Purpose: Generally, the use of cloud-based services has proliferated all around the world. This tendency increased dramatically during the coronavirus disease 2019 (COVID-19) pandemic when many people were obliged to conduct business remotely. This research study aims to investigate the intentions to adopt (IA) cloud-based enterprise resource planning (ERP) systems in Jordan, as this sort of research is scarce in developing countries. Specifically, this research study combined the model of Diffusion of Innovation (DOI) and the Technology-Organization-Environment (TOE) framework, in addition to looking at COVID-19 as a moderating variable.Design/methodology/approach: Data were collected from 60 participants working in Jordanian companies that currently use, or have the intention to use, cloud-based ERP systems. An online questionnaire was developed and used to collect responses. The study hypotheses were examined statistically using simple regression analysis and hierarchical multiple regression.Findings: The study results clearly indicate that complexity, observability, employee information technology (IT) skills, organizational culture (OC) and top management support significantly affect the intention to adopt cloud-based ERP systems. In contrast, relative advantage (RA), compatibility, trialability, regulatory environment and competitive pressure had no significant effect on adoption intention. Interestingly, the advent of COVID-19 increased the intention to adopt cloud-based ERP systems.Research limitations/implications: The study's findings will provide theoretical and practical suggestions for the effective adoption of cloud ERP in Jordan, as well as aiding other developing nations in comparable situations in implementing cloud-based ERP systems.Originality/value: The study enriches the knowledge and further examines the underlying reasons for adopting cloud-based ERP systems in a developing country.

Purpose: Despite its strong potential to optimize business processes, process mining remains largely unexplored in humanitarian operations. Therefore, this study aims to systematically examine the state of research across process mining in procurement and humanitarian procurement analytics and identify complementarities between the two streams. Design/methodology/approach: A scoping review methodology was applied to systematically analyze peer-reviewed studies published between January 2015 and August 2025. Following a structured search and screening procedure, 43 studies were analyzed across two themes: process mining in procurement and humanitarian procurement analytics. Findings: The study reveals a clear methodological divergence between the two themes. Process mining studies mainly provide descriptive and diagnostic insights using real event-log data, yet rarely extend to predictive or prescriptive analytics. In contrast, humanitarian procurement research advances prescriptive models but mainly relies on simulated or parameterized data. Predictive applications remain underdeveloped across both themes, while prescriptive models in humanitarian procurement are rarely validated using operational data. The comparison shows that process-mining capabilities, including compliance checking, bottleneck analysis, anomaly detection and process-variant analysis, could provide empirical evidence to complement existing decision-oriented models. Originality/value: The main contribution of this work is the identification of four cross-cutting themes: compliance and transparency, efficiency and performance, risk, resilience and sustainability and methodological robustness. By linking process-mining capabilities to specific humanitarian procurement challenges and identifying practical conditions for their application, the review provides a foundation for developing more integrated and empirically grounded approaches.