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How Can SMEs Become More Sustainable? Modelling the M-Commerce Consumer Behaviour with Contingent Free Shipping and Customer Journey’s Touchpoints Optimisation

doi: 10.3390/su13126845
Digital technology is leading the transformation of business models into sustainable ones, expanding and changing the competitiveness scenario. This paper aims to promote a new sustainable retailer model shaped by contingent free shipping theory and the optimisation of the customer’s journey, enriching the scientific literature by proposing a consumer behavioural model that highlights the contribution of four selected touchpoints to the sustainable transformation of SMEs. The research was elaborated by deploying a framework that presents five analysis methods, namely, an additive function construction, a TOPSIS method, a Spearman rank correlation coefficient calculation, a content analysis and an analytic hierarchy process, which engender the new model. Discovering nine distinguished categories of e-tailers, the sustainable retailer profile was developed and the touchpoint with the greatest contribution to the transformation process was identified. The results of the study allowed the authors to propose the model as a solution to withstand the preponderant negative experience provided by analysed e-tailers to digital buyers, representing the opportunity for SMEs’ sustainable transformation and long-term growth in a competitive, ever-growing market.
sustainable retailer, Environmental effects of industries and plants, optimisation, TJ807-830, consumer behaviour, decision-making, TD194-195, Renewable energy sources, Environmental sciences, customer journey’s touchpoints, contingent free shipping, business model, mobile commerce, GE1-350
sustainable retailer, Environmental effects of industries and plants, optimisation, TJ807-830, consumer behaviour, decision-making, TD194-195, Renewable energy sources, Environmental sciences, customer journey’s touchpoints, contingent free shipping, business model, mobile commerce, GE1-350
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
