UNCORRECTED PROOF Journal of Retailing and Consumer Services xxx (xxxx) xxx-xxx Contents lists available at ScienceDirect Journal of Retailing and Consumer Services journal homepage: http://ees.elsevier.com Acceptance and use of big data techniques in services companies Juan-Pedro Cabrera-Sánchez, Ángel F. Villarejo-Ramos * University of Sevilla, Spain ARTICLE INFO Keywords Big data Intention behaviour UTAUT Use resistance Perceived risk Opportunity cost Services companies ABSTRACT Companies able to take advantage of the information coming from the use of Big Data will have a competitive advantage by being able to make decisions based on greater knowledge of customers and competition. Besides, the access to the software for the treatment of this great amount of data is free. So, the objective of this paper is to study the level of acceptance and use of these technologies, Big Data techniques, by services companies. To analyse the intention and use it extends the acceptance technologies model- Unifed Theory of Acceptance and Use of Technology (UTAUT) - to the context of Big Data techniques, incorporating the effect on it of three new variables: resistance to use, perceived risk and opportunity cost. The structural model was evaluated using partial least squares structural equation modelling (PLS-SEM) with an adequate global ft. The verifcation is carried out with a sample of 199 Spanish services companies, and its main results are the strong effect of the facilitating con- ditions on the intention and use of Big Data, as well as the direct effect of the opportunity cost and the resistance to use on the intention, and the indirect inhibiting effect of the perceived risk through the resistance to use on intention behaviour. 1. Introduction The exponential increase in the calculation capacity of computers together with the great development of new statistical techniques has made it possible to practically process huge amounts of data in real time. In this sense, all these techniques that are part of the revolution brought by Big Data, have meant a total change in the use of informa- tion within companies, both in the way in which data are managed and stored and in the way in which they are processed, analysed and inter- preted (Agrawal et al., 2011). Big Data is a technology that allows storing, processing and combining huge amounts of different types of data obtained from different sources (Brünink, 2016), large datasets, unstructured and captured almost in real time. Data management and storage techniques fall within the feld of en- gineering. In the feld of marketing study, data analysis is the most rele- vant activity. Sivarajah, Kamal, Irani, and Weerakkody (2016) pro- posed the classifcation of the different types of data analytics: 1) de- scriptive analytics, techniques that help know what has happened; 2) inquisitive analytics are those that help understand why something is happening; 3) predictive analytics are those that help anticipate what is most likely to happen in the future; 4) prescriptive analytics, techniques that answer the question “what now?” and 5) preventive analytics, tech- niques that help to recommend what needs to be done. All these techniques are useful to us to apply them to the treatment of the great amount of information that come from heterogeneous data of: texts, audio, video, social media, data in general and Artifcial Intelli- gence. These large blocks are not mutually exclusive as they can be used together, although each block has specifc techniques and algorithms. Applying this great variety of Big Data Analytic techniques, it is possi- ble, for example, to distinguish photographs, and recognise voices, dis- covering patterns of consumer behaviour or any other phenomenon for which there are data. Rehman, Chang, Batool & Wah (2016) propose a summary of these data analysis techniques and methods for Big Data, which is shown in Table 1: The good use of these techniques gives companies that use them an important competitive advantage over the rest of companies in their sec- tor (Sivarajah et al., 2016), notably helping to make data-based deci- sions (McAfee and Brynjolfsson, 2012). But the implantation, acceptance and use of a technology in the de- cision-making core of companies involves overcoming the brakes arising from ignorance of techniques, resistance to technological change, fear and anxiety, in addition to the limitations of the technology itself to im- plement (Yaqoob et al., 2016). The literature review about Big Data Techniques, such as is shown in the previous table, indicates a greater concern for the purely tech- nical aspects of the tools themselves and their applications (Sivara- jah et al., 2016), not focusing on aspects related to the intention and use of them by companies. Only a few works have focused their study on companies’ intentions of adopting Big Data Techniques (Brünink, 2016; Demoulin and Coussement, 2018; Huang et al., 2012; * Corresponding author. Email address: curro@us.es (Á.F. Villarejo-Ramos) https://doi.org/10.1016/j.jretconser.2019.101888 Received 6 March 2019; Received in revised form 5 July 2019; Accepted 17 July 2019 Available online xxx 0969-6989/© 2019.