Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.

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Purpose This study aims to investigate the moderating effect of sustainability app on environmental citizenship behavior on the basis of norm-activation model. Design/methodology/approach A questionnaire survey, which comprises five variables (i.e. awareness of consequences, ascription of responsibility, personal norms, environmental citizenship behavior in a private sphere and environmental citizenship behavior in a public sphere) measured through 16 items, was conducted in the USA by using Amazon Mechanical Turk. With 549 valid respondents’ answers in hand, the collected data were analyzed applying a multi-group structural equation modelling technique with IBM SPSS AMOS 23 software program. Findings The results revealed that there is a positive and significant relationship between awareness of consequences, ascription of responsibility, personal norms and environmental citizenship behavior in both private and public sphere. Furthermore, this study attested that sustainability apps utilization has a moderating effect on the predictors of environmental citizenship behaviors. Originality/value Past studies have seldom examined the contribution of mobile apps to environmental sustainability. This paper enriches the extant academic literature in the field of technology for behavior change, and bears significant implications on how sustainability apps can be adopted by governments, policymakers, organizations and teacher educators to engage people and stimulate environmental citizenship behaviors.

In the past few years, an explosion of interest in big data has occurred from both academia and business communities. To reduce the uncertainty associated with some types of big data, organizations can adopt specific strategies inspired by service-dominant logic and value co-creation between organizations and customers. Specifically, alternating the extant body of literature and the results emerged from the case-study analysis of Mulino Bianco's 'Nel mulino che vorrei', this research illustrates how different organizations can purposefully design particular engagement platforms for 'joint agential experiential creation' of big data and how organizations design digital and interactive environment in order to acquire and manage integrated, cleaned, trustworthy and efficiently accessible data for decision-making or product innovation activities. From a theoretical viewpoint, this basis model for joint agential experiential creation of big data advances the extant research offering a useful insight to reduce data veracity. From a practical perspective, this study explains why organizations should invest in engagement platforms, such as digital forum, social media brand pages or specific digital and interactive environment built for the occasion, to facilitate the collection, storage and the analysis of customer-generated big data.