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.

/pdf/artificial-intelligence-and-business-value-a-literature-2hf5nsx1ow.pdf

Artificial Intelligence and Business Value: a Literature Review

– Noting that a fundamental tenet of service‐dominant (S‐D) logic is the co‐creation of value‐in‐use, this paper aims to explore the theoretical possibility that the interactions between service systems cannot only co‐create value, but also have adverse consequences leading to actual value co‐destruction., – This conceptual paper critically reviews the dominance of value co‐creation and value‐in‐use in S‐D logic. Noting the relative lack of research in the converse possibility, the study proposes and explores the implications of value co‐destruction as a new concept which should be introduced within the framework of S‐D logic., – The study proposes a formal definition for the new proposed concept of value co‐destruction. It describes in detail the process by which it occurs, showing that value can be co‐destroyed through the interactions between different systems, resulting in value destruction‐through‐misuse. Indeed, value co‐destruction occurs when a service system accidentally or intentionally misuses resources (its own resources and/or those of another service system) by acting in an inappropriate or unexpected manner., – This paper is purely conceptual and exploratory. Empirical examination of the theoretical findings regarding value‐co‐destruction is required. Possible avenues of interest for such empirical research of value co‐destruction are suggested., – Limiting the occurrence of misuse by aligning the mutual expectations of interacting service systems should reduce the risks of value co‐destruction. Recovering from misuse should also be considered., – This study is apparently the first to have introduced the notion of value co‐destruction into the conceptual framework of S‐D logic.

Not always co-creation: introducing interactional co-destruction of value in service-dominant logic

Artificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.

/pdf/leveraging-artificial-intelligence-in-marketing-for-social-4b2l5d7oth.pdf

Leveraging Artificial Intelligence in Marketing for Social Good-An Ethical Perspective.

The objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.,Combining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.,The results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.,The review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.,The authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research


Purpose
The purpose of this study, a current systematic literature review, is to synthesize the extant literature on consumers’ adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. This study also outlines insights for academia, practitioners, AI marketers, developers, designers and policymakers.


Design/methodology/approach
This study used a content analysis approach to conduct a systematic literature review for the period of 10 years (2011–2020) of the various published studies themed around consumer’s adoption of AIR in HATS.


Findings
The synthesis draws upon various factors affecting the adoption of AIR, such as individual factors, service factors, technical and performance factors, social and cultural factors and infrastructural factors. Additionally, the authors identified four major barriers, namely, psychological, social, financial, technical and functional that hinder the consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.


Originality/value
To the best of the author’s/authors’ knowledge, this study is a first attempt to synthesize the factors that drive consumers’ adoption of artificial intelligence and robots in the hospitality and tourism industry. The present work also advances the tourism and consumer behavior literature by offering an integrated antecedent-outcome framework.


Visual abstract
Figure 2 The objective of the current systematic literature review is to synthesize the extant literature on consumer’s adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. For that purpose, authors conducted content analysis of extant literature on consumer’s adoption of AIR in HATS from 2011 to 2020. Authors presented an integrated antecedent outcome framework of the factors that drive consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.


Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: literature review and future research agenda

Artificial intelligence (AI)-powered chatbots are changing the nature of service interfaces from being human-driven to technology-dominant. As a result, customers are expected to resolve issues the...

The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective

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The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective