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This study aims to ascertain the intentions of risk managers to use artificial intelligence in performing their tasks by examining the factors affecting their motivation.

The study employs an integrated theoretical framework that merges the third version of the technology acceptance model 3 (TAM3) and the unified theory of acceptance and use of technology (UTAUT) based on the application of the structural equation model with partial least squares structural equation modeling (PLS-SEM) estimation on data gathered through a Likert-based questionnaire disseminated among Italian risk managers. The survey reached 782 people working as risk professionals, but only 208 provided full responses. The final response rate was 26.59%.

The findings show that social influence, perception of external control and risk perception are the main predictors of risk professionals' intention to use artificial intelligence. Moreover, performance expectancy (PE) and effort expectancy (EE) of risk professionals in relation to technology implementation and use also appear to be reasonably reliable predictors.

Thus, the study offers a precious contribution to the debate on the impact of automation and disruptive technologies in the risk management domain. It complements extant studies by tapping into cultural issues surrounding risk management and focuses on the mostly overlooked dimension of individuals.

Yet, thanks to its quite novel theoretical approach; it also extends the field of studies on artificial intelligence acceptance by offering fresh insights into the perceptions of risk professionals and valuable practical and policymaking implications.

This paper aims to conceptualize supply market orientation (SMO) for the purchasing and supply chain management function and discusses how SMO capabilities are developed and how their application differs within and across firms. This research can thus be used as a blueprint for the development of a SMO capability that accommodates a firm’s unique contextual antecedents’ profile.

The qualitative research design comprises five in-depth case studies with 43 semi-structured interviews with large manufacturing and service firms.

SMO is defined as the capability to exploit market intelligence to assess, integrate and reconfigure the heterogeneously dispersed resources in purchasing and supply chain management in a way that best reflects the peculiarities of a firm’s supply environment. The empirical analysis shows that although SMO capabilities are configured similarly, their application varies across and within firms depending on the characteristics of a firm’s purchasing categories and tasks. Hence, reactive versus proactive SMO application is contingent upon firm-level and purchasing category–level characteristics.

The study uses the dynamic capabilities view as a theoretical background and provides empirical evidence and theoretical reasoning to elaborate and endorse SMO as a dynamic capability that firms need to have to compete in a complex and dynamic environment. The study provides guidance for supply chain managers on how to successfully develop and deploy a SMO capability.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users can be made more accountable.

Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals.

AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI.

This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.