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Results in Journal Journal of Organizational and End User Computing: 527

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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.
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.
, 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.
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.
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.
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.
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.
Xue Yang, Haowen Li, Likun Ni, Teng Li
Journal of Organizational and End User Computing, Volume 33, pp 209-219; https://doi.org/10.4018/joeuc.20210701.oa10

Abstract:
The development of artificial intelligence technology has greatly helped social productivity and economic growth. At the same time, we have changed modern marketing methods, provided technical assistance for precision marketing, improved modern marketing efficiency, and effectively reduced marketing costs. Compared to traditional marketing, artificial intelligence technology is applied to accurate marketing activities. It will make the marketing effect more accurate and personalized. Faced with these technological advances, it is important to study the application of artificial intelligence technology for the precise new marketing model. The advancement of artificial intelligence technology not only changed the way of marketing activities, but also enabled marketers to attract consumers more effectively. The enormous amount of data provides new opportunities and challenges for marketers. AI technology can accurately identify customer needs in a huge database to locate potential customers, meet customer needs, and establish a good relationship between marketers and consumers.
Asvija B., Eswari R., Bijoy M. B.
Journal of Organizational and End User Computing, Volume 33, pp 44-69; https://doi.org/10.4018/joeuc.20210701.oa3

Abstract:
Designing security mechanisms for cloud computing infrastructures has assumed importance with the widespread adoption of public clouds. Virtualization security is a crucial component of the overall cloud infrastructure security. In this article, the authors employ the concept of Bayesian networks and attack graphs to carry out sensitivity analysis on the different components involved in virtualization security for infrastructure as a service (IaaS) cloud infrastructures. They evaluate the Bayesian attack graph (BAG) for the IaaS model to reveal the sensitive regions and thus help the administrators to secure the high risk components in the stack. They present a formal definition of the sensitivity analysis and then evaluate using the BAG model for IaaS stack. The model and analysis presented here can also be used by security analysts and designers to make a selection of the security solutions based on the risk profile of vulnerable nodes and the corresponding cost involved in adding a defense against the identified vulnerabilities.
Francisco J. Martínez-López, Yangchun Li, Changyuan Feng, David López-López
Journal of Organizational and End User Computing, Volume 33, pp 70-93; https://doi.org/10.4018/joeuc.20210701.oa4

Abstract:
Social platforms are currently encountering a set of burning issues: low ad conversion rates, cross-channel free-riding phenomena, lack of monetary incentives to retain premium content creators, etc. Direct purchase behaviors between social platform users (e.g., making a direct purchase through a seller's promotional post) can largely resolve these problems. Therefore, it is imperative to study the factors that influence users' direct purchase behavior. This paper focuses on risk- and trust-related factors, proposing a theoretical model that was tested on two samples of Chinese users of WeChat. The authors concluded that users tend to evaluate the shopping risk associated with the social platform first, then go through a process of building trust in the platform before making purchases. Further, this trust can generate a halo effect on seller risk. Finally, trust and seller risk directly impact on users' purchase intention to buy from the seller on the platform.
Journal of Organizational and End User Computing, Volume 33, pp 125-141; https://doi.org/10.4018/joeuc.20210701.oa6

Abstract:
In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.
, Garry White
Journal of Organizational and End User Computing, Volume 33, pp 185-208; https://doi.org/10.4018/joeuc.20210701.oa9

Abstract:
Organizations expect their employees to connect securely to the organization's computer systems. Often these employees use their personal computers to access the organization's networks. This research explores whether these same employees apply protective security measures to their personal computers. Perhaps these employees behave riskily based on their optimistic bias. Results indicate that while cyber optimistic bias and perceived vulnerability influence individuals to apply more protective security measures, the users still experienced security incidents. Thus, organization are vulnerable to cyber-attacks if they are allowing employees to use personal computers to access these databases.
Robert Zinko, Helene de Burgh-Woodman, Zhan Zhang Furner, Soo Jung Kim
Journal of Organizational and End User Computing, Volume 33, pp 85-104; https://doi.org/10.4018/joeuc.20210301.oa5

Abstract:
Images are frequently used in online reviews, yet little research explores the effects that images have on online consumer behavior. This two-study investigation examines the effects of images in electronic word of mouth (eWOM) for both hedonic and utilitarian products. Results show that images affect the relationship between review text and purchase intention as well as trust for both product categories. However, images were shown to be more effective for hedonic than utilitarian products. Interestingly, it was found that congruence between the image and text is not a significant predictor of trust or purchase intention in some conditions (i.e., the images may not have to perfectly reflect the text to facilitate these outcomes for utilitarian products).
, SoYean Kim, , Donghee Shin
Journal of Organizational and End User Computing, Volume 33, pp 1-27; https://doi.org/10.4018/joeuc.20210701.oa1

Abstract:
Within the technology acceptance literature, the issue of top management support and commitment has been studied extensively; however, the issue of leadership per se has not been addressed directly. A missing piece of the leadership puzzle as it relates to technology acceptance is an exploration of how top management support gets translated in the organizational hierarchy. This study introduces leader-member exchange (LMX) to better understand this missing piece. Specifically, this research explores the role direct supervisors play in the acceptance process by end users based on the moderated model of LMX and supervisor influence. The empirical test results in the field setting show that LMX is a significant moderator for most of the technology acceptance variables within organizations. The study explores the role of the quality of the relationship between supervisors and employees as end users. It also highlights the role of LMX and supervisor influence as a conduit for the acceptance process among end users in the organization.
Jaeheung Yoo, Saesol Choi, Yujong Hwang,
Journal of Organizational and End User Computing, Volume 33, pp 36-58; https://doi.org/10.4018/joeuc.20210301.oa3

Abstract:
This study examines the factors affecting users' adoption of the smartphone as an innovative device. Prior studies on the acceptance of computing devices have primarily focused on the impact of the technological benefits and characteristics. Meanwhile, there is a lack of research approaching user resistance, which hinders the diffusion of an innovation. In particular, the smartphone is a highly communication-oriented device that people's attitude and evaluation critically influence its further diffusion. However, few studies have validated this link in the smartphone adoption context. Therefore, this study has attempted to build a research model that explains factors affecting user's resistance to smartphone adoption by integrating technological and social antecedents forming the resistance, and empirically analyzes the data obtained through a survey. As a result, the relative complexity and relative advantages presented in the theory of innovation diffusion had a direct impact on the user's resistance.
Fanchao Meng, Qingran Ji, Hongzhen Zheng, Huihui Wang, Dianhui Chu
Journal of Organizational and End User Computing, Volume 33, pp 142-166; https://doi.org/10.4018/joeuc.20210701.oa7

Abstract:
The rapid development of e-commerce has led to increased pressure on the express delivery industry to transport products to customers in a timely manner. The problem of how to deliver an increasing volume of express orders to customer clusters in a timely manner and at low cost with the joint distribution mode is becoming urgent. In this study, an express terminal node optimization and integration model is presented with an option to detach single customer clusters. In addition, the simulated annealing algorithm (SAA) based on neighborhood search that includes four rules is proposed to solve the problem. Contrast experiments are performed with SAA, the immune genetic algorithm (IGA), and the CPLEX solver. The experimental results indicate that IGA is less effective than SAA, and the running time of the IGA is longer. The CPLEX solver is less effective than the SAA, too. Additionally, the experimental results also show that every neighborhood rule proposed in this study plays a role in the optimization process.
, Chia-Huei Wu, Dajiang Wang, Shiyun Yao, Yingying Feng
Journal of Organizational and End User Computing, Volume 33, pp 167-184; https://doi.org/10.4018/joeuc.20210701.oa8

Abstract:
This study uses a content analysis method to systematically review 83 research papers from 2002-2018 to explore consumer-to-consumer (C2C) e-commerce research trends. The findings of this study indicate that (1) C2C e-commerce is discussed and investigated in many disciplines, but mainly published in e-commerce journals; (2) studies on C2C e-commerce increasingly focus on diverse topics, but concentrate on regions such as China and the United States; (3) the focus of academic collaboration has shifted from domestic to international collaboration, and collaboration within the same institution. However, collaboration is scarce across different study teams; (4) the data-driven approach is the main approach used in studies on C2C e-commerce; (5) while the number of recent C2C e-commerce studies adopted theories is increasing, few have developed theoretical frameworks or models. Finally, study implications and future study suggestions are also discussed.
Joel Stephan Tagne, Paul Ningaye, Georges Kobou
Journal of Organizational and End User Computing, Volume 33, pp 28-43; https://doi.org/10.4018/joeuc.20210701.oa2

Abstract:
The objective of this study was to analyze the effects of openness on the adoption of managerial innovation by Cameroonian companies, as well as comparing the share of managerial innovation resulting from inter-organizational networks of the same group and of different groups. Noting a lack of such a study on Cameroon, this study used data from the Centre de Recherche en Economie et Gestion (CEREG) to achieve the objective. Using a binary probit model and a recursive bivariate probit model, the authors found that, first, a company that collaborates with other companies has an increased probability of 0.37 of adopting new managerial practices, compared to another company that does not collaborate. Second, a company belonging to a group that collaborates with companies of a different group has an increased probability of 0.30 of adopting new managerial practices, compared to a company that only collaborates with companies of the group to which she belongs. Business leaders should cooperate with all market players.
, , Radu Lixandroiu, Lavinia Dovleac, Maria Anca Maican
Journal of Organizational and End User Computing, Volume 33, pp 94-124; https://doi.org/10.4018/joeuc.20210701.oa5

Abstract:
Over the recent years, online communication and collaboration applications have been extensively used in teaching, learning, and research. The aim of the present study is to evaluate the attitudes and perceptions of higher education students concerning these applications and their use in learning activities. The research was conducted on 748 Romanian students. The findings have revealed that social networks represent the main category of communication and collaboration applications used for learning purposes. Additionally, gender-induced differences have been found, as well as variances among undergraduates and graduates by several dimensions of the unified technology acceptance model, while students' learning engagement proved to mediate the relationship between personality traits, technology self-efficacy, and the use of collaboration tools. The paper also offers a comparative analysis between the students' and the academics' responses to the use of online communication and collaboration applications in the same cultural and organizational context. Practical implications are also presented.
Guofu Li, Ning Cao, Pengjia Zhu, Yanwu Zhang, Yingying Zhang, Lei Li, Qingyuan Li, Yu Zhang
Journal of Organizational and End User Computing, Volume 33, pp 35-49; https://doi.org/10.4018/joeuc.20210501.oa3

Abstract:
Smart transportation system is a cross-field research topic that involves both the organizations that manage the large-scaled system and individual end-users who enjoy these services. Recent advancement of machine learning-based algorithms has either enabled or improved a wide range of applications due to its strength in making accurate predictions for complex problems with a minimal amount of domain knowledge and great ability of generalization. These nice properties imply potential to be explored for building smart transportation system. This paper studies how deep reinforcement learning (DRL) can be used to optimize the operating policy in modern bike sharing systems. As a case study, the authors demonstrate the potential power of the modern DRL by showing a policy-gradient-based reinforcement learning approach to the rebalancing problem in a bike sharing system, which can simultaneously improve both the user experience and reduce the operational expense.
Baili Zhang, Kejie Wen, Jianhua Lu, Mingjun Zhong
Journal of Organizational and End User Computing, Volume 33, pp 50-68; https://doi.org/10.4018/joeuc.20210501.oa4

Abstract:
With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service composition. However, current service composition approaches applied in the internet cannot work well in IoT environments due to weak adaptability, low accuracy, and poor time performance. This paper, based on service dependency graph, proposes a top-k QoS-optimal service composition approach suitable for IoT. It aims to construct the relationship between services by applying the service dependency model and to reduce the traversal space through effective filtering strategies. On the basis of a composition path traversal sequence, the generated service composition can be represented directly to avoid backtracking search. Meanwhile, the redundant services can be removed from the service composition with the help of dynamic programming. Experiments show that the approach can obtain the top-k QoS-optimal service composition and better time performance.
Ahmad A.M. Alwreikat, Husam Rjoub
Journal of Organizational and End User Computing, Volume 33, pp 69-84; https://doi.org/10.4018/joeuc.20210501.oa5

Abstract:
Mobile and smart devices provide a platform for firms/brands to communicate directly with past, present, or potential consumers (via online pop-ups, sponsored ads, ads on social media messengers, timelines, walls, etc.). Existing research on human-mobile interaction and end-user mobile management only highlights the positive fronts of repetitive exposures to mobile ads, ignoring the negative. The present study examines the effects of mobile ad wearout on irritation, intrusiveness, engagement, and loyalty via social media outlets. Survey data were solicited from consumers in Jordan, and the partial least squares structural equation modeling (PLS-SEM) technique was applied. Results show that mobile ad wearout is a strong determinant for increased consumer irritation and perceived intrusiveness. Intrusiveness resulted in lower levels of consumer engagement and higher levels of consumer loyalty. Irritation resulted in lower levels of consumer engagement; no changes were observed in consumer loyalty. This study shows managers how mobile ad wearout causes irritation and intrusiveness, which diminishes consumer engagement and loyalty. In essence, managers can gain insights on the positive and negative outcomes of mobile ad wearout. Implications for theory and practice are discussed.
Mengxia Shuai, Nenghai Yu, Hongxia Wang, Ling Xiong, Yue Li
Journal of Organizational and End User Computing, Volume 33, pp 1-18; https://doi.org/10.4018/joeuc.20210501.oa1

Abstract:
Security and privacy issues in wireless medical sensor networks (WMSNs) have attracted lots of attention in both academia and industry due to the sensitiveness of medical system. In the past decade, extensive research has been carried out on these security issues, but no single study exists that addresses them adequately, especially for some important security properties, such as user anonymity and forward secrecy. As a step towards this direction, in this paper, the authors propose a lightweight three-factor anonymous authentication scheme with forward secrecy for personalized healthcare applications using only the lightweight cryptographic primitives. The proposed scheme adopts pseudonym identity technique to protect users' real identities and employs one-way hash chain technique to ensure forward secrecy. Analysis and comparison results demonstrate that the proposed scheme can not only reduce execution time by 34% as compared with the most effective related schemes, but also achieve more security and functional features.
Dan Yang, Zheng Tie Nie, Fajun Yang
Journal of Organizational and End User Computing, Volume 33, pp 19-34; https://doi.org/10.4018/joeuc.20210501.oa2

Abstract:
Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.
Carmen Camarero, Rebeca San José, Nadia Jiménez, Sonia San-Martín
Journal of Organizational and End User Computing, Volume 33, pp 59-84; https://doi.org/10.4018/joeuc.20210301.oa4

Abstract:
Even though the mobile games industry has grown substantially over the last few years, one permanent challenge which remains is to monetize it and to continue reaching new players. Current players contribute to this aim by purchasing mobile game accessories and by recruiting players. The current work analyses how the present use of the game and its appeal contribute to these behaviors. Results with information obtained from a sample of app gamers show that using a game app can have positive effects on recruitment, a notion reinforced when the level of performance rises, while overuse of the game app may lead to a feeling of addiction or shame that reduces new player recruitment. In addition, the game's perceived appeal (experiential value, procedural justice, and prestige) are also related with investing in new products and with recruitment.
Sridharan R., Domnic S.
Journal of Organizational and End User Computing, Volume 33, pp 17-35; https://doi.org/10.4018/joeuc.20210301.oa2

Abstract:
Due to pay-as-you-go style adopted by cloud datacenters (DC), modern day applications having intercommunicating tasks depend on DC for their computing power. Due to unpredictability of rate at which data arrives for immediate processing, application performance depends on autoscaling service of DC. Normal VM placement schemes place these tasks arbitrarily onto different physical machines (PM) leading to unwanted network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates autoscaling and intercommunication aware task placements (AIATP) as an optimization problem, with additional constraints and proposes solution, which uses the placement knowledge of prior tasks of individual applications. When compared with well-known algorithms, CloudsimPlus-based simulation demonstrates that AIATP reduces the resource fragmentation (30%) and increases the resource utilization (18%) leading to minimal number of active PMs. AIATP places 90% tasks of an application together and thus reduces the number of VM migration (39%) while balancing the PMs.
Brij B. Gupta, Shaifali Narayan
Journal of Organizational and End User Computing, Volume 33, pp 1-16; https://doi.org/10.4018/joeuc.20210301.oa1

Abstract:
This paper presents a framework for mutual authentication between a user device and a point of sale (POS) machine using magnetic secure transmission (MST) to prevent the wormhole attack in Samsung pay. The primary attribute of this method is authenticating the POS terminals by an authentication server to bind the generated token to a single POS machine. To secure the system from eavesdropping attack, the data transmitted between the user device and the machine is encrypted by using the Elgamal encryption method. The keys used in the method are dynamic in nature. Furthermore, comparison and security analysis are presented with previously proposed systems.
Yu-Qian Zhu, Bo Hsiao
Journal of Organizational and End User Computing, Volume 33, pp 71-91; https://doi.org/10.4018/joeuc.2021010104

Abstract:
Although business and researchers acknowledge the importance of social media, little research has been conducted to explore what attracts people to follow brand Twitter accounts. This research attempts to achieve an analytical understanding of the factors that contribute to brand Twitter follower count based on social network and communication theories. Using data from 346 Twitter accounts spanning 48 industries and 31 countries, the authors found that the quality and quantity of tweets, as well as social learning of brand Twitter accounts are positively related to brand Twitter account followers; contrary to popular belief, the use of hashtags and links and interactivity with users are not positively related to brand Twitter account followers. The study is among the first to investigate what attracts brand Twitter account followers, which offers important strategic recommendations for brand social media managers on how to manage their social media accounts.
Vandana Roy, Prashant Kumar Shukla, Amit Kumar Gupta, Vikas Goel, Piyush Kumar Shukla, Shailja Shukla
Journal of Organizational and End User Computing, Volume 33, pp 19-46; https://doi.org/10.4018/joeuc.2021010102

Abstract:
Electroencephalogram (EEG) signals are progressively growing data widely known as biomedical big data, which is applied in biomedical and healthcare research. The measurement and processing of EEG signal result in the probability of signal contamination through artifacts which can obstruct the important features and information quality existing in the signal. To diagnose the human neurological diseases like epilepsy, tumors, and problems associated with trauma, these artifacts must be properly pruned assuring that there is no loss of the main attributes of EEG signals. In this paper, the latest and updated information in terms of important key features are arranged and tabulated extensively by considering the 60 published technical research papers based on EEG artifact removal method. Moreover, the paper is a review vision about the works in the area of EEG applied to healthcare and summarizes the challenges, research gaps, and opportunities to improve the EEG big data artifacts removal more precisely.
Wenqing Wu, Saixiang Ma, Yuzheng Su, Chia-Huei Wu
Journal of Organizational and End User Computing, Volume 33, pp 92-117; https://doi.org/10.4018/joeuc.2021010105

Abstract:
This paper constructs an online community organizational double-layer learning structure model based on exploration-exploitation models. In this way, the authors examine the effect how double-layer online community learning as well as heterogeneous teams affects online work community organizational knowledge performance (OWCOKP) with leaders forgetting and without leaders forgetting. First, the results suggest an inverted-U relationship between the degree of different team member connectivity and OWCOKP. Second, as the leaders forgetting rate increases, the degree of different team member connectivity, which leads to the optimum OWCOKP also increases. Third, with or without leaders forgetting, moderate learning between members and that between the leader and members can improve OWCOKP within a team of online community. Fourth, in different teams, slow learning between leaders produces higher OWCOKP without leaders forgetting while moderate learning between leaders produces higher OWCOKP with their forgetting.
Min Feng
Journal of Organizational and End User Computing, Volume 33, pp 1-18; https://doi.org/10.4018/joeuc.2021010101

Abstract:
The ubiquitous use of ICT can create “techno-stress.” The purpose of the research is to examine the case of the specificity of the techno-stress phenomenon of local managers. The authors develop their research questions on the factors that create the techno-stress and the role stress of the proximity manager. How do the creators of techno-stress influence the performance of the proximity manager? Techno-stress creative factors of the managers have been adjusted thanks to the factorial analysis. The authors believe that 1) the role stress of local managers can be explored by ambiguity and role proximity, and 2) the creator of techno-stress negatively influences the performance of the managers of proximity by role stress.
Santhoshkumar Srinivasan, Dhinesh Babu L. D.
Journal of Organizational and End User Computing, Volume 33, pp 47-70; https://doi.org/10.4018/joeuc.2021010103

Abstract:
Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.
Mamta, Brij B. Gupta
Journal of Organizational and End User Computing, Volume 32, pp 112-137; https://doi.org/10.4018/joeuc.2020100106

Abstract:
Fine-grained searching is an important feature in multi-user cloud environment and a combination of attribute-based encryption (ABE) and searchable encryption (SE) is used to facilitate it. This combination provides a powerful tool where multiple data owners can share their data with multiple data users in an independent and differential manner. In this article, the authors have used key-policy design framework of attribute-based encryption to construct the multi-keyword search scheme where access rights assigned to a data user are associated with his/her secret key. This leads to a situation where a data user can abuse his secret key to distribute it illegally to the unauthorized users to perform search over the shared data which is not intended for him/her. Therefore, to track such kind of key abusers the authors have embedded an extra functionality of tracing the traitors. For this purpose, each user is assigned a unique identity in the form of binary string where each bit represents an attribute related to his identity. In addition to the normal attributes, the access structure of a user also possesses identity-related attributes which are hidden from the user along with some normal attributes. Hence, the proposed scheme supports partial anonymity. Further, in the event of user revocation the proposed scheme efficiently handles the system update process by delegating the computationally intensive tasks to the cloud server. Finally, the proposed scheme is proved secure under Decisional Bilinear Diffie-Hellman (DBDH) assumption and decision linear assumption in the selective security model.
Xiaofeng Chen, Keng Siau
Journal of Organizational and End User Computing, Volume 32, pp 138-161; https://doi.org/10.4018/joeuc.2020100107

Abstract:
This is an empirical research investigating the impact of business analytics (BA) and business intelligence (BI) use, IT infrastructure flexibility, and their interactions on organizational agility. Synthesizing the systems theory and awareness-motivation-capability framework, the authors propose that BA-Use, IT infrastructure flexibility, and their interactions significantly influence organizational agility. The results show the significant association of BA use and IT infrastructure flexibility with organizational agility. The results also suggest that BA use may demand corporations to build a more flexible IT infrastructure. However, the data does not reveal the proposed interaction between the two drivers of organizational agility.
Journal of Organizational and End User Computing, Volume 32, pp 1-25; https://doi.org/10.4018/joeuc.2020100101

Abstract:
This research studied user repurchase intentions on online group buying services. In the research model, satisfaction is hypothesized to have a positive relationship with trust that will have a positive impact on repurchase intention. Each of the three dimensions is divided into two constructs, one for the product and the other for the website. In addition, the moderating effects of social identification on the relationships between trust and repurchase intention, and between satisfaction and trust were tested. A survey collected 300 effective samples to test the research model. The results of PLS showed that all of the causal relationships were all significant while the moderating effects of social identification were significant for several relationships. When social identification is low, the influences of trust toward the product on repurchase intention for the product, satisfaction toward the product on trust toward the product, and satisfaction toward the website on trust toward the website, are stronger. Managerial implications and suggestions for future research were also discussed.
John R. Drake,
Journal of Organizational and End User Computing, Volume 32, pp 63-84; https://doi.org/10.4018/joeuc.2020100104

Abstract:
With the proliferation of social media, job candidate screening and evaluation professionals have new avenues to gather information regarding job candidates. Job candidates recognize that recruiters will examine their social media, and tailor their profiles to foster a positive impression. However, recent popular press news suggests that some employers are taking social media screening to more invasive levels. This study seeks to evaluate how job candidates respond to social media screening from recruiters. Using a scenario-based experiment with 290 subjects, the authors tested relationships between candidate characteristics and trust in the recruiter as well as hesitancy to accept an offer. This research found that under all conditions, trust reduces hesitancy to accept the offer and that age reduces trust in the company. Further, this article found differences in the relationships between privacy protection competence, social media production and trusting stance on trust in the company based on the level of social media screening.
, Helmut Schneider, Kenneth R. Walsh
Journal of Organizational and End User Computing, Volume 32, pp 43-62; https://doi.org/10.4018/joeuc.2020100103

Abstract:
Although graduation rates have interested stakeholders, educational researchers, and policymakers for some time, little progress has been made on the overall graduation rate at four-year state colleges. Even though selective admission based on academic indicators such as high school GPA and ACT/SAT have widely been used in the USA for years, and recent statistics show that less than 40% of students graduate from four-year state colleges in four years in the US. The authors propose using an ensemble of analytic models that considers cost as a better form of analysis that can be used as input to decision support systems to inform decision makers and help them choose intervention methods. This article uses ten years of data for 10,000 students and applies ten analytical models to find the best predictor of at-risk students. This research also uses the receiver operating characteristic curve to help determine the most cost-effective trade-off between false positive and false negative levels.
Ming Chen, Yidan Huang, Shih-Heng Yu, Chia-Huei Wu
Journal of Organizational and End User Computing, Volume 32, pp 162-174; https://doi.org/10.4018/joeuc.2020100108

Abstract:
As social media has developed, online interaction between consumers and companies has increased rapidly. This research explores how companies' replies to consumers' past online comments affect consumers' predictions of their chances of winning randomly determined associated rewards (e.g., a random drawing). The results of two studies show that consumers who left positive comments (encouragement/appreciation) and then received a reply from the company predicted a higher likelihood of winning a random drawing than those whose comments did not receive company replies. Both the boundary and the underlying mechanism of the effect are discussed in the research. The present research contributes to the literature on companies' online reply patterns by linking their online replies with consumer predictions concerning randomly determined rewards, extends consumer efforts from offline purchases to online comments, and provides insights into the differences between consumer predictions regarding traditional offline promotional events and online promotional events.
Silas Formunyuy Verkijika
Journal of Organizational and End User Computing, Volume 32, pp 26-42; https://doi.org/10.4018/joeuc.2020100102

Abstract:
The proliferation of smartphones has provided a huge market for mobile apps and created a massive industry for developers/designers creating various mobile apps for use in our daily activities. However, with many mobile apps competition for users attention, the continued use of these apps has become a growing concern as users easily dump one app for another even after just a single use. This study examined the role of simplicity in the continuance use of mobile apps. The study extended the ECM with simplicity and showed that simplicity had a significant direct positive influence on confirmations, perceived usefulness, and satisfaction as well as a significant total indirect effect on continuance intentions. Additionally, all the ECM proposed relationships were confirmed. The study extends the current literature on continuance use of mobile apps by demonstrating the relevance of a concrete factor that developers/designs can manipulate to improve the continuance use for their mobile apps.
Brij B. Gupta, Pooja Chaudhary,
Journal of Organizational and End User Computing, Volume 32, pp 85-111; https://doi.org/10.4018/joeuc.2020100105

Abstract:
Cross-site scripting is one of the notable exceptions effecting almost every web application. Hence, this article proposed a framework to negate the impact of the XSS attack on web servers deployed in one of the major applications of the Internet of Things (IoT) i.e. the smart city environment. The proposed framework implements 2 approaches: first, it executes vulnerable flow tracking for filtering injected malicious scripting code in dynamic web pages. Second, it accomplished trusted remark generation and validation for unveiling any suspicious activity in static web pages. Finally, the filtered and modified webpage is interfaced to the user. The prototype of the framework has been evaluated on a suite of real-world web applications to detect XSS attack mitigation capability. The performance analysis of the framework has revealed that this framework recognizes the XSS worms with very low false positives, false negatives and acceptable performance overhead as compared to existent XSS defensive methodologies.
Naresh Ramu, Vijayakumar Pandi, Jegatha Deborah Lazarus, Sivakumar Radhakrishnan
Journal of Organizational and End User Computing, Volume 32, pp 1-14; https://doi.org/10.4018/joeuc.2020070101

Abstract:
Distributed networks are networks in which each node can act as a server or client and hence any node can provide service to any other node. In such a scenario, establishing a trust model between the service providing user and the service utilizing user is a challenging task. At present, only a few approaches are available in the past literature to provide this facility. Moreover, the existing approaches do not provide high trust accuracy. Therefore,a novel efficient trust model has been proposed in this article to support the secure dynamic group communication in distributed networks. The main advantage of the proposed work is that it provides higher trust accuracy. Moreover, the proposed work takes less memory for maintaining the trust values and increases the packet delivery ratio in comparison with other existing works which are in the literature.
Ahmad Karim, , Ahmad Firdaus
Journal of Organizational and End User Computing, Volume 32, pp 50-67; https://doi.org/10.4018/joeuc.2020070105

Abstract:
Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually.
Kanniga Devi R., Murugaboopathi Gurusamy, Vijayakumar P.
Journal of Organizational and End User Computing, Volume 32, pp 23-36; https://doi.org/10.4018/joeuc.2020070103

Abstract:
A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.
Ziwei Ye, Yuanbo Guo, , Fushan Wei, Ruijie Zhang, Jun Ma
Journal of Organizational and End User Computing, Volume 32, pp 37-49; https://doi.org/10.4018/joeuc.2020070104

Abstract:
Social engineering attacks are becoming serious threats to cloud service. Social engineering attackers could get Cloud service custom privacy information or attack virtual machine images directly. Existing security analysis instruments are difficult to quantify the social engineering attack risk, resulting in invalid defense guidance for social engineering attacks. In this article, a risk analysis framework for social engineering attack is proposed based on user profiling. The framework provides a pathway to quantitatively calculate the possibility of being compromised by social engineering attack and potential loss, so as to effectively complement current security assessment instruments. The frequency of related operations is used to profile and group users for respective risk calculation, and other features such as security awareness and capability of protection mechanism are also considered. Finally, examples are given to illustrate how to use the framework in actual scenario and apply it to security assessment.
, Arash Shaghaghi
Journal of Organizational and End User Computing, Volume 32, pp 15-22; https://doi.org/10.4018/joeuc.2020070102

Abstract:
Cloud computing has emerged as a dominant platform for computing for the foreseeable future. A key factor in the adoption of this technology is its security and reliability. Here, this article addresses a key challenge which is the secure allocation of resources. The authors propose a security-based resource allocation model for execution of cloud workloads called STARK. The solution is designed to ensure security against probing, User to Root (U2R), Remote to Local (R2L) and Denial of Service (DoS) attacks whilst the execution of heterogeneous cloud workloads. Further, this paper highlights the promising directions for future research.
Stoney Brooks, , Christoph Schneider
Journal of Organizational and End User Computing, Volume 32, pp 1-19; https://doi.org/10.4018/joeuc.2020040101

Abstract:
In today's technology-centric world, people are becoming increasingly dependent on the Internet. The most common use of the Internet is through social media, which is used to communicate, share, collaborate, and connect. However, continued usage of a hedonic system can be linked with compulsion or addiction. Since problematic usage/behaviors can lead to negative outcomes, this study aims to determine differential effects of Internet and social media addictions on social media-related technostress. This is examined in two different cultures: The U.S. and China. The results support the association between the Internet and social media addictions with increases in social media-related technostress. Additionally, these effects are moderated by culture. Implications for research and practice are discussed along with future directions for this stream.
Muhammad Sharif, Muhammad Attique, Muhammad Zeeshan Tahir, Mussarat Yasmim, , Urcun John Tanik
Journal of Organizational and End User Computing, Volume 32, pp 67-92; https://doi.org/10.4018/joeuc.2020040104

Abstract:
Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.
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