Customer e-Loyalty in Online Retailing: Testing a Measurement Scale

Research aim: In recent years, the interest for the activities aimed to nurture a strong relationship among retailers and their customers have increasingly intensified, especially in digital environments. Recently those activities have seen their relevance increased by the growing positive impact of COVID-19 pandemic on online retailing. Working on existing customers rather than capturing new ones is the new imperative for retailers, even if we refer to online platforms, obviously without underestimating the acquisition attempts of new customers. The aim of this study is to test a conceptual model of measurement for Customers e-Loyalty (CeL) in digital context in order to evaluate its impacts on digital retailers (e-commerce retailers, e-banking retailers, e-service providers). Methodology: it has been adopted a component-based Structural Equation Modelling (SEM) on a sample of Italian digital users, who makes online purchases prevalently on Amazon in order to test the CeL scale of measurement as a conceptual (meta) model. A structured questionnaire has been administered online to the consumers through Google Forms. Findings: The study has permitted to get some counterintuitive evidence related to the process of formation of customer loyalty in digital context. The trust isn’t a determinant of CeL and the affective loyalty doesn’t impact any of the elementary dimension of CeL, nor impacts on conative loyalty. Finally, the model has been able to better capture the impact of the individual dimensions of CeL on its outcomes (price sensitiveness, intentional SOW, e-WOM). Theoretical implication and originality: Propose a reliable customer e-loyalty measurement scale in online retailing. The statistical assessment of this conceptual model will permit, in the middle term, also to measure the CeL in several other retailing industries. Furthermore, in a next step, this investigation, could be extended to other geographical settings. Managerial Implication: the better understanding of the relationships among the latent variables and outcomes in the model might encourage the online retailers to figure out appropriate course of actions to win customers’ commitment and satisfaction and to provide better services in order to create a loyal customer base in a digital context

Automata for specifying and orchestrating service contracts

An approach to the formal description of service contracts is presented in terms of automata. We focus on the basic property of guaranteeing that in the multi-party composition of principals each of them gets his requests satisfied, so that the overall composition reaches its goal. Depending on whether requests are satisfied synchronously or asynchronously, we construct an orchestrator that at static time either yields composed services enjoying the required properties or detects the principals responsible for possible violations. To do that in the asynchronous case we resort to Linear Programming techniques. We also relate our automata with two logically based methods for specifying contracts

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016

MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs.

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art

GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering

This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system

“Buon appetito!” - Analyzing Happiness in Italian Tweets

We report the results of an exploratory study aimed at investigating the language of happiness in Italian tweets. Specifically, we conduct a time-wise analysis of the happiness load of tweets by leveraging a lexicon of happiness extracted from 8.6M tweets. Furthermore, we report the results of a statistical linguistic analysis aimed at extracting the most frequent concepts associated with the happy and sad words in our lexicon.Riportiamo i risultati dell’analisi esplorativa di un corpus di tweet in Italiano, al fine di individuare I concetti tipicamente associati alla felicità. Riportiamo inoltre i risultati di un’analisi time-wise dell’happiness load dei tweet nelle diverse ore della giornata e nei diversi giorni della settimana

From Orchestration to Choreography through Contract Automata

We study the relations between a contract automata and an interaction model. In the former model, distributed services are abstracted away as automata - oblivious of their partners - that coordinate with each other through an orchestrator. The interaction model relies on channel-based asynchronous communication and choreography to coordinate distributed services. We define a notion of strong agreement on the contract model, exhibit a natural mapping from the contract model to the interaction model, and give conditions to ensure that strong agreement corresponds to well-formed choreography.Comment: In Proceedings ICE 2014, arXiv:1410.701

Argument Mining on Italian News Blogs

International audienceThe goal of argument mining is to extract structured information, namely the arguments and their relations, from un-structured text. In this paper, we propose an approach to argument relation prediction based on supervised learning of linguistic and semantic features of the text. We test our method on the CorEA corpus of user comments to online newspaper articles, evaluating our system's performances in assigning the correct relation, i.e., support or attack, to pairs of arguments. We obtain results consistently better than a sentiment analysis-based base-line (over two out three correctly classified pairs), and we observe that sentiment and lexical semantics are the most informative features with respect to the relation prediction task.L'estrazione automatica di argomenti ha come scopo recuperare informazione strutturata, in particolare gli argomenti e le loro relazioni, a partire da testo semplice. In questo con-tributo proponiamo un metodo di predizione delle relazioni tra argomenti basato sull'apprendimento supervisionato di feature linguistiche e semantiche del testo. Il metodò e testato sul corpus di commenti di news CorEA, edèed`edè valutata la capacità del sistema di classificare le relazioni di supporto ed attacco tra coppie di argomenti. I risultati ottenuti sono superiori ad una baseline basata sulla sola analisi del sentimento (oltre due coppie di argomenti su trè e classificata correttamente) ed osserviamo che il sentimento e la semantica lessicale sono gli indicatoripì u informativi per la predizione delle relazioni tra argomenti