Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.
In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from.
Multichannel shopping has changed the way that consumers shop by offering them more choice and convenience. The growing competitive apparel market forces retailers to assess their current marketing strategies and their implementation. It is fundamental that multichannel retailers constantly provide high levels of hedonic shopping value through multichannel shopping in order to stimulate purchase. The purpose of this chapter is to emphasize the importance of hedonic shopping value in the context of multichannel shopping (in store, website, catalogue, mobile, and social media). The benefits of this chapter are evaluation of the strengths and weaknesses of each channel from the perception of the five channels for apparel shopping based on 18 hedonic shoppers in central London by using semi-structured interviews. The result shows that store and website gain the highest in the level of hedonic shopping value for apparel shopping and those are the most likely channels in which hedonic shoppers intend to shop for apparel in the future, while shopping via catalogue shows the lowest score of both hedonic shopping value and purchase intention. This chapter suggests that exploring the hedonic shopping value that consumers derive across five channels can enhance the understanding of hedonic shopping value in the context of the multichannel shopping environment.
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