Acceso usuarios
Granada, España
Covilhã (Conceição), Portugal
Sevilla, España
Purpose–Thisstudyproposesahierarchicsegmentation that developsa tree-based classification model and classifies the cases into groups.Thisallowsforthedefinitionofe-commerceuserprofilesforeachofthegroups. Additionally, it facilitates the development of actions to improve the adoption of the online channel that is in such high demand in the current pandemic COVID-19 context. Design/methodology/approach–Regardingthecreatedsegments,twoextremesegmentsstandout dueto their marked differences and high volume. Segment 3 with 23% of the sample is the group with the most predisposition to use the online channel and is characterised by a high level of trust, more habitual use in comparisonwithothergroupsandthebeliefthatitsuseimplieshighperformance,whichindicatestheybelieve it to be useful, quick and helpful for more an effective shopping experience. The other extreme is found in segment 7. This group makes up 17.7% of the total and is the most reluctant to use the online channel. These users are characterised by the complete opposite: they have a low level of trust in this channel. However, the effort expectancy is low, i.e. they consider that the adoption of the online channel does not involve many difficulties in its learning and use. Nevertheless, they use it less regularly than the others. Findings–Basedontheconclusionsreachedinthisstudy,inthecurrentpandemiccontextinwhichconsumer demandforonline shoppingchannels for all types of products is on the rise, it is recommended that companies focus on the following aspects. It is essential to build trust with the user and show them the real benefits of e-commerce, how it would improve their life and why they should use it. Additionally, it is vital that the user perceives it as an easy procedure that does not require a significant learning curve. Other fundamental aspects wouldbetoreduceanyuncertaintytheusermighthaveabouttheonlineshoppingprocess,tomakeitaseasyas possible, and to design a simple, intuitive and user-friendly interface. It is also recommendable to manage data usageefficiently. Todoso,theauthorsrecommendaskingtheuserfortheleastamountofinformationpossible, offering a data protection policy and assuring them that their information will not be misused nor shared with third parties. All of this provides a series of facilities to modify the online shopping habits of users. Researchlimitations/implications– As in most of the research, this study presents a series of limitations that shouldbedebatedandthatcouldopenfuturelinesofinvestigation.Firstly,regardingthesampleusedthat waslimited to twoneighbouringcountrieswith similar profiles a priori; it would be necessary to compare their possible cultural differences according to Hofstede’s dimensions as well as increase the number of European countries being analysed to reach a more generalised conclusions. Secondly, the variables used are a combination of those derived from the UTAUT2 model and others suggested in the literature as decisive in technology adoption by users, in this sense other theories and variables could be incorporated to complete a more holistic model. Practical implications– This work contributes in a general way to (1) analysing the intention to use e-commerce platforms from a set of antecedents previously defined by their importance, after a period of economic and social restrictions derived from the pandemic; (2) determination of customer segments from the classification made by theCHAIDanalysis;(3)characterisationofthepreviouslydefinedsegmentsthroughthe successive divisions that were proposed in the analysis carried out. Social implications– Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process to make it as easy as possible, and to design a simple, intuitive, and userfriendly interface. It is also recommended to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy, and assuring them that their information will not be misused or shared with third parties. Originality/value– The results obtained have allowed us to establish predictive and explanatory models of the behaviour of the segments and profiles created, which will help companies to improve their relationships with online customers in the coming years.
