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Predictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.

This study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.

There are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.

This paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into the decision-making process in emergency scenarios to help mitigate the high levels of complexity and uncertainty associated with these situations. An AI solution is designed to extract insights into opioid overdose (OD) that can help government agencies to improve their medical emergency response and reduce opioid-related deaths.

This paper employs the design science research paradigm as an overarching framework. Open-access digital data and AI, two essential components within the digital transformation domain, are used to accurately predict OD survival rates.

The proposed AI solution has two primary implications for the advancement of informed emergency management. Results show that it can help not only local agencies plan their resources for timely response to OD incidents, thus improving survival rates, but also governments to identify geographical areas with lower survival rates and their primary contributing factor; hence, they can plan and allocate long-term resources to increase survival rates and help in developing effective emergency-related policies.

This paper illustrates that digital transformation, particularly open-access digital data and AI, can improve the emergency management framework (EMF). It also demonstrates that the AI models developed in this study can identify opioid OD trends and determine the significant factors improving survival rates.

This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.