Purpose: The purpose of this paper is to contribute to a growing body of research on cloud computing, by studying the small to medium‐sized enterprise (SME) adoption process. If SMEs have access to scalable technologies they could potentially deliver products and services that in the past only large enterprises could deliver, flattening the competitive arena.Design/methodology/approach: By adopting the Technological, Organisational and Environmental (TOE) framework as a theoretical base, this qualitative exploratory study used semi‐structured interviews to collect data in 15 different SMEs and service providers in the north east of England. The north east of England was selected as it is a region that aspires to become home to innovative digital firms and most of the companies in the region are SMEs.Findings: The main factors that were identified as playing a significant role in SME adoption of cloud services were: relative advantage, uncertainty, geo‐restriction, compatibility, trialability, size, top management support, prior experience, innovativeness, industry, market scope, supplier efforts and external computing support. In contrast, this study did not find enough evidence that competitive pressure was a significant determinant of cloud computing adoption.Research limitations/implications: These findings have important implications and great value to the research community, managers and information and communication technologies (ICT) providers, in terms of formulating better strategies for cloud computing adoption. For service providers, using the research model in this study can assist in increasing their understanding of why some SMEs choose to adopt cloud computing services, while seemingly similar ones facing similar market conditions do not. Also, cloud computing providers may need to improve their interaction with SMEs which are involved in the cloud computing experience, in an effort to create a healthy environment for cloud computing adoption, and to remove any vagueness surrounding this type of technology.Originality/value: This study is an attempt to explore and develop an SME cloud computing adoption model that was theoretically grounded in the TOE framework. By adopting the TOE framework this study has shown that the three contexts of this framework (technological, organisational, and environmental) are connected to each other.

Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.

The development of emotion detection technology has emerged as an efficient possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of various start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparisons to social data. This study compares eight technologies: IBM Watson Natural Language Understanding, ParallelDots, Symanto – Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and Natural Language Processing Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores they delivered and the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.

With the increasing popularity of mobile learning in educational settings, the use of social networking sites (SNSs) as a tool for remote learning has become increasingly prevalent. However, the negative aspects of mobile learning through SNSs have not been extensively explored by scholars. Therefore, in this paper, we aim to investigate the impact of social overload, information overload, life invasion, and privacy invasion on students’ technostress and exhaustion and the resulting reduced intention to use SNSs for mobile learning. We also aim to investigate the impact of social overload, information overload, life invasion, and privacy invasion on students’ technostress and exhaustion and the resulting reduced intention to use SNSs for mobile learning. Data were collected through an online survey from 648 voluntary participants in Jordanian universities. The SOR framework provided a theoretical foundation for understanding the impact of SNSs on mobile learning. Through this study, we found information overload and life invasion were significantly related to higher levels of technostress and exhaustion among students. This suggests that, when students feel overwhelmed by excessive information or when their personal lives are invaded by SNS use, they experience increased technostress and exhaustion. Moreover, the study revealed that technostress and exhaustion were positively associated with a reduced intention to use SNSs for mobile learning. This indicates students who experience higher levels of technostress and exhaustion are less likely to engage with SNSs as a platform for their mobile learning needs. In contrast, we did not find a significant relationship between social overload and technostress, suggesting the amount of social interaction on SNSs may not directly contribute to students’ technostress levels. Additionally, we observed no significant relationship between privacy invasion and exhaustion, indicating privacy concerns may not directly contribute to students’ feelings of exhaustion in the context of mobile learning through SNSs. Regarding practical implications, we thus suggest the importance of establishing norms and rules to protect students’ privacy and prevent overburdening them with excessive SNS use. The SOR framework provides a theoretical foundation for understanding the impact of SNSs on mobile learning, and future researchers could benefit from its application.