Template-Type: ReDIF-Article 1.0 Author-Name: M. Venkatesh Author-X-Name-First: M. Author-X-Name-Last: Venkatesh Author-Name: V. Mohan Raj Author-X-Name-First: V. Mohan Author-X-Name-Last: Raj Author-Name: Y. Suresh Author-X-Name-First: Y. Author-X-Name-Last: Suresh Title: Mining massive online location-based services from user activity using best first gradient boosted distributed decision tree Abstract: User activity is predicted through the frequency in which the online substances in location-based social networks (LBSN) are produced and used by the consumer. Users are classified by researchers into a number of groups depending upon the level of their functioning. This work involves gradient boosted distributed decision tree (GBDT) which is optimised on the basis of total iterations and shrinkage on using best algorithm. Implementation of the data is done through Hadoop network. A foursquare dataset is created using work, food, travel, park and shop. One of the most commonly used machine learning algorithm is stochastic gradient boosted decision trees (GBDT) at present. The node with lowest lower bound is developed through best first search (BFS). Its own filing system is provided through Hadoop which is called Hadoop distributed file system (HDFS). The algorithm used is K-nearest Neighbour (KNN) classifier algorithm. Journal: Int. J. of Enterprise Network Management Pages: 3-13 Issue: 1 Volume: 11 Year: 2020 Keywords: user activity; foursquare dataset; stochastic gradient boosted decision trees; GBDT; best-first search; BFS; K-nearest neighbour classifier; KNN; social network; location-based social networks; LBSN; big data; Hadoop network; Hadoop distributed file system; HDFS. File-URL: http://www.inderscience.com/link.php?id=103880 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:3-13 Template-Type: ReDIF-Article 1.0 Author-Name: V. Rajeswari Author-X-Name-First: V. Author-X-Name-Last: Rajeswari Author-Name: M. Kavitha Author-X-Name-First: M. Author-X-Name-Last: Kavitha Author-Name: Dharmishtan K. Varughese Author-X-Name-First: Dharmishtan K. Author-X-Name-Last: Varughese Title: GRO and WeGO - algorithmic approaches to integrate the heterogeneous databases and enhance the evaluation of ontology mapping systems in the semantic web Abstract: In the present day world, where information driven economy and information enhanced living standards rule everything, the sources of data from which the information is derived, are highly heterogeneous. The heterogeneity necessitates a mechanism for integrating data, before it is presented to the user. The internet and WWW are forming the backbone of information. Semantic web is an initiative in achieving the goal of 'machine processed information' being available to us than requiring human intelligence for processing information. This work is carried out to address the heterogeneity problem that exists among data sources and provides a solution through the application of ontology. Ontology is a conceptual tool for handling semantic heterogeneity. The algorithmic approach is adopted in the mapping solution system. The elements of ontology are compared and similarity analysis is carried out to arrive at the degree of matching of individual nodes as well as the ontology in totality for an ontology alignment. Journal: Int. J. of Enterprise Network Management Pages: 14-31 Issue: 1 Volume: 11 Year: 2020 Keywords: ontology; semantic web; heterogeneous databases; GRO; WeGO. File-URL: http://www.inderscience.com/link.php?id=103902 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:14-31 Template-Type: ReDIF-Article 1.0 Author-Name: V. Karunakaran Author-X-Name-First: V. Author-X-Name-Last: Karunakaran Author-Name: M. Suganthi Author-X-Name-First: M. Author-X-Name-Last: Suganthi Author-Name: V. Rajasekar Author-X-Name-First: V. Author-X-Name-Last: Rajasekar Title: Feature selection and instance selection using cuttlefish optimisation algorithm through tabu search Abstract: Over the recent decades, the amount of data generated has been growing exponentially, the existing machine learning algorithms are not feasible for processing of such huge amount of data. To solve such kind of issues, we have two commonly adopted schemes, one is scaling up the data mining algorithms and other one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this paper, cuttlefish optimisation algorithm along with tabu search approach is used for data reduction. Dataset can be reduced mainly in two ways, one is the selecting optimal subset of features from the original dataset, in other words eliminating those features which are contributing lesser information another method is selecting optimal subset of instances from the original data set, in other words eliminating those instances which are contributing lesser information. Cuttlefish optimisation algorithm with tabu search finds both optimal subset of features and instances. Optimal subset of feature and instance obtained from the cuttlefish algorithm with tabu search provides a similar detection rate, accuracy rate, lesser false positive rate and the lesser computational time for training the classifier that we obtained from the original data set. Journal: Int. J. of Enterprise Network Management Pages: 32-64 Issue: 1 Volume: 11 Year: 2020 Keywords: data reduction; instance selection; feature selection; cuttlefish optimisation; tabu search. File-URL: http://www.inderscience.com/link.php?id=103907 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:32-64 Template-Type: ReDIF-Article 1.0 Author-Name: S. Radha Author-X-Name-First: S. Author-X-Name-Last: Radha Author-Name: C. Nelson Kennedy Babu Author-X-Name-First: C. Nelson Kennedy Author-X-Name-Last: Babu Title: Enterprise big data analysis using SVM classifier and lexicon dictionary Abstract: The emergence of the digital era has led to growth in various types of data in a cloud. In fact, there may be three fourth of the total data will be treated as big data. In many organisations, massive volume of both structured and unstructured data sit idle. Various categories of data are complex for pre-processing, analysing, storing and visualising. Cloud computing provides suitable platform for big data analytics for the storage and for predicting customer behaviour to sell products. Unstructured data like emails, notes, messages, documents, notifications and Twitter comments (including from IoT devices) remains untapped and is not stored in a relational database. Valuable information on pricing, customer behaviour and competitors may be inhumed within unstructured data. This makes cloud-based analytics as an effective research field to address several issues and risks need to be reduced. So we propose a method to extract and cluster sentiment information from various types of unstructured text data from social networks by using SVM classifiers combined with lexicons and machine learning for sentiment analysis of customer behaviour feedback. The method has performed efficient data collection, data loading and efficiently performs sentiment analysis on deep and hidden web. Journal: Int. J. of Enterprise Network Management Pages: 65-75 Issue: 1 Volume: 11 Year: 2020 Keywords: deep web mining; sentiment analysis; big data; unstructured data; map reduce; hidden information; big data analytics; text mining; clusters; enterprise data. File-URL: http://www.inderscience.com/link.php?id=103913 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:65-75 Template-Type: ReDIF-Article 1.0 Author-Name: B. Bhuvaneswari Author-X-Name-First: B. Author-X-Name-Last: Bhuvaneswari Author-Name: T. Meeradevi Author-X-Name-First: T. Author-X-Name-Last: Meeradevi Title: An optimised neural network-based spectrum prediction scheme for cognitive radio Abstract: A cognitive radio (CR) technology enables all the users to utilise spectrum without interference. There will be a spectrum sensing for all the non-authorised users to perceive the other possibilities of getting a channel. The traffic feature will be unknown to be a priori to design the spectrum predictor with the back propagation (BP) neural network (NN) model and the multi-layer perceptron (MLP).This work proposed an optimised neural network to obtain improved results. The BP algorithm will not require prior knowledge of the real world problems that are trapped within the local minima. This is used widely to solve the problems and found in literature as an evolutionary algorithm like the bacterial foraging optimisation algorithm (BFOA) used for the MLP NN for enhancing the process of learning and improving the rate of convergence as well as accuracy of classification. Performing this spectrum predictor will be analysed using some extensive simulations. Journal: Int. J. of Enterprise Network Management Pages: 76-93 Issue: 1 Volume: 11 Year: 2020 Keywords: spectrum prediction; cognitive radio; CR; neural networks; NN; multi-layer perceptron; MLP; back propagation; BP; bacterial foraging optimisation algorithm; BFOA. File-URL: http://www.inderscience.com/link.php?id=103914 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:76-93 Template-Type: ReDIF-Article 1.0 Author-Name: S. Radhakrishnan Author-X-Name-First: S. Author-X-Name-Last: Radhakrishnan Author-Name: S. Neduncheliyan Author-X-Name-First: S. Author-X-Name-Last: Neduncheliyan Author-Name: K.K. Thyagharajan Author-X-Name-First: K.K. Author-X-Name-Last: Thyagharajan Title: An improved downlink packet scheduling algorithm for delay sensitive devices in both H2H and M2M communications in LTE-advanced networks Abstract: The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission. Journal: Int. J. of Enterprise Network Management Pages: 94-111 Issue: 1 Volume: 11 Year: 2020 Keywords: LTE-advanced; greedy algorithm; downlink packet scheduling; CA; multi input and multi output; MIMO; M2M; QoS. File-URL: http://www.inderscience.com/link.php?id=103916 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:1:p:94-111 Template-Type: ReDIF-Article 1.0 Author-Name: Medha Author-X-Name-First: Author-X-Name-Last: Medha Author-Name: Sharath Kumar Reddy Author-X-Name-First: Sharath Kumar Author-X-Name-Last: Reddy Author-Name: K.E.K. Vimal Author-X-Name-First: K.E.K. Author-X-Name-Last: Vimal Author-Name: Aravind Raj Sakthivel Author-X-Name-First: Aravind Raj Author-X-Name-Last: Sakthivel Author-Name: Jayakrishna Kandasamy Author-X-Name-First: Jayakrishna Author-X-Name-Last: Kandasamy Title: Labour productivity improvement using hybrid Maynard operation sequence technique and ergonomic assessment Abstract: Productivity measures how efficiently productions inputs, such as labour and capital, are being used in an economy to produce a given level of output. In this article, Maynard operation sequence technique was used for time measurement study and minimisation of fatigue among the operators by using ergonomics in a stamping unit. The primary objective of the study reported was to reduce the motion of all tasks in order to reduce the effort and time to achieve higher production and better service level by the ergonomic approach. Scoring sheets approach was used in conducting ergonomics study to decide the fitness of any unit on the basis safety and posture analysis of the operator. The proposed hybrid approach (MOST-Ergo) can be used to improve the productivity of any organisation by reducing the time and fatigue consumed by the operator during the operation. Journal: Int. J. of Enterprise Network Management Pages: 113-143 Issue: 2 Volume: 11 Year: 2020 Keywords: Maynard operation sequence technique; MOST; time study; standard time; productivity; ergonomic work posture analysis. File-URL: http://www.inderscience.com/link.php?id=106306 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:2:p:113-143 Template-Type: ReDIF-Article 1.0 Author-Name: S.K. Lavanya Author-X-Name-First: S.K. Author-X-Name-Last: Lavanya Author-Name: B. Parvathavarthini Author-X-Name-First: B. Author-X-Name-Last: Parvathavarthini Title: Context-sensitive contrastive feature-based opinion summarisation of online reviews Abstract: Contrastive opinion summarisation (COS) systems produce summary by selecting and aligning contrastive sentences from a set of positive and negative opinionated sentences. Most of the existing COS methods do not consider the implicit opinion present in a sentence while producing summary. Implicit opinion can be identified based on context terms present in a sentence. Therefore, a new COS approach called context-sensitive contrastive opinion summarisation is proposed. Initially linguistic rules are framed based on dependency relation to extract context-feature-opinion phrases. To automatically cluster the extracted context-feature-opinion phrases into contrastive arguments, a clustering algorithm is proposed. Context sensitive weight is calculated for each phrase based on their probability of occurrence in the concepts of ConceptNet. Clustering algorithm integrates context sensitivity with contrastive similarity for producing better arguments summary. Experimental conducted on car and product review datasets demonstrate that the context-sensitive clusters achieved good coverage and precision when compared to state-of-art approaches. Journal: Int. J. of Enterprise Network Management Pages: 144-163 Issue: 2 Volume: 11 Year: 2020 Keywords: feature-based opinion summarisation; contrastive summary; contrastiveness; representativeness; context-aware sentiment analysis. File-URL: http://www.inderscience.com/link.php?id=106309 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:2:p:144-163 Template-Type: ReDIF-Article 1.0 Author-Name: Sujana Adapa Author-X-Name-First: Sujana Author-X-Name-Last: Adapa Author-Name: Josie Fisher Author-X-Name-First: Josie Author-X-Name-Last: Fisher Title: Owner-managers' perceptions of corporate social responsibility practices within small and medium-sized accounting firms – an Australian study Abstract: This article explores conceptualisations of corporate social responsibility (CSR); perceptions of its importance; and practices implemented by owner-managers of small and medium sized enterprises (SMEs) in Australia. Qualitative in-depth interview data was obtained from 17 owner-managers of small and medium-sized accounting firms operating in Sydney. Inductive content analysis was conducted by the researchers to identify the concepts and themes of importance by using Leximancer qualitative text analytical software. The results revealed that the owner-managers of these firms were aware of the basics of social responsibility and recognised that the adoption of responsible business practices contributes to business success. The owner-managers perceptions of the practices of CSR varied based on the firm size that resulted in the emergence of an additional category of family-owned firms. Micro-sized firms emerged on the basis of distinct CSR practices and unique orientations towards the concept of CSR as highlighted by the owner-managers. Journal: Int. J. of Enterprise Network Management Pages: 164-188 Issue: 2 Volume: 11 Year: 2020 Keywords: corporate social responsibility; CSR; small and medium-sized firms; owner-managers; stakeholders; firm size; accounting. File-URL: http://www.inderscience.com/link.php?id=106311 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:2:p:164-188 Template-Type: ReDIF-Article 1.0 Author-Name: K. Geetha Author-X-Name-First: K. Author-X-Name-Last: Geetha Author-Name: A. Kannan Author-X-Name-First: A. Author-X-Name-Last: Kannan Title: Maximising the efficiency of keyword analytics framework in wireless mobile network management Abstract: Nowadays, data analytics in spatial database objects are associated with keywords. In the past decade, searching the keyword was a major focusing and active area to the researchers within the database server and information retrieval community in various applications. In recent years, the maximising the availability and ranking the most frequent keyword items evaluation in the spatial database are used to make the decision better. This motivates to carry out research towards of closest keyword cover search, which is also known as fine tuned keyword cover search methodology; it considers both inter object distance and keyword ranking of items in the spatial environment. Baseline algorithm derived in this area has its own drawbacks. While searching the keyword increases, the query result performance can be minimised gradually by generating the candidate keyword cover. To resolve this problem a new scalable methodology can be proposed in this paper. Journal: Int. J. of Enterprise Network Management Pages: 189-207 Issue: 2 Volume: 11 Year: 2020 Keywords: information retrieval; keyword searching; nearest neighbour; point of interest; spatial database. File-URL: http://www.inderscience.com/link.php?id=106314 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:2:p:189-207 Template-Type: ReDIF-Article 1.0 Author-Name: J. Frankvijay Author-X-Name-First: J. Author-X-Name-Last: Frankvijay Title: Neuro fuzzy cognitive connection functional point for enterprise network management Abstract: Software effort estimation (SEE) is one of the vital roles in enterprise management. This created SEE process used to minimise the incomplete data involvements. It may require large amount of data, which increases the efficiency of the effort estimation system. So, in this paper introduces the enterprise data analytics process by using the size and judgmental software effort estimation process. This method analyses the effort in terms of using expert's opinions, use case, functional points and software size unit information. This method evaluates the neuro fuzzy cognitive connection-based functional points are used to estimates the effort with effective manner. This method examines the connectivity between one requirement to another requirements and it constructs the relationship graph that eliminates the incomplete requirements and details successfully. It reduces the time for examining the software effort. Then the performance of the system is calculated with the support of experimental results such as MRE and VAF. Journal: Int. J. of Enterprise Network Management Pages: 209-219 Issue: 3 Volume: 11 Year: 2020 Keywords: software effort estimation; SEE; cognitive connection-based functional points; VAF; MRE. File-URL: http://www.inderscience.com/link.php?id=108722 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:3:p:209-219 Template-Type: ReDIF-Article 1.0 Author-Name: V. Sathya Author-X-Name-First: V. Author-X-Name-Last: Sathya Author-Name: A. Oliver Bright Author-X-Name-First: A. Oliver Author-X-Name-Last: Bright Title: CAMELS model analysis for district central co-operative banking enterprises in Andra Pradesh Abstract: CAMELS is a perceived worldwide rating framework to evaluate the relative money related quality of banking enterprises and to propose essential procedures to enhance shortcomings of banking enterprises. In India, Reserve Bank of India established methodology in 1996 pursued on the proposals of Padmanabham Working Group (1995) board of trustees. In the present examination, an endeavour has been made to rank the different district co-operative banking enterprises working in Andra Pradesh and investigate the five positions in their budgetary execution amid the examination time frame. In Andhra Pradesh before detachment of Telangana State, there were 22 DCCBs (district co-operative banking enterprises) in Andhra Pradesh State Cooperative Bank. For breaking down similar execution of the DCCBs in Andhra Pradesh, CAMELS model has been utilised for the (CAGR compound yearly development rate) of 12 years (2002-2003 to 2013-2014) and from that point, thorough rank test and factual measures have been utilised. CAMELS remain for capital adequacy, asset quality, management efficiency, earnings capacity, liquidity and sensitivity. CAMELS' proportions have the imperative to feature the sound money related position and additionally the wellbeing of the DCCBs of the co-agent DCCB through smaller scale investigation of an asset report and pay explanation things. Journal: Int. J. of Enterprise Network Management Pages: 233-250 Issue: 3 Volume: 11 Year: 2020 Keywords: CAMELS; Andra Pradesh; cooperative bank; micro analysis. File-URL: http://www.inderscience.com/link.php?id=108723 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:3:p:233-250 Template-Type: ReDIF-Article 1.0 Author-Name: V.G. Vinu Author-X-Name-First: V.G. Author-X-Name-Last: Vinu Author-Name: A. Oliver Bright Author-X-Name-First: A. Oliver Author-X-Name-Last: Bright Title: Study and prioritising factors of productivity of the employees of steel manufacturing industry, Kanjikode by extended ACHIEVE model Abstract: Employee productivity is a key factor for the success of manufacturing companies. Performance analysis studies with a wide range of approaches are used in an attempt to improve employee productivity. However, these studies take only one or two factor into consideration, which may not provide a comprehensive solution to the productivity problem they face. An extended ACHIEVE model by the name MACHIEVE model has been proposed to overcome this, with additional factor M-'Material'. Survey analysis based on this new model has been performed among employees in the steel manufacturing industry in Kanjikode. This is a structural equation modelling analysis which used filled questionnaire data of randomly selected 420 employees from among a population of 1,280 employees. The results indicated that all eight factors of MACHIEVE model has impact on employee productivity. The analysis also suggested that the factors C-Clarity and H-Help have the greatest impact on labour productivity. Journal: Int. J. of Enterprise Network Management Pages: 220-232 Issue: 3 Volume: 11 Year: 2020 Keywords: ACHIEVE; MACHIEVE; productivity; employees; steel manufacturing. File-URL: http://www.inderscience.com/link.php?id=108724 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:3:p:220-232 Template-Type: ReDIF-Article 1.0 Author-Name: C. Krubakaran Author-X-Name-First: C. Author-X-Name-Last: Krubakaran Author-Name: K. Venkatachalapathy Author-X-Name-First: K. Author-X-Name-Last: Venkatachalapathy Title: A novel learning and prediction Bayesian hierarchical clustering-Dirichlet mixture model for effective data mining Abstract: Decision making and business support is an important process in data mining and this can be achieved by means of pattern classification and extraction. Since the huge volume of data needs starving knowledge to process and organisation faces many issues in solving those issues. Clustering is an effective technology available to analyse and convert the datasets into meaningful patterns. Clustering in data mining uses various attributes to compute large dataset and meet out the real time issues. The proposed model uses Bayesian hierarchical clustering model with Dirichlet model to resolve the issues in large dataset analysis. Experimental results prove that proposed model experience better clustering efficiency than conventional complete link agglomerative clustering by achieving 92% of clustering accuracy. Journal: Int. J. of Enterprise Network Management Pages: 251-263 Issue: 3 Volume: 11 Year: 2020 Keywords: data mining; data clustering; Bayesian hierarchical clustering; BHC; Dirichlet. File-URL: http://www.inderscience.com/link.php?id=108729 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:3:p:251-263 Template-Type: ReDIF-Article 1.0 Author-Name: Gururaj Kidiyoor Author-X-Name-First: Gururaj Author-X-Name-Last: Kidiyoor Author-Name: Amol S. Dhaigude Author-X-Name-First: Amol S. Author-X-Name-Last: Dhaigude Title: Obsolescence risk in B2B purchase of high technology products Abstract: This study investigates the relative importance of perceived obsolescence risk in comparison to generic perceived risk dimensions among business buyers while purchasing high-technology products. We use step-wise regression to assess the contribution of obsolescence risk towards the overall perceived risk (OPR). Our results show that perceived obsolescence risk is a significant predictor of variation in the OPR and reports the highest mean score, implying that it is also the most important of all the risk dimensions. Our findings have significant implications for marketing managers of high-technology products, enabling them to design their marketing mix in a manner that reduces the OPR with particular emphasis on reducing the obsolescence risk. Journal: Int. J. of Enterprise Network Management Pages: 264-288 Issue: 3 Volume: 11 Year: 2020 Keywords: high-technology products; risk; uncertainty; perceived obsolescence risk; business buying; stepwise regression. File-URL: http://www.inderscience.com/link.php?id=108730 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:3:p:264-288 Template-Type: ReDIF-Article 1.0 Author-Name: S. Karthikeyan Author-X-Name-First: S. Author-X-Name-Last: Karthikeyan Author-Name: P. Meenakshi Devi Author-X-Name-First: P. Meenakshi Author-X-Name-Last: Devi Title: An attempt to enhance the time of reply for web service composition with QoS Abstract: The web services are the commonly prevailing service clusters of the service-oriented framework (SOA) and service related assessments. The disputes are related to the quality of service (QoS) for choosing web services freely and creating a collection of web services for carrying out trades. The ultimate aim is to choose web services based on the non-functional features and quality of service (QoS) ranks. In order to choose a web service for every process a social aspect web (SAW) scheme is employed which does not comprehensively make use of all sorts of web services. It employs the requirements of the user for ranking web service set of the applications and finally provides SAW schemes over a set of web service applicants. The mechanism helps in selecting the web services in terms of quality of services (QoS) scores and user needs. The choice of web services over varied web services based on scheme can be utilised for aggregation and organising web services resulting in optimised time of reply to the web service actions. Journal: Int. J. of Enterprise Network Management Pages: 289-303 Issue: 4 Volume: 11 Year: 2020 Keywords: quality of service; QoS; social aspect web; SAW; ranks; web services; non-functional features. File-URL: http://www.inderscience.com/link.php?id=111750 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:4:p:289-303 Template-Type: ReDIF-Article 1.0 Author-Name: S. Prabu Author-X-Name-First: S. Author-X-Name-Last: Prabu Author-Name: S. Karthik Author-X-Name-First: S. Author-X-Name-Last: Karthik Title: Attempting to design differed service broker forwarding strategy for data centres in cloud environment Abstract: The cloud computing is based on broadcasted computing resources for controlling diverse services like a server, storage and applications. The applications and models are offered in terms of pay per usage using the data centre to the users. The data centres are positioned globally and moreover, these data centres could be overloaded with the escalated number of client applications that are being serviced at the identical time and position which corrupts the comprehensive quality of service of the relayed services. Diverse user applications might need diverse customisation and demands calibrating the performance of the user applications at differed resources are quite intricate. The service supplier is incapable of performing choices for the suitable set of resources. The design of differed service broker forwarding strategies is based on heuristics intended to accomplish minimal reply time based on the transmission medium, bandwidth, latencies and task size. The designed service broker strategy attempts in minimising the overloads of the data centres by conveying the user demand to the subsequent data centres that acquire improved reply and operational time. The analysis reveals potential outcomes in terms of reply and operational time as estimated to the other exiting broker strategies. Journal: Int. J. of Enterprise Network Management Pages: 304-319 Issue: 4 Volume: 11 Year: 2020 Keywords: service broker; cloud environment; quality of service; QoS; data centres and latency. File-URL: http://www.inderscience.com/link.php?id=111751 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:4:p:304-319 Template-Type: ReDIF-Article 1.0 Author-Name: Habib Ullah Khan Author-X-Name-First: Habib Ullah Author-X-Name-Last: Khan Author-Name: Hameed Sulthan Imam Abdul Samad Author-X-Name-First: Hameed Sulthan Imam Abdul Author-X-Name-Last: Samad Title: Enterprise strategic shift of technology: cloud-based systems verses traditional distributed system Abstract: Cloud computing can be perceived as an amalgamation of technology, facilitator, source and as an application that raised the curtain for a shift in the global data management system. There is a need for more research and promotion to encourage the laggards towards this revolution. The present research is such a trial to explain laggards about the road map, framework and possible benefits of the strategic shift from traditional or distributed systems to cloud-based systems. The research work is mainly carried out to facilitate optimal information technology (IT) services using cloud-based technology for a medium-sized multi-national company. The study tool, a questionnaire, is shared with 250 respondents of different cadres in the organisation based on 28 countries worldwide. To explain the necessity of this shift, the study collected the version of the employees regarding their favouritism for cloud technology adoption (CA) as well as the need for user training (UT) for practicing cloud adoption. Journal: Int. J. of Enterprise Network Management Pages: 320-346 Issue: 4 Volume: 11 Year: 2020 Keywords: international data corporation; cloud-enabled services; cloud adoption framework; CAF; information technology; Software-as-a-Service; SaaS; Platform-as-a-Service; PaaS; Infrastructure-as-a-Service; IaaS. File-URL: http://www.inderscience.com/link.php?id=111775 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:4:p:320-346 Template-Type: ReDIF-Article 1.0 Author-Name: Santanu Mandal Author-X-Name-First: Santanu Author-X-Name-Last: Mandal Author-Name: Venkateswara Rao Korasiga Author-X-Name-First: Venkateswara Rao Author-X-Name-Last: Korasiga Author-Name: Payel Das Author-X-Name-First: Payel Author-X-Name-Last: Das Title: Influence of social media on medical chain agility and resilience: an empirical investigation Abstract: Social media has been dominant in shaping competition and business performance. However, the importance of social media in the development of medical supply chain capabilities is still unexplored. Our research investigates the role of social media as a prominent enabler of medical supply chain agility and resilience. To this end, the study explores the importance of social media interaction and social media usability on medical chain agility and resilience. Furthermore, the study examined the moderating role of social media orientation on the above linkages. The responses were collected using online survey and were analysed using structural equation modelling. Based on 276 completed responses, the study found positive influences of social media interaction and social media usability on both medical chain agility and medical chain resilience. The study contributes to the emerging literature of social media by undersigning the importance of social media interaction, usability and orientation in services supply chain. Journal: Int. J. of Enterprise Network Management Pages: 347-371 Issue: 4 Volume: 11 Year: 2020 Keywords: healthcare; agility; resilience; social media; dynamic capabilities; supply chain. File-URL: http://www.inderscience.com/link.php?id=111777 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:4:p:347-371 Template-Type: ReDIF-Article 1.0 Author-Name: D. Gopi Author-X-Name-First: D. Author-X-Name-Last: Gopi Author-Name: A. Pal Pandi Author-X-Name-First: A. Pal Author-X-Name-Last: Pandi Author-Name: R. Rajesh Author-X-Name-First: R. Author-X-Name-Last: Rajesh Title: Integrated quality healthcare practice in hospitals - a structural equation modelling approach Abstract: In today's highly competitive environment, healthcare establishments HCEs like hospitals and health centres are focusing on service quality, as a measure to face the competition. There is a strong doubt that, whether the present quality systems practice in hospitals serve the stakeholders' expectations effectively. The authors felt that the proposed model namely integrated quality healthcare system (IQHS) with ten important critical factors could meet out this gap. The objective of this paper is to discuss the IQHS practice in hospitals in Tamil Nadu, India through the perception of stakeholders. Further the model has been validated through structural equation modelling (SEM) approach. The authors conclude that the contextual relationships of the ten critical factors of IQHS were found to be the significant drivers of quality performance to attain sustainability. Journal: Int. J. of Enterprise Network Management Pages: 372-386 Issue: 4 Volume: 11 Year: 2020 Keywords: hospitals; critical factors; integrated quality healthcare system; structural equation modelling; SEM. File-URL: http://www.inderscience.com/link.php?id=111778 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijenma:v:11:y:2020:i:4:p:372-386