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Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator.

The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model.

The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented.

The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.

This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.

AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.

AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.

This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

The purpose of this paper is to examine the factors that influence enterprise resource planning (ERP) effectiveness within the context of emergency service organizations. Drawing on information systems (IS) effectiveness, ERP implementation and job satisfaction literature, the authors posit that user involvement, top management involvement and training satisfaction are the antecedents to perceived job satisfaction, and perceived job satisfaction leads to ERP effectiveness in emergency service organizations.

Survey methodology is used for collecting data for this research, and the PLS-SEM technique is used for analysis.

Results indicate users will be more satisfied with their training if their inputs are taken into account during their training and the top management is actively involved during the training process. Further, if the users perceive that they are adequately trained, they will be more satisfied with their jobs in using ERP, which will also lead to more effective ERP usage in emergency service operations.

The focus of this study is on a single emergency service organization and thus may not be generalizable to other sectors. The authors extend ERP research to the context of emergency service organizations and thus add to the literature on ERP and emergency services. They conceptualize perceived job satisfaction to integrate roles, teamwork, supervisor and their perception regarding their potential to grow in the organization.

The managerial contribution of this research is to identify the motivational aspects and provide practical insights into the effective use of ERP systems for emergency service organizations. From a managerial perspective, the study provides a framework for both IS and emergency service providers/executives to understand and evaluate the factors that help them use ERP effectively in their firms.

This study extends the knowledge of ERP systems. While most of the ERP research focuses on implementation, the authors’ focus is on the effective use of ERP in emergency service organizations. They focus on identifying key factors that are important to using ERP effectively, specifically in emergency service organizations.

Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance.

The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience.

The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance.

The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model.

The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.