Search results

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.

The paper presents the analyses of selected management theories on decision-making, knowledge management, customer service, human resource management and administrative tasks and explains what may change after generative AI adoption.

The paper indicates that some management theories and concepts need to be studied in the generative AI environment that may influence managerial work at the strategic, functional and administrative levels.

This paper is an opinion piece article and does not refer to empirical data. It formulates some conclusions to further empirical research studies.

The paper analyzes selected management theories in a new technological setting. The paper also provides information about the functions of generative AI that are useful in understanding and overcoming how new technology may change organizations and management.

In addressing the future trajectory of knowledge management systems, this paper uses the psycho-social notion of generativity which recently stimulated contributions in technology and innovation for a holistic systemic knowledge management (KM) review. The purpose of this study is to identify current shortcomings and fixations together with their ramifying affordances, all enveloped within a novel KM concept and prototype-system-under-development.

It follows up on prior publications using design science research (DSR) methodologies in compliance with theory effectiveness, a principle expecting system designs to be purposeful in terms of utility and communication. The KM perspective taken prioritizes a decentralizing agenda benefiting knowledge workers while also aiming to foster a fruitful co-evolution with traditional organizational KM approaches.

The notions of generative fit and capacities in their technical, informational and social interpretations prove able to accommodate diverse KM models and to cumulatively synthesize a wide range of related concepts and perspectives. In the process, Nonaka’s renowned socialize, externalize, combine, internalize and Ba model is repurposed and extended to suggest a corresponding complementing seize, imbed, collate, encompass, effectuate workflow embedded in distinct digital ecosystems fully aligned to the diversity of the generative attributes introduced.

Although the prototype development is still in progress, the study conforms to the DSR practice to report on early visions of technology impact on users, organizations and society and also refers to and reflects on aspects of feasibility, suitability, acceptability and the system’s prospect as a general-purpose technology or disruptive innovation.

The paper transdisciplinarily integrates the well-established psychological notions of generativity into its newer digital and systemic KM dimensions. The resulting new insights transparently inform the concept and prototype design, present a holistic framework for individuals and organizations and suggest avenues for new KM applications and KM research directions inspired by the adopted and adapted novel generativity contexts.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.

One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.

Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.

The method is only designed to defend against MIA in black-box classification models.

The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.

This paper aims to demonstrate how the new generative artificial intelligence (AI) tool ChatGPT changes knowledge work for individuals and what are the implications of this change for companies.

Based on 22 interviews from informants across different industries, the authors conducted an inductive analysis on the use and utility of ChatGPT in knowledge work. Based on this initial analysis, they discovered different ways in which ChatGPT either augments human agency, makes it redundant or lacks capability in that regard.

The authors develop a 2 × 2 framework of algorithmic assistance, which demonstrates four ways in which ChatGPT (and generative AI in general) interacts with knowledge workers, depending on the usefulness of ChatGPT in particular tasks and the type of the task (routine vs creative).

Based on the insights from the interviews, the authors propose a set of actionable questions for individual knowledge workers and companies from four viewpoints: skills and capabilities; team structure and workflow coordination; culture and mindset; and business model innovation.

To the best of the authors’ knowledge, this study is among the first to identify and analyze the use of ChatGPT by knowledge workers across different industries.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.

The purpose of this study is to fill a gap in the literature by examining the import and impact of the generative leadership philosophy and praxis of Ambassador Aurelia Erskine Brazeal, an African American Female Foreign Service Officer.

This single subject case study, augmented by portraiture, employs an interdisciplinary methodological design also using polyvocal narrative, oral history and arts-based research.

The research revealed that a prosocial disposition, compassion, strategic vision, clarity of purpose, commitment to fair play, focus on balance, hearing everyone out and the practice of leadership as a potentiating art are the hallmarks of a generative leadership praxis.

The research posits that to be effective in the 21st century, leaders would do well to incorporate generative leadership qualities and characteristics into their praxis.

This study found that listening, co-creating connections and safe spaces, promoting dialog, critical reflection and collective action are as important to diplomatic tradecraft as they are to generative leadership practice.

The challenge of epistemic exclusion suggests that a well-conceived case study examining the life, leadership philosophy and praxis of Aurelia Erskine Brazeal – an individual of merit and distinction – can serve as an exemplar in efforts to reimagine public leadership in the 21st century.

The value of this research is found in its phenomenological approach which shares insights drawn from personal biography as well as key perspectives on public history.

In further conceptualizing a novel generative knowledge management system (KM/KMS), this paper aims to focus on identifying and mitigating the risks related to its envisaged scaling from a prototype to an application with a rapidly growing user base.

It follows up on prior publications using design science research (DSR) methodologies in compliance with theory effectiveness, a principle expecting system designs to be purposeful in terms of utility and communication. The KMS perspective taken prioritizes a decentralizing agenda benefiting knowledge workers while also aiming to foster a fruitful co-evolution with conventional organizational KM approaches.

The utilization and further extension of the CKDT and a “scalable innovation” heuristic are assisting the detecting of potential scaling risks related to the logics and logistics, generative interoperability, technological capacitating, knowledge dynamics and value chain which further validates the viability of the proposed KM concept and system.

Although the prototype development is still in progress, the paper conforms to the DSR practice to report on early visions of technology impact on users, organizations and society but also reflects on expectations of viability, desirability and commitment, as well as the system’s prospect as a general-purpose-technology or disruptive innovation.

In addition to the novel KM-related perspectives, the paper’s practical emphasis on the scaling of more complex systems is rarely dealt with in the literature due to the respective projects’ often large-scale collaborative nature, broad methodological scope and diverse stakeholders’ interests. In this case, the task is eased as prior DSR outputs can be referred to.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

The purpose of this study is to contribute to research concerning the role of digital infrastructure in digital government. This is done by answering the research question: how does digital infrastructuring constrain ambidexterity in public sector organizations?

The research is designed as a clinical inquiry in a large Swedish municipality, involving data collection in the form of interviews and internal documents. The method of analysis involves both exploring generative mechanisms in digital infrastructuring and theorizing on the findings based on previous literature.

The findings identify four generative mechanisms through which stability and change in digital infrastructuring constrain ambidexterity in terms of both efficiency (exploitation) and innovation (exploration).

This study’s limitations are related to international and intersectoral transferability and risks associated with its approach to clinical inquiry. The main implications are its contribution to the literature on how stability counteracts not only innovation but also efficiency and how change counteracts not only efficiency but also innovation.

This study identifies clear generative mechanisms that should be avoided by managers striving for digital government, and it offers clear recommendations for said managers regarding how to avoid them.

This study offers implications for national-level digital infrastructure policy and contributes to efforts to increase the capabilities of digital government.

As two of the four identified generative mechanisms are novel contributions, this study offers a concrete addition to existing research. This study has resulted in factual change in the studied organization as well as at the national level through successful dissemination of the findings for both policy and practice in other public sector organizations.