Business Horizons最新文献

The new generative AI (GenAI) paradigm offers unprecedented opportunities for users to tap into. AI capabilities are increasingly helpful in creative and knowledge-intensive domains that have long been considered a territory of human expertise. The new breed of chatbots is based on large language models (LLMs), and they have overcome many constraints that plague the everyday use of previous AI technologies. This article employs the theory of affordances to understand how ChatGPT facilitates (i.e., affords) and constrains (i.e., disaffords) the usefulness of a new breed of chatbots. We further divide two distinct yet interrelated dimensions of new AI affordances: creational and conversational. Via 29 interviews with professionals using ChatGPT in various creative and knowledge-intensive sectors, we identify three creational affordances (content creation and enhancement, knowledge acquisition and creativity augmentation, and task automation) and three conversational affordances (contextual sensitivity, interactive accessibility, and human-AI workflow synergy). Creational affordances refer to the system’s ability to produce novel outputs and automate routine work, whereas conversational affordances encompass the variety of interaction possibilities with an AI system. Both affordances also involve disaffordances that limit the usefulness of the new types of AI systems. Furthermore, we introduce an integrated framework that shows how creational and conversational affordances reinforce each other via meta-affordances of accessibility, accumulation, and adaptability. We illustrate our findings with practical examples and offer guidelines for using these emerging capabilities in company settings.

Generative artificial intelligence (GenAI) is a method of machine learning that uses algorithms to create new content such as images, text, and video. In the last year, the popularity of GenAI has exploded. Websites like ChatGPT and DALL-E have become ubiquitous in everything from logo and NFT creation to social media content and artistic verse construction. While the popularity of GenAI is undeniable, the adoption of these technological tools has been splintered in higher education. This conceptual study examines the relationship between transparency and responsibility in the usage of GenAI. We go further, examining the relationship between training and application of skills within higher education. Finally, we propose a framework for how higher education can engage with GenAI to better prepare students to use it outside of school.

The ubiquity of chatbots continues to expand, offering a multitude of benefits for firms. While acknowledging the capabilities of AI chatbots to handle customer interactions and improve response times, we critically examine several challenges they present, including interoperability challenges, data protection concerns, and biased output. This research presents a novel framework for managers to assess a firm’s readiness to adopt AI chatbot technology through the lens of the technology acceptance model (TAM), which has been adapted to account for the critical challenges associated with the emerging technology. Incorporating four factors—subjective norms, compatibility, facilitating conditions, and trust—allows for a more holistic assessment of a firm’s readiness to adopt AI chatbot technology. The framework, together with a readiness assessment tool, provides a comprehensive mechanism for managerial decision-making, focusing on the adoption of AI chatbots in customer service, sales, and marketing business functions. In exploring these factors, our article explores the managerial implications of integrating AI chatbots into business processes, ensuring an informed and holistic approach to technology adoption.

Generative artificial intelligence—defined as AI-enabled technology that analyzes and learns from existing data and generates novel, humanlike content—has emerged as a revolutionary technology for firms seeking sustainable competitive advantage. We highlight the evolution of generative AI (GenAI) from generic, domain-tailored and collaborative systems, which are democratized and only offer demand-driven insights, to the next frontier of alternative perceptual systems. Managers who integrate current large language models into building their brand personae will empower their firms to experiment along the evolutionary journey. By embedding alternative perceptual systems into GenAI platforms, firms can achieve novel, interactive, and personalized insights that their competitors may find difficult to replicate.

Generative AI (GenAI) technology is evoking both excitement and fear about its potential impact across a host of industries—including advertising, where it is expected to have a significant disruptive effect. This article utilizes the paradox lens to explore the implications of text-to-text GenAI in the form of ChatGPT for the advertising industry. Drawing on 48 interviews with advertising professionals, we identify three operational paradoxes that are associated with conducting research, creativity, efficiency, and one psychological paradox related to work identity. To gain a competitive advantage, we urge practitioners to adopt a confrontation-based coping strategy to navigate these paradoxes. This can be mobilized via an ambidexterity or contingency paradox management approach. We outline specific tactics in this article.

This article focuses on how recent advances in artificial intelligence (AI), particularly chatbots based on large language models (LLMs), such as ChatGPT, can be used for innovation purposes. The article begins with a brief overview of the development and characteristics of generative AI (GenAI). Elaborating on the implications of GenAI, we provide examples to demonstrate four mechanisms of LLMs: translation, summarization, classification, and amplification. These mechanisms inform a framework that highlights how LLMs enable the creation of innovative solutions for organizations through capacities in two dimensions: context awareness and content awareness. The strength of LLMs lies in the combination of capacities in both these dimensions, which enables them to comprehend and amplify content. Four managerial suggestions are presented, ranging from starting out with small-scale projects and data exploration, to scaling through integration efforts and educating prompt engineers. By presenting the framework, recommendations, and examples of use cases in various contexts, the article contributes to the emerging literature on GenAI and innovation.

Generative artificial intelligence (GenAI) presents a disruptive innovation for brands and society, and the power of which is still yet to be realized. In the context of customer service, gen AI affords companies new possibilities to communicate, connect, and engage customers. This article draws on scholarly research and consultation with customer service leaders to present and discuss the possibilities for GenAI in the context of customer service, specifically GenAI chatbots. Importantly, this article presents potential paradoxes of GenAI-enabled customer service, adding to the debate about the role and impact of GenAI for brands. Specifically, we present six paradoxes of GenAI customer service: (1) connected yet isolated, (2) lower cost yet higher price, (3) higher quality yet less empathy, (4) satisfied yet frustrated, (5) personalized yet intrusive, and (6) powerful yet vulnerable. For each paradox, we suggest brand response strategies to mitigate downside and manage potential upside.

This article explores the evolution and prospects of conversational chatbots, specifically the latest generation referred to as generative artificial intelligence (GenAI) chatbots. This article comprehensively examines GenAI chatbots’ business applications and impact across macro, meso, and micro levels of organizations. At the macro level, this article explores how GenAI chatbots reshape industry dynamics. The meso perspective delves into organizational changes, while the micro lens focuses on enhancing individual productivity, learning, and creativity. GenAI chatbots’ immense potential is accompanied by risks in four areas: matching, ethics, technology, and adaptability (META). In response to these challenges, the article introduces a human-centric CARE framework—standing for collaboration, accountability, responsiveness, and empowerment—to mitigate the risks and to optimize the effects of GenAI chatbots. This work provides practical guidelines for navigating the complex landscape of GenAI implementation.