Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization

Makarius, Erin E.; Mukherjee, Debmalya; Fox, Joseph D.; Fox, Alexa K.

doi:10.1016/j.jbusres.2020.07.045

Cited by 285 publications

(211 citation statements)

References 88 publications

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“…In their definition, some scholars focus on the idea that AI should not need to be explicitly programmed to perform an intelligent task (Demlehner & Laumer, 2020). It should be able to sense, interpret, learn, plan, comprehend, and act on its own (Demlehner & Laumer, 2020;Kolbjørnsrud et al, 2017;Wang et al, 2019), meaning that AI should be able to correctly interpret external data, learn from this data, and use this learning to achieve specific goals and tasks through flexible adaption (Makarius et al, 2020). Doing so should be achieved without following predetermined rules or action sequences throughout the whole process (Demlehner & Laumer, 2020).…”

Section: Artificial Intelligencementioning

confidence: 99%

“…It is also identifiable that there are two main ways of defining AI. The first of these defines AI as a tool that solves a specific task that could be impossible or very time-consuming for a human to complete (Demlehner & Laumer, 2020;Makarius et al, 2020). The second group of definitions regards AI as a system that mimics human intelligence and cognitive processes, such as, interpreting, making inferences, and learning (Mikalef & Gupta, 2021).…”

Section: Artificial Intelligencementioning

confidence: 99%

“…A common notion in both categories is Kolbjørnsrud et al (2017) AI is defined as computers and applications that sense, comprehend, act, and learn. Afiouni (2019) AI is the general concept for computer systems able to perform tasks that usually need natural human intelligence, whether rule-based or not Lee et al (2019) Artificial Intelligence: Intelligent systems created to use data, analysis, and observations to perform certain tasks without needing to be programmed to do so Wang et al (2019) AI is a broad concept that captures the intelligent behavior of the machine Makarius et al (2020) Artificial Intelligence: a system's capability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaption Schmidt et al (2020) Artificial Intelligence: The endeavor to mimic cognitive and human capabilities on computers Demlehner and Laumer (2020) Artificial Intelligence: a computer system having the ability to percept, learn, judge, or plan without being explicitly programmed to follow predetermined rules or action sequences throughout the whole process. Wamba-Taguimdje et al (2020) Artificial Intelligence: defined as a set of "theories and techniques used to create machines capable of simulating intelligence.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Roles may need to be redesigned, and new roles can emerge. Thus, the employees need to understand the purpose of AI, what role it will play, and how it will change the employee's role and responsibilities within the organization (Makarius et al, 2020).…”

Section: Organizationalmentioning

confidence: 99%

“…However, despite the growing interest in AI, many companies struggle to realize value from AI (Fountaine et al, 2019). The expected benefits of AI may be absent even though companies invest time, effort, and resources into the adoption process (Makarius et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence and Business Value: a Literature Review

et al. 2021

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Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.

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Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Section: Organizationalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence and Business Value: a Literature Review

et al. 2021

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Digital data‐driven technologies and the environmental sustainability of micro, small, and medium enterprises: Does size matter?

Hernández,

Revilla,

Rodríguez

2024

Bus Strat Env

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Despite the increasing interest in understanding the relations between firms' digitalization and their environmental sustainability, they are still poorly understood as research on this hot topic is nascent and empirical evidence is sparse and fragmented. In this study, we delve into the relations between different digital data‐driven technologies (DTs) used for gathering and storing (smart devices and cloud computing), analyzing (Big Data and Artificial Intelligence), and sharing data (Blockchain) and environmental sustainability in micro, small, and medium enterprises (MSMEs). These firms, despite their relevance in the business landscape and their heterogeneity, are understudied in this area. Thus, we also examine the moderating role of firm size to understand the inherent diversity of MSMEs in the use of DTs and the development of the sustainability oriented practices. Empirical results from a large‐scale representative sample of European enterprises show that the adoption of DTs for data gathering and analysis is positively associated with sustainability‐oriented actions in MSMEs. The findings also reveal the positive moderating role of size: relations between data analysis and sharing technologies and sustainability are stronger for larger MSMEs, with the latter being significant only for medium‐sized enterprises.

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Employee experience –the missing link for engaging employees: Insights from an MNE's AI‐based HR ecosystem

Malik

Budhwar

Mohan³

et al. 2022

Human Resource Management

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Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)-assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI-assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI-mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI-assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI-assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed.

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Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization

Cited by 285 publications

References 88 publications

Artificial Intelligence and Business Value: a Literature Review

Artificial Intelligence and Business Value: a Literature Review

Digital data‐driven technologies and the environmental sustainability of micro, small, and medium enterprises: Does size matter?

Employee experience –the missing link for engaging employees: Insights from an MNE's AI‐based HR ecosystem

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