Cloud-based ERP solutions offer many benefits to small and medium enterprises (SMEs) and help them to integrate their activities, such as improve communications and reduce operational and maintenance costs. Primarily, it was only adopted by large organizations, but now SMEs are also keen on adoption. However, the motivation regarding the adoption of these systems in SMEs is relatively low in developing countries. This fact urges us to investigate the challenges faced by Pakistani SMEs. A qualitative research approach along with unstructured interviews was conducted by means of face to face. Interview methods are used to extract understanding, opinions, and challenges faced by SMEs on their way to adopt the cloud-based ERP system. The data were collected from eight well-reputed organizations, directly involved in the adoption. The study found ten (10) themes that are reluctant to adopt cloud ERP among Pakistani SMEs. The main benefit of these themes is to provide results that can be easily accessible to enterprises who want to adopt a cloud-based ERP. This can also contribute to the lack of the literature of cloud ERP and delivers insight for future study by practitioners and researchers.
With the latest technology, smartphone's profound impact may be valuable for the users in different age groups, but the elders always face difficulties while adopting the technology. The usability of a smartphone application is essential when the target audience is elderly users, as the designer did not satisfy the specific requirements. The importance of smartphone application and the issues that the elders are facing in using smartphones have motivated us to provide a list of barriers that could negatively impact the usability of smartphone applications in elderly people. This research focused on identifying the barriers that affect the usability of smartphones, especially among elders. A systematic literature review (SLR) was used to identify and validate the barriers. After that, we apply the analytic hierarchy process (AHP) on identified barriers of all barriers’ groups to find out their relative importance. A total of fifteen barriers were identified through the SLR approach, and the barriers were then classified and assigned to one of the five categories. It is expected that the barriers that have been recognized will help the designers of smartphone applications in the early stages of designing applications. The result of the study will help in dealing with the issues related to the elder community and will make the designers develop smartphone applications accordingly.
A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation (AP), namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems.
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