Big Data has been listed as one of the current and future research frontiers by Gartner. Large-sized companies are already investing on and leveraging big data. Small-sized and medium-sized enterprises (SMEs) can also leverage big data to gain a strategic competitive advantage but are often limited by the lack of adequate financial resources to invest on the technology and manpower. Several big data challenges still exist especially in computer architecture that is CPU-heavy but I/O poor. Cloud computing eliminates the need to maintain expensive computing hardware and software. Cloud computing resources and techniques can be leveraged to address the traditional problems associated with fault tolerance and low performance causing bottlenecks to using big data. SMEs can take advantage of cloud computing techniques to avail the advantages of big data without significant investments in technology and manpower. This paper explores the current trends in the area of big data using cloud resources and how SMEs can take advantage of these technological trends. The results of this study will benefit SMEs in identifying and exploring possible opportunities and also understanding the challenges in leveraging big data.

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The authors explored how a technology model captured the factors that motivated and demotivated students to accept a new learning management system in online supply chain courses at an accredited public American university. Technology resistance is a well-known social science problem that results in reduced performance in business although it has rarely been examined in higher education. A new LMS is mandatory technology that is necessary to use in order to perform well in online global supply chain management courses. The authors drew a sample of graduating supply chain management students to explore this problem because these participants represent the next generation of employees who are likely to work with mandatory technology. The authors argue that it is important to study management students because their technology acceptance motivations will generalize to the future supply chain workforce. If decision makers could understand why management students resist new software, then they could develop strategies to address the critical success factors in hopes that organizational performance might be increased. The design was statistically powerful according to social research standards because the authors examined actual behavior by measuring grade as the dependent variable instead of relying on behavioral intent (BI) which is a subjective perceptional factor because participants are generally asked to self-report this through a survey. In keeping with published empirical literature the authors determined that perceived usefulness predicted BI using multiple regression. In contrary to the existing literature, they did not find that perceived resources (PR) was causally related to actual performance, although they did observe through regression that BI could be forecasted from PR. Their regression model indicated that perceived ease of use (PEOU) was not related to BI but in regression they found PEOU significantly impacted actual performance. These were two contradictory findings that PU impacted BI but not actual performance yet PR and PEOU predicted actual performance but not BI. Another unique departure from the empirical literature was that Computer Self Efficacy (CSE), Perceptions of External Control (PEC), and Computer Anxiety (CANX) were not related to either BI or actual performance. An interesting finding was that perceived enjoyment was strongly related to both BI and actual performance, although the perceptions were opposite between intention versus actual behavior. Multiple regression revealed that lower perceptions of enjoyment was significantly linked to BI while strong perceptions of enjoyment predicted actual performance. Although the authors were certain that peer influence would impact BI and actual performance, in their sample they did not find any support for subjective norm pressure on BI or actual performance. In a similar breakthrough they determined that job relevance and output quality were not causally linked to BI or actual performance. Another statistically significant finding was that positive perceptions of voluntariness were causally related to BI through multiple regression tests and also positively related to actual performance based on hierarchical regression. The authors determined that results demonstrability was causally related to BI from their multiple regression tests but interestingly it was not related to actual performance. The authors extensively discuss the above findings in their conclusions and provide many recommendations for future research. Finally, another valuable contribution the authors made to the scholarly community of practice through this study was to develop large effect-size parsimonious models with very few required factors that captured 56% of the variance on BI and 49% of the variance on actual performance.

The purpose of this study is to explore how small and medium enterprises in India has identified the opportunities and challenges in adopting the Web 3.0 technologies to improve their productivity and efficiency. After an in-depth literature review, researchers framed a semistructured questionnaire with open-ended questions for collecting responses from managers working in 40 Indian SME's representing five key economic sectors. The collected data was analyzed, and themes were encoded using the NVivo 11 computer-assisted qualitative data analysis software. Content analysis was used to analyze the data collected with the semi-structured interviews. This study identified five key themes and 12 subthemes illustrating the key advantages and challenges as perceived by the managerial leadership of SMEs. The five key themes identified in this study include integration of data and services, the creation of new functionalities, privacy and security, financial and technological challenges, and organizational challenges. The results of this study will benefit the organizational leadership of SMEs in planning and developing their short-term and long-term information systems strategies and will enable SME leaders to make optimal use of their information technology assets, improving the productivity and competitiveness of the firms. Web 3.0 technologies are considered as emerging technologies, so the advantages and challenges of using these technologies for SMEs have not been explored in the context of emerging economies, such as India.