sequence, but simultaneously and ambiguously. This highlights the need for a nuanced understanding of convergent and divergent transition pathways for emerging digital general-purpose technology that do not tend to settle on one dominant design. We propose to leverage paradox theory to reconcile these tensions. JEL: M000, O310, O320, 033

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Section: Management Research As Sensegiver and Sensemaker For Applied...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Divergent Transition Pathways for Artificial Intelligence: A Longitudinal and Multi-Level Perspective Using Structural Topic Modeling

Dahlke,

Ebersberger

2023

Preprint

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“…However, there are only a few successful ML applications to substantiate this claim (Reis, 2020). The potential of ML in adding values and gaining competitive advantages is well known in this era of digital disruption (Bolton et al , 2019), and business leaders need to rethink the relationship between workers and machines (Shukla et al , 2017). Organizations are swimming in the ocean of data that can fuel ML (Akter et al , 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying research trends of machine learning in business: a topic modeling approach

Pramanik

Jana

2022

MBE

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Purpose This paper aims to discuss the suitability of topic modeling as a review method, identifies and compares the machine learning (ML) research trends in five primary business organization verticals. Design/methodology/approach This study presents a review framework of published research about adopting ML techniques in a business organization context. It identifies research trends and issues using topic modeling through the Latent Dirichlet allocation technique in conjunction with other text analysis techniques in five primary business verticals – human resources (HR), marketing, operations, strategy and finance. Findings The results identify that the ML adoption is maximum in the marketing domain and minimum in the HR domain. The operations domain witnesses the application of ML to the maximum number of distinct research areas. The results also help to identify the potential areas of ML applications in future. Originality/value This paper contributes to the existing literature by finding trends of ML applications in the business domain through the review of published research. Although there is a growth of research publications in ML in the business domain, literature review papers are scarce. Therefore, the endeavor of this study is to do a thorough review of the current status of ML applications in business by analyzing research articles published in the past ten years in various journals.

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“…The growth of data volumes available for analysis assists with managerial decisions and business intelligence (Gokhberg et al, 2017). It is essential to employ a clear data strategy to produce, capture and refine the data that is necessary for process redesign (Shukla, Wilson, Alter, & Lavieri, 2017).…”

Section: The Future Of Human Resources Managementmentioning

confidence: 99%

The future and the role of human resource management in South Africa during the Fourth Industrial Revolution

Schultz

2021

SA Journal of Human Resource Management

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Orientation: The world of work is evolving at an alarming rate, and human resource (HR) practitioners need to familiarise themselves with the future of human resource management (HRM) in order to add value to their organisations.Research purpose: This article presents South African HR practitioners’ views about the future and the role of HRM in the Fourth Industrial Revolution (4IR) from a qualitative perspective.Motivation for the study: Human resource practitioners play a central role in the 4IR, but theories on how their role is enacted remain insufficient.Research approach/design and method: A qualitative survey design was used to study the views of 105 HR practitioners affiliated with the South African Board of People Practices. Three open-ended questions were sent to participants by means of a SurveyMonkey link. Deductive and inductive coding were used to thematically analyse the data.Main findings: The following themes were identified: technology-driven, data-driven, ethically driven, change driven, business-driven, human–machine collaboration and presilience.Practical/managerial implications: South African HR practitioners should be prepared for the future world of work. If these HR practitioners are not technology-driven, data-driven, ethically driven, change driven, business-driven, human–machine collaboration and presilient, they may have difficulty to add value to the organisation in the 4IR.Contribution/value-add: This study extends the body of knowledge about the future world of work and the role of HRM in South Africa by founding that HR practitioners must have presilience and respect ubuntu. The study also extends contemporary scholarship by using an open-ended qualitative review design to investigate the future of HRM in South Africa during the 4IR.

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Machine reengineering: robots and people working smarter together

Cited by 8 publications

References 0 publications

Divergent Transition Pathways for Artificial Intelligence: A Longitudinal and Multi-Level Perspective Using Structural Topic Modeling

Divergent Transition Pathways for Artificial Intelligence: A Longitudinal and Multi-Level Perspective Using Structural Topic Modeling

Identifying research trends of machine learning in business: a topic modeling approach

The future and the role of human resource management in South Africa during the Fourth Industrial Revolution

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