Journal of Organizational and End User Computing

Journal Information
ISSN / EISSN : 1546-2234 / 1546-5012
Published by: IGI Global (10.4018)
Total articles ≅ 568
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Latest articles in this journal

Sathiyamoorthi V., Keerthika P., Suresh P., Zuopeng (Justin) Zhang, Adiraju Prasanth Rao, Logeswaran K.
Journal of Organizational and End User Computing, Volume 33, pp 135-152; https://doi.org/10.4018/joeuc.20210901.oa7

Abstract:
Cloud computing is an optimistic technology that leverages the computing resources to offer globally better and more efficient services than the collection of individual use of internet resources. Due to the heterogeneous and high dynamic nature of resources, failure during resource allocation is a key risk in cloud. Such resource failures lead to delay in tasks execution and have adverse impacts in achieving quality of service (QoS). This paper proposes an effective and adaptive fault tolerant scheduling approach in an effort to facilitate error free task scheduling. The proposed method considers the most impactful parameters such as failure rate and current workload of the resources for optimal QoS. The suggested approach is validated using the CloudSim toolkit based on the commonly used metrics including the resource utilization, average execution time, makespan, throughput, and success rate. Empirical results prove that the suggested approach is more efficient than the benchmark techniques in terms of load balancing and fault tolerance.
Eran Rubin, Frederik Beuk
Journal of Organizational and End User Computing, Volume 33, pp 1-24; https://doi.org/10.4018/joeuc.20210901.oa1

Abstract:
A growing body of literature supports the notion that the well-being of individuals is influenced by their social networks site (SNS) experiences. In this research, the authors analyze the effect of such SNS experience perceptions, termed social networks affective well-being (SNAWB) on behavior in non-SNS sites. Specifically, the authors ask if the visual interface design of a non-SNS site affects the level to which the decisions made in that site are influenced by the decision maker's SNAWB. Relating to theory on emotion and action readiness, this research hypothesizes on the expected effects of a visual interface design that includes elements that may trigger SNS-related emotions. To test the hypothesis, this paper conducts two experiments: 1) an online experiment and 2) a controlled lab experiment with eye-tracking. The results show that individuals' decisions are affected by the level to which the website interface design may trigger SNS emotions. The results further provide evidence on the emotional process leading to different effects according to the type of decision made.
Guanghai Tang, Hui Zeng
Journal of Organizational and End User Computing, Volume 33, pp 25-41; https://doi.org/10.4018/joeuc.20210901.oa2

Abstract:
According to the UNWTO, within 4 to 5 years, the proportion of tourism e-commerce in e-commerce will reach 20%-25%. The purpose of this paper is to improve the inadequacy of tourism e-commerce in customer experience, to conduct customer e-commerce satisfaction surveys, and to draw customers' dissatisfaction with tourism e-commerce. The experimental results show that the overall customer satisfaction is 2.6128. According to the division of the scale vector, the overall satisfaction of the travel e-commerce customers is generally level. The first-level fuzzy comprehensive evaluation is 0.0967, 0.1696, 0.3366, 0.2469, 0.502. According to the principle of maximum membership degree, the evaluation grades of R3 and R5 in the first-level fuzzy comprehensive evaluation are “unsatisfactory,” that is, the tourism-supporting services and contract-performance services become the main factors affecting customer satisfaction. In order to improve customer satisfaction, the tourism e-commerce platform should strengthen the management of tourism-supporting services and contract-fulfillment services.
Mohammad Kamel Daradkeh
Journal of Organizational and End User Computing, Volume 33, pp 42-73; https://doi.org/10.4018/joeuc.20210901.oa3

Abstract:
With the proliferation of big data and business analytics practices, data storytelling has gained increasing importance as an effective means for communicating analytical insights to the target audience to support decision-making and improve business performance. However, there is a limited empirical understanding of the relationship between data storytelling competency, decision-making quality, and business performance. Drawing on the resource-based view (RBV), this study develops and validates the concept of data storytelling competency as a multidimensional construct consisting of data quality, story quality, storytelling tool quality, storyteller skills, and storyteller domain knowledge. It also develops a mediation model to examine the relationship between data storytelling competency and business performance, and whether this relationship is mediated by decision-making quality. Based on an empirical analysis of data collected from business analytics practitioners, the results of this study reveal that the data storytelling competency is positively linked to business performance, which is partially mediated by decision-making quality. These results provide a theoretical basis for further investigation of possible antecedents and consequences of data storytelling competency. They also offer guidance for practitioners on how to leverage data storytelling capabilities in business analytics practices to improve decision-making and business performance.
Wei Li, Li Zhang, Changgen Han
Journal of Organizational and End User Computing, Volume 33, pp 98-110; https://doi.org/10.4018/joeuc.20210901.oa5

Abstract:
With the rapid development of e-commerce and the increasing popularity of smartphones, online shopping has become a trend of the times, and various online shopping platforms have emerged endlessly. As a new type of shopping platform, the micro store has gradually occupied a place in online shopping with its unique characteristics. In this context, the dissertation first introduces the management of micro-store operation models; then, it summarizes the causes of micro-store operation model risks into seven aspects.
Nithiya Baskaran, Eswari R.
Journal of Organizational and End User Computing, Volume 33, pp 153-179; https://doi.org/10.4018/joeuc.20210901.oa8

Abstract:
A cloud data center is established to meet the storage demand due to the rate of growth of data. The inefficient use of resources causes an enormous amount of power consumption in data centers. In this paper, a fuzzy soft set-based virtual machine (FSS_VM) consolidation algorithm is proposed to address this problem. The algorithm uses four thresholds to detect overloaded hosts and applies fuzzy soft set approach to select appropriate VM for migration. It considers all factors: CPU utilization, memory usage, RAM usage, and correlation values. The algorithm is experimentally tested for 11 different combinations of choice parameters where each combination is considered as fuzzy soft set and compared with existing algorithms for various metrics. The experimental results show that proposed FSS_VM algorithm achieves significant improvement in optimizing the objectives such as power consumption, service level agreement violation rate, and VM migrations compared to all existing algorithms. Moreover, performance comparison among the fuzzy soft set-based VM selection methods are made, and Pareto-optimal fuzzy soft sets are identified. The results show that the Pareto-based VM selection improves the QoS. The time complexity of the proposed algorithm increases when it finds best VM for migration. The future work will reduce the time complexity and will concentrate on developing an efficient VM placement strategy for VM migration since it has the greater impact on improving QoS in VM placement.
Ying Hong, Meng Wan, Zheng Li
Journal of Organizational and End User Computing, Volume 33, pp 180-203; https://doi.org/10.4018/joeuc.20210901.oa9

Abstract:
Studies have focused on elucidating the sharing behavior of media users. However, few studies have specifically investigated users' health information sharing behavior in the social media context, especially WeChat. This study proposes a theoretical research model that integrates social capital and user gratification with the theory of planned behavior to explore health information sharing behavior of WeChat users. Based on online survey data collected from 616 WeChat users, correlation analysis and structural equation modeling were sequentially performed. It was found that both social capital and gratification factors play important roles in influencing WeChat users' health information sharing. Social interaction, acting both as social capital and gratification factor directly and indirectly generated positive effects on health information sharing intention. In conclusion, this study revealed the key determinants of health information sharing intention among WeChat users and examined the mediation effects to effectively understand users' health information sharing behavior.
, Zuopeng (Justin) Zhang, Prajwal Eachempati
Journal of Organizational and End User Computing, Volume 33, pp 204-226; https://doi.org/10.4018/joeuc.20210901.oa10

Abstract:
The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.
Mei-Lan Li, Shu-Ping Lin, Ya-Hui Chan, Chia-Huei Wu
Journal of Organizational and End User Computing, Volume 33, pp 74-97; https://doi.org/10.4018/joeuc.20210901.oa4

Abstract:
In this study, the authors extended the perceived risk-value model to include customer involvement to conceptualize an adoption intention model in the context of internet-only bank services (IOBSs). Hypotheses were tested using survey data collected in China. A total of 252 valid questionnaires were returned. Structural equation modeling was used to test two models, an antecedent model, and moderating model, constructed by assuming that customer involvement affects the perceived risk-value model in different ways. The findings verified that the perceived value could explain customers' intention to adopt IOBSs, whereas the influences of perceived risk were discovered to be nonsignificant, reducing the fitness of the perceived risk-value model. However, the opposite result was obtained when customer involvement was considered to exert a moderating effect rather than an antecedent effect. The implications of this research for IOBS service operators are discussed, and suggestions for future research are provided.
Journal of Organizational and End User Computing, Volume 33, pp 111-134; https://doi.org/10.4018/joeuc.20210901.oa6

Abstract:
A mobile ad hoc network (MANET) has several intrinsic features that create unique queuing dynamics, and thus congestion control inside a MANET must be achieved under time-critical conditions. Meanwhile, the Named Data Networking (NDN) architecture ensures traffic optimization and has attracted renewed attention as part of the future internet. The synergy between NDN and MANETs can be exploited in order to improve the performance of dynamic content routing and congestion control mechanisms. This overview identifies the key concepts involved in congestion control for NDN-based MANETs. It also proposes some criteria for categorising existing congestion control solutions for NDN-based MANETs and discusses the advantages and disadvantages of each category. Future challenges regarding congestion control for NDN-based MANETs are also highlighted.
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