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The potential application of generative AI (e.g. ChatGPT) in organizations
Source publication
Purpose
The primary purpose of this paper is to examine how generative Artificial Intelligence (AI) such as ChatGPT may serve as a new context for management theories and concepts.
Design/methodology/approach
The paper presents the analyses of selected management theories on decision-making, knowledge management, customer service, human resource m...
Contexts in source publication
Context 1
... at the administrative level, generative AI may be applied to automate different repetitive tasks such as scheduling appointments, generating business documents or record keeping. Figure 1 presents the potential application of ChatGPT in organizations. In the following sections, we present selected management theories and explain how ChatGPT may serve as a new context for these theories. ...
Context 2
... at the administrative level, generative AI may be applied to automate different repetitive tasks such as scheduling appointments, generating business documents or record keeping. Figure 1 presents the potential application of ChatGPT in organizations. In the following sections, we present selected management theories and explain how ChatGPT may serve as a new context for these theories. ...
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Citations
... Brex, a major provider of corporate card and spend management solutions, used Open AI technology to build AI tools that empower CFOs and finance teams with real-time answers and important insights. Finance leaders receive access to AI-powered chat interfaces and natural language processing capabilities Korzynski et al. [2023] via the Brex Empower platform, allowing them to make educated decisions and optimize corporate spending. The platform improves live budget capabilities by delivering AI-powered insights to finance professionals to analyze spending patterns, optimize budget allocation, and visualize spending evolution via bespoke graphs and visualizations. ...
The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones, revolutionizing sectors from production and manufacturing to finance. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact. This paper explores the sweeping changes driven by deep learning models like Large Language Models (LLMs), highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes. As we stand at the threshold of an AI-driven economic era, Generative AI is emerging as a pivotal force, driving innovation, disruption, and economic evolution on a global scale.
... Some previous studies (e.g. Ahuja et al., 2023;Korzynski et al., 2023) have partially discussed the needs for improving the multilingual performance of generative models. This concern highlights the importance of ensuring the linguistic accuracy and relevance of generative AI tools to enhance their usability and effectiveness in educational settings. ...
Purpose
This study explored the themes and sentiments of online learners regarding the use of Generative Artificial Intelligence (AI) or “generative AI” technology in higher education.
Design/methodology/approach
English-language tweets were subjected to topic modelling and sentiment analysis. Three prevalent themes were identified and discussed: curriculum development opportunities, lifelong learning prospects and challenges associated with generative AI use.
Findings
The results also indicated a range of topics and emotions towards generative AI in education, which were predominantly positive but also varied across male and female users.
Originality/value
The findings provide insights for educators, policymakers and researchers on the opportunities and challenges associated with the integration of generative AI in educational settings. This includes the importance of identifying AI-supported learning and teaching practices that align with gender-specific preferences to offer a more inclusive and tailored approach to learning.
... For example, early management theories by Frederick Taylor or Elton Mayo have been revisited using modern AI tools, uncovering nuances and alignments with current theories that were not previously recognized (Korzynski et al. (2023). This re-evaluation enriches our understanding of foundational leadership and management concepts, highlighting relevant principles. ...
The emergence of Artificial Intelligence (AI) has transformed the way historical data is interpreted, uncovering previously unseen trends and fundamentally altering modern management practices. This shift has significantly impacted the analysis of business history and leadership studies. However, there is still a notable gap in research regarding the effects of AI-driven reinterpretations of historical events on contemporary business strategies, leadership frameworks, and ethical considerations. This study aims to address this gap by exploring the methodologies and implications of AI in business history analysis, focusing on case studies of Walmart and JPMorgan Chase. Integrating foundational theories such as evolutionary theory, path dependency, transformational, and authentic leadership reveals how AI-driven insights can refine strategic planning, advance leadership development, and promote equitable management practices. It also highlights the ethical challenges in using AI for historical analysis, particularly the risks of perpetuating existing biases. Findings indicate that AI's capacity to analyze large volumes of historical data significantly reshapes our understanding of business evolution and leadership effectiveness. The study concludes with actionable recommendations for adopting ethical AI practices, outlining future research directions, including developing AI models for contextual analysis and exploring AI’s role in promoting diverse leadership models. This research advances academic discourse by providing a theory-driven framework for leveraging AI in business history and leadership studies. It underscores AI's transformative potential and complex implications for future business practices.
... AI technologies enhance the operational aspects of hospitality and tourism, and significantly improve guests' experiences through personalised services, achieved by automating decision-making processes using advanced data analysis and machine learning (Li et al., 2021). GenAI further enhances these capabilities by automating guest interactions and personalising communications processes, crucial in supporting employee roles and connections between guests, employers and employees (Korzynski et al., 2023). ...
The Hotel Management School Leeuwarden and the European Tourism Futures Institute organised a webinar to discuss and explore how artificial intelligence (AI) will impact the hospitality and tourism industry in the future. The webinar brought together a panel of academics, including Professor Iis Tussyadiah from the University of Surrey, Professor Stanislav Ivanov from the Varna University of Management, and Frederik Jan van der Meulen from Hotel Management School Leeuwarden, to discuss the multifaceted applications of AI in the industry. The speakers shared their insights through a series of presentations, underscoring AI integration’s strategic, economic and sustainability aspects. AI is presented as a critical element in enhancing customer experiences, optimising operational processes, and shaping the industry’s future landscape. The speakers discussed the role of generative AI in improving the industry’s resilience, the economic implications of AI’s integration and the challenges and strategies for sustainable AI adoption. This webinar goes beyond traditional AI debates by analysing AI’s strategic function in future-proofing hospitality and tourism businesses, its economic impact and its role in sustainability. The speakers’ crucial insights can help the hospitality and tourism industry embrace AI, highlighting the delicate balance between innovation, ethics and sustainability. This webinar’s extensive discussion of AI as a tool for efficiency and industry transformation makes it valuable for academics and industry professionals.
... As the generative AI industry grows, so does research in the academia. In particular, research exploring the applicability and future value of generative AI is gaining traction in various fields including healthcare (Zhang and Boulos 2023;Varghese and Chapiro 2024); finance (Lee and Chen 2022;Mogaji and Nguyen 2022); education (Lim et al. 2023;Tlili et al. 2023); and news production (Korzynski et al. 2023;Naeem et al. 2024). Recently, there has been research on managing the misuse and abuse of generative AI technologies such as ChatGPT (Fiona et al. 2023). ...
This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. This research verified the effect of these factors on intention to continue using the services and tested the meditating effect of trust and acceptance attitude. We tested this through verifying trust and acceptance attitudes. An online survey was conducted on language-based generative AI service users such as OpenAI’s ChatGPT, Google Bard, Microsoft Bing, and Meta-Lama, and a structural equation analysis was conducted through a total of 356 surveys. As a result of the analysis, individual, social, and technical motivational factors all had a positive (+) effect on trust and acceptance attitude on the attitude toward accepting generative AI services. Among them, individual motivation such as self-efficacy, innovation orientation, and playful desire were found to have the greatest influence on the formation of the acceptance attitude. In addition, social factors were identified as the factors that have the greatest influence on trust in the use of generative AI services. When it comes to using generative AI, it was confirmed that social reputation or awareness directly affects the trust in usability.
... Jarrari et al. (2023) highlight potential AI application in different KM processes such as sifting through organizational data and discovering relationships in knowledge creation processes, harvesting, classifying, organizing, storing, and retrieving explicit knowledge in knowledge storing and retrieving, and promoting equitable access to knowledge without fear of reprisal or social cost in knowledge application. Other authors state that organizations ma adopt ChatGPT to store, transform, and distribute organizational data (Korzynski et al., 2023). However, we aim to take a different approach by showing how KM can contribute to AI applications. ...
As the diversity and complexity of Artificial Intelligence (AI) systems increase, there is a growing need for advanced knowledge representation methods to enhance decision-making capabilities. Existing research indicates a gap between AI and Knowledge Management (KM), emphasizing the necessity of coordinating learning and knowledge creation processes between humans and machines. Despite the widespread use of generative AI, as seen through the growing popularity of conversational AI tools like chatbots powered by Large Language Models in recent years, the absence of a theoretical framework for effectively managing the knowledge they generate could mean missing out on significant opportunities. This work seeks to bridge this gap between KM and IA through an integrated framework that aims to apply KM to support IA chatbot applications, adapted from the Internet of Everything Integrated Knowledge Management Model (IoE IKM Model). The IoE IKM Model’s original goal is to support knowledge creation in IoE applications, but here, we show how it can be adapted to bring KM to the context of AI. We accomplish this by explaining the development process of the IoE IKM Model, identifying shared aspects between IoE and AI general applications, and adapting necessary elements to establish our integrated KM framework tailored for supporting AI chatbot applications. The resulting framework is then discussed, and examples of how it can be applied to enhance human interaction with a chatbot, namely Open AI's ChatGPT. Research has been conducted to demonstrate the advantages of applying AI in KM. However, we aim to take a different approach by showing how KM can contribute to AI applications. We expect this work to be helpful for those whose professional activities may involve the usage of AI systems by providing them with the necessary tools to manage the knowledge generated by these same AI systems and by offering a Knowledge Manager’s perspective on how to boost human-machine interaction.
... 1. Knowledge management: Knowledge management is the process of collecting, compiling, analysing, and disseminating knowledge within an organisation (Korzynski et al., 2023). The use of ChatGPT will allow information to spread throughout the organisation, thus enhancing knowledge creation and dissemination for all, and fostering the creation of shared business intelligence. ...
The prevalence of ChatGPT (and generative artificial intelligence in general) has precipitated a paradigm shift in diverse industries, including tourism and hospitality. ChatGPT revolutionalises all business functions (from marketing to operations), empowering tourism and hospitality organisations to transform and innovate their business models. This study seeks to comprehensively examine the use and implications of ChatGPT in tourism and hospitality by discussing the current and future state of the technology, while also suggesting an agenda for future research. To that end, six areas, namely, business intelligence and tourism analytics, tourism marketing and experience, hospitality services, cultural and heritage tourism, travel services, and destination management, are elaborated on in depth. By compiling views solicited from international experts, this groundbreaking opinion piece unveils profound insights into the evolutionary journey of an emerging technology that is shaping tourism and hospitality. The paper provides useful implications for tourism scholars and professionals alike.
... Mögliche Ansatzpunkte zu Einsatzpotenzialen der GKI wurden unter anderem im Bereich der Wirtschaftsinformatik-Lehre evaluiert [LM23]. Darüber hinaus gibt es in der wissenschaftlichen Literatur, bis auf wenige Ausnahmen, wie Korzynski et al. [KM23] und Wennker [We20], bislang keine weiterführenden Untersuchungen, die sich konkret mit der Anwendung von GKI in Unternehmen befassen. ...
... 2: Identifizierte Einsatzpotenziale der GKI in der Praxis (Eigene Darstellung) Die durchgeführte Literaturrecherche basiert auf einem Informationsstand von Mitte 2023. Die explorative Fallstudie liefert zusätzliche Erkenntnisse sowie konkrete neue Einsatzpotenziale, die über die kaum vorhandene Literatur wie die von Korzynski et al. [KM23] hinausgehen. Die in diesem Beitrag zusammengetragenen 17 Einsatzpotenziale für GKI wurden aktiv durch Gruppendiskussionen mit Innovationsexperten eines Versorgungsunternehmens ermittelt und erstrecken sich über drei Ebenen: Auf der strategischen Ebene beziehen sie sich auf die übergreifende Ausrichtung und die langfristige Planung des Unternehmens, exemplarisch durch die Erschließung neuer Geschäftsfelder oder die Stärkung der Wettbewerbsposition mittels KI-Innovationen. ...
... Moreover, guaranteeing compatibility, achieving data management, and facilitating seamless integration are key factors organizations must focus on (Zhou & Cen, 2023). Also, it is crucial to stress the criticality of tackling these technical challenges when integrating AI chatbots like ChatGPT into HR systems (Korzynski et al., 2023). ...
... However, when integrating AI into business processes, organizations must incorporate risk-based controls as a standard practice (Budhwar et al., 2023). Furthermore, deploying AI should also align with the recommended frameworks of the organization (Korzynski et al., 2023) to ensure effective and secure implementation. ...
... Gen AI refers to AI systems capable of autonomously producing new content like text, images, audio, and video [40,41]. It plays a crucial role in various sectors, with the market projected to grow significantly by 2026 [42]. Gen AI operates by creating open-ended systems through a combination of post-structuralist theories and neo-cybernetic mechanisms, utilizing both bottom-up and top-down approaches for system development [43]. ...
This paper aims to develop a groundbreaking approach to fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports tourism decision making and travel planning in smart tourism destinations. The acquisition of this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis and design, implementation, and user evaluation. The results revealed that the synergistic combination of these technologies was organized into three tiers. The system provides information, including place names, images, descriptive text, and an audio option for users to listen to the information, supporting tourists with disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable of generating predictive analytics, personalized recommendations, and conducting real-time, multilingual communication with tourists. This system was implemented and evaluated in Suphan Buri and Ayutthaya, UNESCO World Heritage sites in Thailand, with 416 users participating. The results showed that system satisfaction was influenced by (1) the tourism experience, (2) tourism planning and during-trip factors (attention, interest, and usage), and (3) emotion. The relative Chi-square (χ2/df) of 1.154 indicated that the model was suitable. The Comparative Fit Index (CFI) was 0.990, the Goodness-of-Fit Index (GFI) was 0.965, and the model based on the research hypothesis was consistent with the empirical data. This paper contributions significant advancements in the field of smart tourism by demonstrating the integration of Gen AI, NLP, and the IoT and offering practical solutions and theoretical insights that enhance accessibility, personalization, and environmental sustainability in tourism.