In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations

Chiu, Yi-Te; Zhu, Yu‐Qian; Corbett, Jacqueline

doi:10.1016/j.ijinfomgt.2021.102379

Cited by 59 publications

(53 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to the cognitive and operational capabilities of AI-based platforms (Chiu et al 2021), intelligent DPAs have the potential to collaborate with individuals in planning, monitoring, and control of thinking, feeling, and actions. AI's automated action supported by natural language processing and machine learning algorithms may, therefore, affect co-regulation dynamics.…”

Section: Theoretical Lens: Co-regulation and Technology Affordancesmentioning

confidence: 99%

“…You et al 2021) by capturing data on their behaviour and making suggestions on how they can improve. However, as noted earlier, an understanding of the human-AI relationship as it occurs in practice is currently lacking, which prevents researchers and practitioners from addressing negative attitudes and resistance when AI is introduced to help workers (Chiu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Partnering with AI: the case of digital productivity assistants

Cranefield

Winikoff

Chiu

et al. 2022

Journal of the Royal Society of New Zealand

Self Cite

View full text Add to dashboard Cite

An emerging class of intelligent tools that we term Digital Productivity Assistants (DPAs) is designed to help workers improve their productivity and keep their work-life balance in check. Using personalised work-based analytics it raises awareness of individual collaboration behaviour and suggests improvements to work practices. The purpose of this study is to contribute to a better understanding of the role of personalised work-based analytics in the context of (improving) individual productivity and work-life balance. We present an interpretive case study based on interviews with 28 workers who face high job demands and job variety and our own observations. Our study contributes to the still ongoing sensemaking of AI, by illustrating how DPAs can co-regulate human work through technology affordances. In addition to investigating these opportunities of partnering with AI, we study the perceived barriers that impede DPAs' potential benefits as partners. These include perceived accuracy, transparency, feedback, and configurability, as well as misalignment between the DPA's categorisations of work behaviour and the categorisations used by workers in their jobs.

show abstract

Section: Theoretical Lens: Co-regulation and Technology Affordancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partnering with AI: the case of digital productivity assistants

Cranefield

Winikoff

Chiu

et al. 2022

Journal of the Royal Society of New Zealand

Self Cite

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) technology enables human-like cognitive capacity for advanced decision making in contemporary intelligent systems (Dwivedi et al, 2021;Hradecky et al, 2022). Despite these advances, research shows that human perceptions can trigger behavioral responses that affect an organization's capability to leverage such systems when predictions are presented to end users (Berger et al, 2021;Chiu et al, 2021). As certain algorithms exhibit a higher degree of explainability through their inherent interpretability, end users may perceive them more benevolently than others (Shin, 2021;Wanner et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

Herm

Heinrich

Wanner

et al. 2023

International Journal of Information Management

View full text Add to dashboard Cite

“…Furthermore, adoption of AI in B2B marketing presents challenges to the traditional view of B2B service ecosystems (Vargo et al, 2017 ) whereby third-party suppliers provide AI marketing solutions for marketing buyers resulting in the automation of marketing processes (Davenport et al, 2020 ). Whilst prior studies have examined issues regarding new technology within B2B networks (e.g., Jaakkola & Hakanen, 2013 ), the influence of AI adoption and implementation from the marketers’ perspective remains under studied (Borges et al, 2021 ; Chiu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

2022

View full text Add to dashboard Cite

Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.

show abstract

In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations

Cited by 59 publications

References 105 publications

Partnering with AI: the case of digital productivity assistants

Partnering with AI: the case of digital productivity assistants

Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Contact Info

Product

Resources

About