An Alternative Rationalisation of Creative AI by De-Familiarising Creativity: Towards an Intelligibility of Its Own Terms

Ng, Jenna

doi:10.16997/book55.d

Cited by 6 publications

(6 citation statements)

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“…Reliability refers to the ability of the AISA to perform the service dependably and accurately (Parasuraman et al, 1988). Three themes emerged as important when assessing the reliability of AISA—command recognition, intent recognition, and task fulfillment—and correspond with the sequential order in which AISA process commands that are given to them (A. Ng, 2019).…”

Section: Analysis and Findingsmentioning

confidence: 99%

Recasting Service Quality for AI-Based Service

Noor

Hill

Troshani

2021

Australasian Marketing Journal

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Artificial intelligence service agents (AISA), such as chatbots and virtual assistants, are becoming increasingly pervasive in service. Research to date has not adequately addressed how the unique nature of AISA shape consumers’ service quality expectations. A deeper understanding of AISA service quality is important for their successful deployment in the service sector. To address this gap, we reviewed marketing and information systems literatures and conducted qualitative in-depth interviews with 37 informants, inclusive of 28 AISA users and nine AISA experts. We developed a conceptual framework for how consumers use and evaluate AISA. Twelve service quality dimensions emerged from the qualitative evidence representing AISA service quality, two of which align with AISA’s unique characteristics. The study extends the service quality theory to a new context and offers fresh insights for theory and practice. It culminates with a research agenda to advance research on AISA service quality.

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Section: Analysis and Findingsmentioning

confidence: 99%

Recasting Service Quality for AI-Based Service

Noor

Hill

Troshani

2021

Australasian Marketing Journal

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“…2. AI, machine learning, and data science for digital marketing AI represents a large set of methods, techniques, and approaches to data processing that enable the intelligent behaviour of computers or machines, hence the development of intelligent computer systems (Ng, 2018). It is an interdisciplinary field that comprises numerous sub-disciplines and requires their study, such as computer vision, sensors, robotics, and language synthesis (Kotu & Deshpande, 2019).…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

Application of artificial intelligence in digital marketing

Marić,

Grljević,

Gluščević

2024

Anali Ekon fak Subotica

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Contemporary digital technologies have significantly influenced the transformation of marketing activities in companies over the past decade. Artificial intelligence is considered one of the most disruptive modern technologies. Their implementation is the most significant challenge of digital marketing. Artificial intelligence can automate customer service, generate content, and enable targeted ad campaigns. The problem identified by the authors is that smaller companies and entrepreneurs lack the necessary knowledge and understanding of artificial intelligence techniques that could enhance digital marketing strategies. By reviewing current literature, this paper aims to explain the digital marketing strategies and the role and place of various artificial intelligence techniques for their improvement. In line with the defined goal, the paper presents three case studies on industry giants-Coca-Cola, Starbucks, and Nike-illustrating successful implementations of artificial intelligence and machine learning techniques in their digital marketing.

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“…This is important because not every business problem may be solved by AI. It is also important to understand what questions AI can handle and what it cannot (Ng 2018). Second, having clear AI questions can lead to identifying the appropriate AI algorithms and technology to implement.…”

Section: • Ai Questionsmentioning

confidence: 99%

An AI decision‐making framework for business value maximization

2023

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This article addresses a key question of why businesses are failing to maximize business value from their artificial intelligence (AI) investments and proposes a strategic decision-making framework for AI decision-making to address this problem. We suggest that a firm's business strategy must drive AI-driven business outcomes and measurements, which in turn should drive the AI implementation decisions. Very often, we find that businesses fail to successfully cast business problems into AI problems. To bridge this gap, we propose that firms use a performance management system such as objectives and key results (OKRs) to ensure that the business and AI goals & objectives are well defined, tightly aligned, and made transparent across the company, and the AI efforts are approached in an integrated manner by the different parts of a firm. We use McDonald's use of AI initiatives as a business use case to demonstrate support for our AI decisionmaking framework. We argue that using the business strategy as a primary driver will enable firms to solve the right problems using AI, turning it to be a source of technology innovation and competitive advantage.

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An Alternative Rationalisation of Creative AI by De-Familiarising Creativity: Towards an Intelligibility of Its Own Terms

Cited by 6 publications

References 0 publications

Recasting Service Quality for AI-Based Service

Recasting Service Quality for AI-Based Service

Application of artificial intelligence in digital marketing

An AI decision‐making framework for business value maximization

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