This paper funded by Universiti Teknologi Malaysia (UTM) under the University Research grant number (Q.J130000.2528.18H56).

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

Enterprise resource planning (ERP) systems have a major impact on the functioning of organizations and the development of business strategy. However, one of the main reasons that cause failure in ERP implementations to achieve the expected benefits is that the system is not fully accepted by end users. User rejection of the system is the second reason after time and budget overrun, while the fourth barrier to ERP post-implementation. Most studies have focused on ERP adoption and installation while neglecting post-implementation evaluation, which omits insights into the priority of ERP systems and CSFs from the stance of ERP users. Therefore, this study identified factors that led to user acceptance of the use of ERP systems at both implementation and post-implementation stages (after installation). In addition, this study assessed the interrelationship between the factors and the most influential factors toward user acceptance. A survey was conducted among pioneers of the food industry in Saudi Arabia, which included 144 ERP system users from assembly and manufacturing, accounts, human resources, warehouse, and sales departments. The descriptive-analytical approach was deployed in this study. As a result, project management, top management support, and user training had significant impacts on the efficacy of ERP system implementation. On the contrary, support for technological changes in new software and hardware, managing changes in systems, procedures, and work steps already in place within the organization, as well as user interfaces and custom code, displayed a direct impact on user acceptance of ERP systems post-implementation. This study is the first research that provides a rating of CSFs from the perspective of its users in Saudi Arabia. It also enables decision makers of food industries to better assess the project risks, implement risk-mitigation methods, create appropriate intervention techniques to discover the strengths and limitations of the ERP users, and value the “best of fit” solutions over “best practice” solutions when determining the most appropriate option for food industries.

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users' needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.