Purpose The purpose of this paper is to examine how user-generated positive social electronic word-of-mouth (eWOM) via Facebook affects brand attitude and, consequently, influences purchase intention of smartphones. The spending patterns of consumers, particularly decision-makers, have been affected to a substantial degree by the strong presence of brands on the web. eWOM, one among the shape of net product reviews, exercises extensive influence not only on the consumers’ attitude towards the brand but also impacts their buying intentions. Design/methodology/approach A survey-based empirical study was conducted to examine the influence of social eWOM on brand attitude and purchase intention of consumers. Structural equation modeling (SEM) was applied using data collected from 311 respondents comprising users of Facebook. Findings The research established that user-generated positive eWOM on social networking site, Facebook significantly influences brand attitude and purchase intention of consumer electronics. Research limitations/implications The data set used for the study limits generalizing of results, as the data are not representative across industries or across all social media applications. The study provides a useful and interesting insight into the theory and practice of eWOM. It shows how social eWOM, an emerging communication tool, not only helps twenty-first century marketers in reaching customers, but how it also plays a vital role in affecting brand attitude and purchase intention of products. Originality/value This paper provides useful and valuable insights into the relationship between social eWOM, brand attitude and purchase intention of consumer electronics, an area that largely remains unexplored. The study can also be replicated for other products or services for future research.

Purpose The purpose of this paper is to explore the relationship between employer brand dimensions and employer of choice (EOC). The paper also analyses the role of person-organisation fit in transferring employer brand dimensions to EOC status, and the moderating role of social media in the relationship between person-organisation fit and EOC. Design/methodology/approach Factor analysis has been conducted to validate the “employer attractiveness” scale for identifying the dimensions of employer brand. Structural equation modelling has been used to conduct mediation and moderation analysis. The results are based on the perceptions of college students regarding employer brand dimensions and EOC status. Findings The paper provides empirical insights on how the person-organisation fit helps in transferring employer brand dimensions to EOC status. The results indicate that the person-organisation fit acts as a full mediator, indicating that for becoming a EOC, the dimensions of employer brand must be linked to the person-organisation fit. Also, the moderation analysis results highlight the importance of social media towards obtaining EOC status. Originality/value The authors believe that the study is the first of its kind to investigate drivers of EOC, and to identify the role of the person-organisation fit as a mediating variable and social media as a moderating variable.

Purpose The advent of e-retailing has created multiple options for customers. Hence, most important concern is to identify the experience which entices customers for repurchase from the one particular e-retailer. This paper tries to identify the customer experience with reference to activities happening post-purchase. The purpose of this paper is to develop and validate online post-purchase customer experience (OPPCE). Design/methodology/approach The methodology of this scale development study starts with skimming of relevant literature to identify the knowledge gap and prepare a theoretical background for the study. Then scientific method is applied for scale creation. First, items of the scale been identified through interviews of online shoppers and marketing experts. Then the major dimensions were identified through exploratory factor analysis applied on the data collected from active shoppers, who transacted online in last six months, with the help of a structured questionnaire survey. These data are analyzed through structural equation modeling to validate the scale. Findings The study yields that the scale for measuring OPPCE is multi-dimensional. It has six dimensions, i.e. delivery, product-in-hand, return and exchange, customer support, benefits and feel-good factors. The proper focus on the items of these dimensions can help e-retailers improve customer experience and increase repeat purchase. Originality/value Post-purchase activities have a significant impact on customer and their repeat purchase intensions. But it has not received its due attention particularly in the online context. Hence, this paper fills this knowledge gap and gives e-retailers a tool to enhance their customers’ experience.

With the development of information technology, a large volume of data is growing and getting stored electronically. Thus, the data volumes processing by many applications will routinely cross the petabyte threshold range, in that case it would increase the computational requirements. Efficient processing algorithms and implementation techniques are the key in meeting the scalability and performance requirements in such scientific data analyses. So for the same here, it has been analyzed with the various MapReduce Programs and a parallel clustering algorithm (PKMeans) on Hadoop cluster, using the Concept of MapReduce. Here, in this experiment we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Means Clustering Algorithm. It has been found that as the number of nodes increases the execution time decreases, but also some of the interesting cases has been found during the experiment and recorded the various performance change and drawn different performance graphs. This experiment is basically a research study of above MapReduce applications and also to verify and validate the MapReduce Program model for Parallel KMeans algorithm on Hadoop Cluster having four nodes.

Purpose The study considers a four-construct model for validating the factors of overall patient satisfaction with medication. This paper aims to study the satisfaction of patients with their medication. Patient satisfaction with medication influences treatment-related behaviors, such as their possibility of continuing to use their medication, to take their medication correctly and to adhere with medication regimens. Design/methodology/approach treatment satisfaction questionnaire for medication (TSQM) version 1.4 patient satisfaction model has been tested for reliability and validity through confirmatory factor analysis. A structured questionnaire, incorporating variables identified from original TSQM version 1.4 (Atkinson et al., 2005), has been used as a survey instrument for the study. Final respondent sample size was 380 patients who were on medication for a minimum duration of 10 days. Findings In total, 75 per cent of the willingly participating patients were found to adhere to medication regimen as advised by their physician. Effectiveness, side effects, convenience and global satisfaction were found to be reliable and valid factors for assessing satisfaction with medication among patients in emerging market settings. Originality/value The existing studies on measuring patient satisfaction have been majorly confined to developed economies. There is lack of focused research on patient satisfaction and its underlying determinants in the emerging market settings. The present study is an attempt to fill the existing research gap.

From the recent years the large volume of data is growing bigger and bigger. It is difficult to measure the total volume of structured and unstructured data that require machine-based systems and technologies in order to be fully analyzed. Efficient implementation techniques are the key to meeting the scalability and performance requirements entailed in such scientific data analysis. So for the same in this paper the Sequential Support Vector Machine in WEKA and various MapReduce Programs including Parallel Support Vector Machine on Hadoop cluster is analyzed and thus, in this way Algorithms are Verified and Validated on Hadoop Cluster using the Concept of MapReduce. In this paper, the performance of above applications has been shown with respect to execution time/training time and number of nodes. Experimental Results shows that as the number of nodes increases the execution time decreases. This experiment is basically a research study of above MapReduce applications.

With the development of information technology, a large volume of data is growing and getting stored electronically. Thus, the data volumes processing by many applications will routinely cross the petabyte threshold range, in that case it would increase the computational requirements. Efficient processing algorithms and implementation techniques are the key in meeting the scalability and performance requirements in such scientific data analyses. So for the same here, it has been analyzed with the various MapReduce Programs and a parallel clustering algorithm (PKMeans) on Hadoop cluster, using the Concept of MapReduce. Here, in this experiment we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Means Clustering Algorithm. It has been found that as the number of nodes increases the execution time decreases, but also some of the interesting cases has been found during the experiment and recorded the various performance change and drawn different performance graphs. This experiment is basically a research study of above MapReduce applications and also to verify and validate the MapReduce Program model for Parallel KMeans algorithm on Hadoop Cluster having four nodes.

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