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Xu Shao, Yanlin Yang, Lingzhi Wang
Journal of Global Information Management, Volume 29, pp 20-36;

With the widespread use of the internet, exploring how it will influence the labor market is of great significance. Based on the 2010-2018 China Family Panel Studies dataset, this paper investigates the effect of the internet on sustainable employability among Chinese aged 16-60. The empirical results of the panel double-hurdle model show that the internet can significantly enhance an individual's competitiveness in the labor market. Moreover, the heterogeneity tests show that the middle aged and older adults, freelancers, and those living in disadvantaged regions can benefit more on employability brought about by the internet. The authors define this phenomenon as the information welfare of the internet, which has narrowed the digital gap caused by the uneven development of technology among different social groups. In addition, the positive coefficient associated with internet use is driven by higher skill requirements in specific workplaces. The authors further explored the role workplace computerization has had in this process.
Shuili Yang, Yang Yi
Journal of Global Information Management, Volume 29, pp 37-54;

Under the backdrop of the continuous escalation of the Sino—U.S. trade friction, China's industrial development environment in global value chains (GVCs) has further deteriorated, research on the improvement GVC status of the Chinese manufacturing industry has become the focus of attention for industry and academia. The direction of R&D inputs are of utmost importance to the improvement of GVC status. However, comparatively little attention has been paid to this topic in existing studies. Following the production activity decomposition framework and combining with the World Input-Output Tables, the action mechanism of R&D inputs on GVC status from two aspects of industrial value-added and embedding position were analyzed, the moderating effect of digital servitization was demonstrated. Results show that: The input of applied research has inverted U-shaped influence on the industrial value-added, while it has U-shaped influence on embedding position; The input of basic research has U-shaped influence on the industrial value-added, while it has inverted U-shaped influence on embedding position; The moderating effect of digital servitization between R&D inputs and GVC status is significant, in the short term, the digital servitization can not only magnify the promotion effect of the applied research inputs on GVC status, but also shorten the lag period of basic research inputs on GVC status. In the long run, the digital servitization can not only weaken the marginalization trend of the applied research inputs on GVC status, but also enhance the positive feedback effect of basic research inputs on GVC status. This study is important for China to improve the GVC status.
Shuzhong Ma, Yichun Lin, Gangjian Pan
Journal of Global Information Management, Volume 29, pp 86-111;

The impact of cross-border e-commerce (CBEC) on international trade is prominent in recent years. The authors extend the international trade model with heterogeneous firms to include CBEC export and deduce that CBEC lowers the capability threshold for export. Firms and regions with different capabilities are affected differently, but the total regional export is increasing. In the empirical analysis section, they use panel data from 31 provinces in China from 2015 to 2018 and construct proxy variables for CBEC with CBEC comprehensive pilot zones and CBEC exporters. They find that CBEC contributes to economic growth and economic convergence. The underlying mechanisms include the convergence of regional exports and total factor productivity, while the convergence of capital isn't supported by the results.
, Prajwal Eachempati
Journal of Global Information Management, Volume 29, pp 176-193;

Today, the advent of social media has provided a platform for expressing opinions regarding legislation and public schemes. One such burning legislation introduced in India is the Citizenship Amendment Act (CAA) and its impact on the National Citizenship Register (NRC) and, subsequently, on the National Population Register (NPR). This study examines and determines the opinions expressed on social media regarding the act through a Twitter analysis approach that extracts nearly 18,000 tweets during 10 days of introducing the scheme. The analysis revealed that the opinion was neutral but tended to a more negative reaction. Consequently, recommendations on improving public perception about the scheme by suitable for interpreting the Act to the public are provided in the paper.
Jing Li, Jun Wang
Journal of Global Information Management, Volume 29, pp 1-19;

Under the background of digital economy, technological diversification and R&D internationalization are important strategic choices for eMNCs, represented by China, to seek advanced technological resources and create competitive advantages. This paper takes China's listed MNCs from 2009 to 2019 as the research object and applies a non-equilibrium panel negative binomial fixed effect regression to investigate the impact mechanism of technological diversification of China's MNCs on enterprise innovation performance and the moderating effect of overseas R&D networks. Results show that the related technological diversification of MNCs has a significant positive impact, and the unrelated technological diversification and their innovation performance are in inverted U-shaped relationship; overseas R&D networks have significant moderating effect while the breadth and depth of the moderating effect are not the same; significant differences exist in the moderating effect of overseas R&D networks due to the heterogeneity of institutional development levels among regions in China.
Eran Rubin, Frederik Beuk
Journal of Organizational and End User Computing, Volume 33, pp 1-24;

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;

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;

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;

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;

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;

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;

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.
, Sethumadhavan Madathil, Lakshmy K. V.
International Journal of Digital Crime and Forensics, Volume 13, pp 78-100;

Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.
Sunitha R., Chandrika J.
International Journal of Digital Crime and Forensics, Volume 13, pp 130-144;

The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.
M. Sravan Kumar Reddy, Dharmendra Singh Rajput
International Journal of Digital Crime and Forensics, Volume 13, pp 43-64;

At present, the field of homeland security faces many obstacles while determining abnormal or suspicious entities within the huge set of data. Several approaches have been adopted from social network analysis and data mining; however, it is challenging to identify the objective of abnormal instances within the huge complicated semantic graphs. The abnormal node is the one that takes an individual or abnormal semantic in the network. Hence, for defining this notion, a graph structure is implemented for generating the semantic profile of each node by numerous kinds of nodes and links that are associated to the node in a specific distance via edges. Once the graph structure is framed, the ternary list is formed on the basis of its adjacent nodes. The abnormalities in the nodes are detected by introducing a new optimization concept referred to as biogeography optimization with fitness sorted update (BO-FBU), which is the extended version of the standard biogeography optimization algorithm (BBO). The abnormal behavior in the network is identified by the similarities among the derived rule features. Further, the performance of the proposed model is compared to the other classical models in terms of certain performance measures. These techniques will be useful to detect digital crime and forensics.
Prakash K. R., Santhosh M. S., Purushothama G. K., Ramya M. V.
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 108-120;

Practical learning methodologies have become the need of the hour in the education sector, in particular in engineering education, to achieve technical competencies and skills. Advancements in web-based learning strategies have evolved over the years, and technology has played a major role in creating e-learning resources, among which remote labs are the ones transforming engineering laboratory delivery structure. In the present work, the methodology of improvising an existing traditional automation laboratory into an internet of things (IoT)-enabled laboratory with remote operation is presented. The implementation process of industrial internet of things (IIoT) for enabling remote operation of automation laboratory at the NIE, India is described in this paper. This paper describes the implementation of novel learning platform for programmable logic controllers (PLC), industrial PC (IPC), and hydraulic laboratories using a common IIoT architecture and framework. Additionally, user-level web-based application is created to control the use of all laboratory equipment from remote locations.
Krishnaveni P, Balasundaram S R
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 39-57;

The learners and teachers of the teaching-learning process highly depend on online learning systems such as E-learning, which contains huge volumes of electronic contents related to a course. The multi-document summarization (MDS) is useful for summarizing such electronic contents. This article applies the task of MDS in an E-learning context. The objective of this article is threefold: 1) design a generic graph based multi-document summarizer DSGA (Dynamic Summary Generation Algorithm) to produce a variable length (dynamic) summary of academic text based learning materials based on a learner's request; 2) analyze the summary generation process; 3) perform content-based and task-based evaluations on the generated summary. The experimental results show that the DSGA summarizer performs better than the graph-based summarizers LexRank (LR) and Aggregate Similarity (AS). From the task-based evaluation, it is observed that the generated summary helps the learners to understand and comprehend the materials easily.
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 137-157;

To respond to the needs of digital transformation, universities must continue to play their role as proving ground for educating the future generation and innovation. The article is devoted to overview, discussion, and investigation of application in higher education of two modern information technologies: big data and internet of things. The article identifies the role of analytics, based on big data, in improvement of education process and outlines the challenges, related with big data mining, storage, and security. Proposed statements are based on practical experience of the authors; architecture of program and methodological solution are the focus of the article. The article contributes to theory by the new approach to combination of big data and internet of things technologies in educational resources and, at the same time, includes implications for practice, presenting examples of the approach's realization and sharing the authors' experiences of such realization.
Ulrich Lichtenthaler
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 68-79;

Many companies have recently started digital transformation initiatives, and they now increasingly focus on artificial intelligence (AI). By means of smart algorithms and advanced analytics, firms attempt to leverage some of the results of their ongoing digital transformation initiatives, for example with regard to data about their established business operations. A conceptual framework underscores the need for combining data management and AI initiatives in order to ensure a firm's digital readiness and to realize digital business opportunities subsequently. An overview of recent trends further illustrates how different companies respond to these managerial challenges. This paper contributes to the literature on digitalization, AI, and ‘integrated intelligence' by highlighting the role of AI for leveraging data from digital transformation initiatives. Specifically, the use of AI applications helps companies to turn data into valuable knowledge and intelligence. In addition, this paper provides new knowledge about achieving superior performance in the digital economy.
Hullash Chauhan, , Ashok K. Sahoo
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 148-166;

Framing is a hazardous, stressful occupation that can directly affect farmers' health. In accumulation, a farmer makes a large economic commitment exposing them to high levels of economic risk. Stressors among farmers may help health professionals improve health outcomes by developing targeted intervention strategies and services. Throughout the last 70 years, the agricultural sectors have changed, and it is found to be one of the latent areas for stress. On large uses of mechanization technology, more uses of organic production, there is a decrease in price of agricultural product, and new complex legislation has made the life of farmers more stressful. In this study, based on in-depth review of literature and analysis of works based on the mental work pressure and stresses over framers, the risk associated with the Indian agricultural sectors were recognized by the use of fuzzy TOPSIS method. All the identified risk-factors were ranked based on their preferences. Then, the QFD technique was used to suggest the design parameters so as to minimize the work stress on farmers.
Ahmad Rajaa Al-Batayneh, Amineh A. Khaddam, Hani Jaza'A Irtaimeh, Suliman Raja Al-Batayneh
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 14-28;

This study sought to identify drivers of performance improvement indicators for GSCM strategy in Alban Al-Youm Company in Jordan and hence the impact of this strategy on sustainability performance through innovation strategy. Quantitative data were collected from a sample of participants from Alban Al-Youm Company in Jordan by a questionnaire-base survey to analyze research hypotheses via structural equation modeling (SEM). GSCM strategy success depends on pivotal drivers of performance improvement indicators. Eighteen significant drivers were identified in the current study. Moreover, the results found that GSCM strategy success exerted a significant effect on both innovation strategy and sustainability performance. Principally, innovation strategy mediated the effect of GSCM strategy on sustainability performance. This study is the first one to address performance indicators of GSCM practices in the dairy industry sector in Jordan. The study added knowledge value on the assessment of GSCM strategy performance, its impact on sustainability performance through innovation strategy.
Abhilasha Rangra,
International Journal of E-Health and Medical Communications, Volume 12, pp 36-49;

In recent years, the concept of cloud computing and big data analysis are considered as two major problems. It empowers the resources of computing to be maintained as the service of information technology with high effectiveness and efficiency. In the present scenario, big data is treated as one of the issues that the experts are trying to solve and finding ways to tackle the problem of handling big data analytics, how it could be managed with the technology of cloud computing and handled in the recent systems, and apart from this, the most significant issue is how to have perfect safety of big data in the cloud computing environment. In this paper, the authors mainly improve the performance of big data storage on cloud mechanics as the integration of mobile digital healthcare. The proposed framework involves the process of refining the sensitivity by using a deep learning approach. After this, it involves the step of computing or storage in the cloud-based server in an optimized manner. The experimental analysis provides a significant improvement in terms of cost, time, and accuracy.
Shivlal Mewada
International Journal of E-Health and Medical Communications, Volume 12, pp 50-66;

The valuable information is extracted through data mining techniques. Recently, privacy preserving data mining techniques are widely adopted for securing and protecting the information and data. These techniques convert the original dataset into protected dataset through swapping, modification, and deletion functions. This technique works in two steps. In the first step, cloud computing considers a service platform to determine the optimum horizontal partitioning in given data. In this work, K-Means++ algorithm is implemented to determine the horizontal partitioning on the cloud platform without disclosing the cluster centers information. The second steps contain data protection and recover phases. In the second step, noise is incorporated in the database to maintain the privacy and semantic of the data. Moreover, the seed function is used for protecting the original databases. The effectiveness of the proposed technique is evaluated using several benchmark medical datasets. The results are evaluated using encryption time, execution time, accuracy, and f-measure parameters.
Sonal Beniwal, Usha Saini, Puneet Garg, Rakesh Kumar Joon
International Journal of E-Health and Medical Communications, Volume 12, pp 84-96;

This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities.
Shabana R. Ziyad, Radha V.,
International Journal of E-Health and Medical Communications, Volume 12, pp 1-15;

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.
Ajay Dev, Sanjay Kumar Malik
International Journal of E-Health and Medical Communications, Volume 12, pp 67-83;

The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.
Yibing Ding, Hongyuan Zhang, Sitong Tang
Journal of Global Information Management, Volume 29, pp 71-85;

The digital economy continuously injects new momentum into the traditional economy and has become an important driving force for national economic development. Against this backdrop and using input-output data from the WIOD from 2002 to 2014, this paper empirically analyzes the impact of the development of the digital economy on the domestic value-added rate of Chinese manufacturing industry exports and the mechanism underlying this relationship. The results show that (1) digital economic input significantly promotes growth in the domestic value-added rate of manufacturing industry exports, (2) digital economic input mainly increases the domestic value-added rate of intermediate-product exports, (3) digital input has a significant positive impact on the capital-intensive and knowledge-intensive manufacturing industries, and (4) technological progress and cost reduction are important mechanisms through which the digital economy promotes the domestic value-added rate of exports.
Mei-Lan Li, Shu-Ping Lin, Ya-Hui Chan, Chia-Huei Wu
Journal of Organizational and End User Computing, Volume 33, pp 74-97;

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;

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.
Barkha Shree, Parneeta Dhaliwal
International Journal of Digital Crime and Forensics, Volume 13, pp 20-42;

Recent developments in digital forensics (DF) have emphasized that along with inspection of digital evidence, the study of behavioural clues based on behavioural evidence analysis (BEA) is vital for accurate and complete criminal investigation. This paper reviews the existing BEA approaches and process models and concludes the lack of standardisation in the BEA process. The research comprehends that existing BEA methodologies are restricted to specific characteristics of the forensic domain in question. To address these limitations, the paper proposes a standardised approach detailing the step-by-step implementation of BEA in the DF process. The proposed model presents a homogenous technique that can be practically applied to real-life cases. This standard BEA framework classifies digital evidence into categories to decipher associated offender characteristics. Unlike existing models, this new approach collects evidence from diverse sources and leaves no aspect unattended while probing criminal behavioural cues, thus facilitating its applicability across varied forensic domains.
Rajashree Soman, Sukumar R.
International Journal of Digital Crime and Forensics, Volume 13, pp 65-77;

Visitor validation at entrance generates a large number of image files that need to be transmitted over to cloud for future reference. The image data needs to be protected by active and passive adversaries from performing cryptographic attacks on these data. The image data also needs to be authenticated before giving it for future use. Focusing on reliable and secure image sharing, the proposed method involves building a novel cloud platform, which aims to provide a secure storage in the public cloud. The main objective of this paper is to provide a new way of secure image data storage and transmission on cloud using cryptographic algorithms. To overcome the flaws in current system, a novel method using BigchainDB, which has advantages of blockchain technology and traditional database, is proposed for storing attributes of image.
Zuhrieh Shana, Tareq Mohamad Alyatim, Mohammad Alkhazaleh, Nahla Alshalabi
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 171-192;

The purpose of this study is to assess the students' skills in creating virtual classrooms using the Google Classroom (GC) application through a 30 item questionnaire. In addition, the study used a Likert scale and an interview to explore students' perceptions towards GC. The sample consisted of 26 Master's students in the College of Education at Al Ain University in UAE. Data was generated from analyzing the questionnaire, and the Likert scale and the interview were used to answer the research questions. The results revealed that the degree of Master's students' skills in creating virtual classrooms was weak, and there are no statistically significant differences between students' skills based on gender and specialization. The results also showed that the perception of the GC was positive, and the majority of participants assured that GC is easy to create and use. They also expressed interest to learn more about GC through training. The findings from this study could be utilized by College of Education faculty members, students, administrators, and policymakers.
Karima Boussaha, Farid Mokhati, Amira Hanneche
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 80-107;

This article introduces a new learner's self-assessment environment as CEHL that allows comparison of learners' programs with those elaborated by the teacher. The subjacent idea is to indirectly compare programs through their graphical representations described by ontologies. So, CEHL developed so-called S_Onto_ALPPWA which allows comparing learners' productions with those elaborated by the teacher. The tool allows essentially (1) generating two ontologies from the learner's program and the teacher's one, (2) applies some matching algorithms for measuring degrees of similarity and dissimilarity between learner's program and teacher's one, and (3) assessing the learners by giving them a list of semantic and syntactic errors detected in their programs. The present work is an extension of the authors' previous work, which did not take into account semantics errors. In the present work, they have managed to detect syntactic and semantic errors by using ontologies. To demonstrate the effectiveness of the system, two prospective experiments were conducted. The obtained results were very encouraging.
Silvia Gaftandzhieva, Rositsa Doneva
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 58-79;

Social networking is becoming a more powerful tool for students for communication, information sharing, and discussions. This paper presents a study, based on a survey questionnaire, which aims to investigate to what extent and for what purposes teachers from different countries from all over the world use social networking in their teaching practice. The attitude towards the use of social networking in higher education in general is examined. The study is intended to seek dependences between the answers related to the above issues and different teachers' characteristics, on the point of view if the teachers are well informed about social networking sites, or whether they participate in interest groups or research related to social networking and higher education. Finally, summarized results of the survey are presented, depending on the continent where the countries of the participants are located.
Rugaiyah Rugaiyah, Cecep Kustandi, Desi Rahmawati, Dini Nur Fadhillah
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 158-170;

This study aimed to develop a valid draft clinical supervision web-based model for increasing the professionalism of elementary school teachers who have complete commitment, integrity, and competence in Jakarta, Indonesia. This study used qualitative research. Data was collected through observation and interview. The researcher worked together with three supervisors, five schools, two elementary school head chiefs, and three elementary school teachers. Clinical supervision consists of five phases following a research and development procedure, such as the initial preparation, observation, interpretation, post-observation meeting, and reverse meetings. The supervisor assessed explained clinical supervision web-based model was very effective and efficient considering teachers who had to be fostered in one target area, good communication between supervisors and teachers in the target area, because they could use the website and even upload videos teaching teachers in the classroom.
Yazan Emnawer Al-Haraisa, Noor Al-Ma'Aitah, Khalaf Al-Tarawneh, Ahmad Abuzaid
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 102-115;

This study investigated the role of talent management practices (talent discovery, talent development, and talent retention) on achieving a competitive advantage in Jordanian insurance companies. Data were collected via a questionnaire using a convenience sample consisting of 130 managers and their assistants. Using SPSS 21, a multiple regression analysis was conducted and found a positive effect for talent management (talent discovery, talent development, and talent retention) on competitive advantage, and the relative importance of dimensions was talent development, talent discovery, and talent retention, respectively. This study concluded the importance of talent management practices in Jordanian insurance sector, in particular in the selection stage for choosing the right people. Recommendations for future researches are also identified.
Pratap Chandra Mandal
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 1-13;

Companies providing services operate in a dynamic business environments. They require understanding and appreciating the new service realities. They should devise and implement appropriate strategies to sustain the competition. The objective of the study is to analyze the strategies. New service realities include shifting customer relationships, customer empowerment, and customer co-creation. The study aims at a conceptual analysis of the literature. Service companies require satisfying both customers and employees to remain competitive. Companies should aim to prevent service failures from occurring, rectify service failures once they have occurred to the satisfaction of customers, involve employees at each stage, and ensure that employee enthusiasm and motivation are high. Findings of the study suggest that proper understanding of the new service realities will allow companies to develop strategies, implement the strategies, and execute the strategies effectively. All such initiatives provide a direction for service companies to excel, delight their customers, and build long-term customer relationships.
Ann Myril Chua Tiu, Reciel Ann B. Tanaid, Jonash Oropeza Durano, Esperanza M. Del Fierro, Kafferine D. Yamagishi, Maria Esther Medalla, , Brian J. Galli, ,
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 29-52;

This study explores the disparity between the food safety knowledge and practices of street food vendors in a developing country. A purposive survey to determine the knowledge and practices of street food vendors was conducted on 36 street food vendors. The food safety knowledge of the street vendors was tested under five factors, as adopted in the literature. Through a chi-square goodness-of-fit test, a disparity between the food safety knowledge and practices of street food vendors was observed. Moreover, a structural modeling technique was adopted to analyze the interrelationships between food safety knowledge factors and practices. As a result, it was found that most of the food safety knowledge impact their counterpart factors on food safety practices. Likewise, such factors on food safety knowledge are crucial in explaining the reported food safety practices. When mapping the food safety knowledge.of the street food vendors to their practices, it was found that street vendors have a high level of food safety knowledge but low in actual practice, as evidenced by poor food handling practices among street food vendors which also significantly differs from the established standards. The results in this study provide crucial insights into the literature in developing a holistic view of the dynamics of street food vending. Moreover, the results may be beneficial to stakeholders as it may aid them in the development of management and policy initiatives that may be crucial in addressing the contemporary issues of street food vending in developing countries.
Manju Lata,
International Journal of Service Science, Management, Engineering, and Technology, Volume 12, pp 133-147;

The widespread deployments of IoT technologies and their applications present a number of significant challenges due to integration of a number of different categories of devices and technologies. Most importantly, this creates big challenges for the large global organizations in crafting security and access policies that span any region they operate in. As of now, the technical standards for specific IoT security components in the industry are only just starting to be addressed by established IT security standards bodies, consortium organizations, and vendor alliances. Most of government bodies and regulators are also finding it hard to develop globally accepted standards. This present work is concerned with identifying the most important challenges and opportunities with reference to security issues in IoT and identifies the role of standards and regulatory compliances for meeting these challenges at the global level. The available standards and regulations have been presented from practice and research perspectives. A review of the successful IoT standards have been presented, which has been accepted by the industry. At the same time, the most important country specific regulatory compliances have been presented, which have been made mandatory by the government.
Ankur Bhardwaj, Sanmukh Kaur, Anand Prakash Shukla, Manoj Kumar Shukla
International Journal of E-Health and Medical Communications, Volume 12, pp 16-35;

Ultrasound images have an inherent property termed as speckle noise that is the outcome of interference between incident and reflected ultrasound waves which reduce image resolution and contrast and could lead to improper diagnosis of any disease. In different approaches for reducing the speckle noise, there exists a class of filters that convert multiplicative noise into additive noise by using algorithmic functions. The current study proposes a cellular automata-based despeckling filter (CABDF) that implements a local spatial filtering framework for the restoration of the noisy image. In the proposed CABDF filter, a dual transition function has been designed which emphasizes the calculation of nearby weighted separation whose loads originate from the CABDF filtered image, including spatial separation, extend inconsistency, and statistical dispersion. The proposed filter found efficient both in terms of filtering and restoration of the original structure of the ultrasound images.
Gaoju Yang, Yujie Wang, , , Shuzhong Ma
Journal of Global Information Management, Volume 29, pp 55-70;

Cross-border e-commerce has gradually expanded in international trade markets over the past decades. This paper analyzes the determinants of the volume of cross-border e-commerce in a gravity model framework. Moreover, the paper explores the role of internet popularity and finds a significant promotion effect on the volume of cross-border e-commerce. Furthermore, by utilizing cross-border express delivery data, the analysis indicates a significant difference in the impacts of the determinants between the aggregate and consumer levels and provides an important addition to the literature on e-commerce and international trade.
Haitao Li
Journal of Global Information Management, Volume 29, pp 112-137;

With proliferated applications of the internet and world wide web, people are increasingly interacting with government websites. It is therefore significant to measure satisfaction on government systems from citizen's perspective. While general customer satisfaction index (CSI) models have received much attention from researchers, few studies are conducted to evaluate public satisfaction on government websites. The extent to which traditional CSI models can be extended to investigate public satisfaction on government websites remains unclear. On analysis of CSI, technology acceptance model (TAM), and task-technology fit (TTF) model, this study constructs a government website public satisfaction index (GWPSI) model and provides an empirical study by adapting GWPSI model in the context of G2C e-government. Structural equation modeling (SEM) is applied to data collection and processing with questionnaires collected from users of the government website of Guangdong Province in China. The findings provide several important implications for e-government research and practice.
Lin Xiao, Jian Mou, Lihua Huang
Journal of Global Information Management, Volume 29, pp 138-160;

Despite the popularity of online health services (OHSs) among patients in recent years, academic research on this phenomenon is limited. Drawing on the valence framework, the authors proposed a model to explore both the most important facilitators of OHS use intention from the perceived value perspective and inhibitors of OHS use intention from the perceived risk perspective. Data were collected from 407 OHS users through an online survey. Results showed that the inhibitors of OHS use intention include privacy risk and social risk, while facilitators include social support value, convenience value, and utilitarian value. These findings enrich the OHS literature by revealing both the inhibitors and facilitators of OHS use intention. This study also provides practical implications for platforms offering OHS in relation to effectively attracting users.
Journal of Global Information Management, Volume 29, pp 161-175;

The growth and rising prominence of multinationals from emerging markets (eMNCs) mark a significant phase in the evolution of the world economy in the last decade. This study investigates the effect of eMNCs' institutional embeddedness in terms of age on the adoption strategy of new and emerging information and communication technologies (ICT). Using panel multiple regression on a unique database of 3,756 observations from 394 Indian eMNCs in period of 2009 to 2019, the authors find that firm age has a unique negative impact on ICT investments of eMNCs. However, ownership is able to influence the negative impact of age in unique ways. Business group affiliation attenuates the impact of firm age on ICT investments, such that the reduction in ICT investments with firm age is less for BG-affiliated firms. Meanwhile, the higher the foreign institutional ownership in eMNCs, the lower the impact of firm age on ICT investments.
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;

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.
Chethana H. T., Trisiladevi C. Nagavi
International Journal of Digital Crime and Forensics, Volume 13, pp 1-19;

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.
Rupa Ch., Sumaiya Shaikh, Mukesh Chinta
International Journal of Digital Crime and Forensics, Volume 13, pp 101-113;

In current days, there is a constant evolution in modern technology. The most predominant usage of technology by society is the internet. There are many ways and means on the internet through which data is transmitted. Having such rapid and fast growth of communicating media also increases the exposure to security threats, causing unintellectual information ingress. Steganography is the main aspect of communicating in an aspect that hides the extent of communication. Steganalysis is another essential concern in data concealing, which is the art of identifying the existence of steganography. A framework has been designed to identify the concealed data in the multimedia file in the proposed system. This work's main strength is analyzing concealed data images without embedding and extracting the image's payloads. A quantitative steganalysis approach was considered to accomplish the proposed objective. By using this approach, the results were achieved with 98% accuracy.
More Swami Das, A. Govardhan, Vijaya Lakshmi Doddapaneni
International Journal of Digital Crime and Forensics, Volume 13, pp 114-129;

The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.
Thach Ngoc Pham, Giang Hong Nguyen
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 121-136;

Online learning's application in the language learning process has become an important topic as the COVID-19 crisis has restricted in-person classes. This study investigates student satisfaction in an online language learning course at a higher educational institution in Vietnam. The study tested the influence of learner-learner, learner-content, and learner-instructor interactions; internet self-efficacy; and self-regulation on student course satisfaction. The effects of student background variables were also explored. Linear regression analysis was conducted to determine the contribution of predictor variables to student satisfaction. The findings showed that student interaction with peers, content, and instructors and self-regulation were good predictors of student satisfaction; however, internet self-efficacy was not a good predictor. Additionally, there were no differences in effects of gender and prior online language learning experience on student course satisfaction.
Fahima Hajjej, Sarra Ayouni, Hadil Shaiba,
International Journal of Web-Based Learning and Teaching Technologies, Volume 16, pp 21-38;

Due to the COVID-19 pandemic, many higher education institutes shifted to online learning with the precautionary measures taken by governments. This transition was very rapid and sudden, which brought challenges to all learning methods in all disciplines while opening up new opportunities. Different studies have been carried out to evaluate experiences of online migration and study its effect on stakeholders in education. This paper is aimed to rationally evaluate the transition to online learning in PNU from the student perspective. Five thousand ten student responses to an online survey were collected. The survey results indicate that the majority of students were satisfied by the quality of the delivered courses during this crisis period as they have received adequate support from instructors, IT, and leaders. Moreover, student satisfaction can be explained by the readiness and preparedness of PNU for such circumstances. Indeed, students and instructors are poised to adopt new learning modalities as they were familiar with new technologies and innovation in learning and teaching so far.
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