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<p> <span class="fontstyle0">22 February 2022</span></p> <p><span class="fontstyle0"><strong>Abstract</strong><br /></span></p> <p><span class="fontstyle1"><strong>Purpose</strong> </span><span class="fontstyle3">– </span><span class="fontstyle4">Due to the recent development of Big Data and arti</span><span class="fontstyle5">fi</span><span class="fontstyle4">cial intelligence (AI) technology solutions in customer relationship management<br />(CRM), this paper provides a systematic overview of the </span><span class="fontstyle5">fi</span><span class="fontstyle4">eld, thus unveiling gaps and providing promising paths for future research.<br /></span><span class="fontstyle1"><strong>Design/methodology/approach</strong> </span><span class="fontstyle3">– </span><span class="fontstyle4">A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus<br />database, and 2 bibliometric techniques were used: bibliographic coupling and keywords</span><span class="fontstyle3">’ </span><span class="fontstyle4">co-occurrence.<br /></span></p> <p><span class="fontstyle1"><strong>Findings</strong> </span><span class="fontstyle3">– </span><span class="fontstyle4">Outcomes of the bibliometric analysis enabled the authors to identify three main sub</span><span class="fontstyle5">fi</span><span class="fontstyle4">elds of the AI literature within the CRM domain<br />(Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI</span><span class="fontstyle3">–</span><span class="fontstyle4">CRM integrations)<br />and capture promising paths for future development for each of these sub</span><span class="fontstyle5">fi</span><span class="fontstyle4">elds. This study also develops a three-step conceptual model for AI<br />implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this </span><span class="fontstyle5">fi</span><span class="fontstyle4">eld and, on the other hand,<br />managers in planning an appropriate and coherent strategy.<br /></span><span class="fontstyle1"><strong>Originality/value</strong> </span><span class="fontstyle3">– </span><span class="fontstyle4">To the best of the authors</span><span class="fontstyle3">’ </span><span class="fontstyle4">knowledge, this study is the </span><span class="fontstyle5">fi</span><span class="fontstyle4">rst to systematise and discuss the literature regarding the relationship<br />between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can bene</span><span class="fontstyle5">fi</span><span class="fontstyle4">t from the study, as it unveils recent<br />important directions in CRM management research and practices.</span></p> <p> </p> <p><span class="fontstyle0"><strong>Methodology</strong><br /></span><span class="fontstyle2">To achieve the research goal, we performed a literature review combined with a bibliometric analysis. Bibliometrics is<br />described as </span><span class="fontstyle3">“</span><span class="fontstyle2">the mathematical and statistical analysis of bibliographic records</span><span class="fontstyle3">” </span><span class="fontstyle2">(</span><span class="fontstyle2" style="color: #0000ff;">Pritchard, 1969</span><span class="fontstyle2">) and is used to<br />establish intellectual linkages among articles and keywords, thus providing a big picture of rising trends and potential<br />research opportunities </span><span class="fontstyle2">. Bibliometric techniques offer the advantage of introducing quantitative rigor compared to<br />narrative literature reviews, which might be invalidated by the<br />subjective bias of the researcher</span></p> <p> </p> <p> </p> <p> </p>
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tipologia (61.4%), corso (56.1%), classifica (55.4%), previsione (55.1%), lista (54.0%), lstm (53.9%), crispr (53.1%), tesi (52.6%), nicchia (51.8%), abstract (51.7%), generative (50.4%), glossario (49.7%), statistica (48.9%), keras (47.8%), metodo_socratico (47.8%), prompt (47.8%), indice (47.7%), attivita (47.6%), doctit (47.6%), pytorch (47.0%)
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ai
5
bigdata
5
crm
5
fine-tuning
5
machine learning
5
neural network
5
wpg
5
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