DOI: 10.7910/dvn/eejst3
title: Customer Experience Management & CRM - Crossref Bibliographic Metadata

description: &lt;p&gt;This dataset provides detailed bibliographic metadata records for scholarly publications related to &lt;b&gt;'Customer Experience Management' (CEM) and 'Customer Relationship Management' (CRM)&lt;/b&gt;, as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding these interconnected domains.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Contextual Overview of Customer Experience Management & CRM:&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;1. Definition and Context:&lt;/strong&gt;&lt;br&gt;
Customer Relationship Management (CRM) focuses on practices, strategies, and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, aiming to improve business relationships, assist in customer retention, and drive sales growth. Customer Experience Management (CEM) is a broader strategy that focuses on the perceptions and feelings a customer has as a result of all their interactions with a company over time. Both gained significant traction from the late 1990s, fueled by technology and a shift towards customer-centricity.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2. Strengths and Weaknesses:&lt;/strong&gt;&lt;br&gt;
Strengths include enhanced customer loyalty, increased sales, improved customer service, and better customer insights. Weaknesses often stem from costly and complex system implementations, challenges in achieving a truly unified customer view, potential for data privacy concerns, and organizational resistance to customer-centric cultural shifts. Success requires strategic alignment, robust data governance, and consistent execution across all touchpoints, not just technological deployment.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;3. Relevance and Research Potential:&lt;/strong&gt;&lt;br&gt;
CEM and CRM are critical in today's competitive, digitally-driven markets where customer expectations are high. They are central to marketing, sales, and service management. Research avenues include the impact of AI and big data on CRM/CEM effectiveness, measuring and managing omnichannel customer journeys, the role of employee experience in delivering customer experience, personalization at scale, and the ethical implications of customer data utilization, particularly within emerging digital ecosystems.&lt;/p&gt;

&lt;hr/&gt;

&lt;p&gt;&lt;strong&gt;Dataset Structure and Content:&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for CEM/CRM.&lt;/p&gt;

&lt;p&gt;Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;code&gt;term_results/&lt;/code&gt;: A subfolder containing JSON files for results of initial broad keyword searches related to CEM/CRM.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;merged_results.json&lt;/code&gt;: Aggregated results from these individual term searches before advanced filtering.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;filtered_results.json&lt;/code&gt;: Results after applying a more specific, complex Boolean query (e.g., &lt;code&gt;("customer experience management" OR CRM ...) AND ("strategy" OR ...)&lt;/code&gt;) and exact phrase matching to refine relevance. The exact query used is detailed within this file.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;final_results.json&lt;/code&gt;: &lt;strong&gt;This is the primary file of interest for most users.&lt;/strong&gt; It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Customer Experience Management & CRM' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref).&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;statistics_results.json&lt;/code&gt;: Summary statistics of the search and filtering process for the month.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This granular monthly structure allows researchers to trace the evolution of academic discourse on CEM/CRM and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (&lt;a href="https://dataverse.harvard.edu/dataverse/mgmt_crossref_metadata" target="_blank" rel="noopener noreferrer"&gt;Management Tool Bibliographic Metadata (Crossref)&lt;/a&gt;).&lt;/p&gt;
&lt;p&gt;Users interested in aggregated publication counts or trend analysis for CEM/CRM should consult the corresponding datasets in the &lt;a href="https://dataverse.harvard.edu/dataverse/mgmt_raw_data" target="_blank" rel="noopener noreferrer"&gt;Raw Extracts Dataverse&lt;/a&gt; and the &lt;a href="https://dataverse.harvard.edu/dataverse/mgmt_normalized_indices" target="_blank" rel="noopener noreferrer"&gt;Comparative Indices Dataverse&lt;/a&gt;.&lt;/p&gt;
description: <strong>Note on the Dataset: Management Tools Lifecycle Analysis</strong>
<strong>Overview and Context</strong><br>This dataset provides raw empirical data for the Management Tools Lifecycle Analysis project, an interdisciplinary study examining the longitudinal evolution of 23 management tool groups through bibliometric and statistical analysis of heterogeneous data sources: Google Trends, Google Books Ngram Viewer, Crossref.org, and Bain & Company’s usability and satisfaction surveys. Hosted at https://github.com/Wise-Connex/Management-Tools-Analysis/tree/main/Informes, the dataset underpins a series of 138 technical reports spanning over 20,000 pages of quantitative analysis, systematically characterizing patterns of adoption, diffusion, and decline of these tools within organizational contexts.
The project is framed within Diomar Anez’s doctoral research, titled "Ontological Dichotomy in 'Management Fads': A Perspective from the Paradoxes of the Transorganizational Ecosystem". This inquiry investigates the emergence, diffusion, and persistence/obsolescence of technological-administrative innovations—conceptualized as tools, techniques, philosophies, or management approaches exhibiting potentially cyclical trajectories. Moving beyond phenomenological descriptions of these cycles, the research probes their causal underpinnings, exploring onto-anthropological, microeconomic, and organizational dimensions. It posits that a management tool’s longevity depends not solely on its intrinsic efficacy but on its capacity to mediate inherent organizational paradoxes, such as stability versus innovation or continuity versus disruption.
<strong>Purpose and Contributions</strong><br>The dataset and associated technical reports aim to detect and characterize systematic patterns in the adoption and use of management tools, assessing whether their behavior aligns with the construct of “management fads” (marked by boom-and-bust cycles) or conforms to alternative models of diffusion and persistence. This exploratory and descriptive phase, grounded in quantitative methods, delivers an unprecedented empirical foundation in its scope and granularity, contributing to management sciences with a longitudinal data repository and advanced statistical analyses. These encompass time-series modeling (ARIMA), comparative trend analysis, trend means, cycle analysis, Fourier transform, correlation maps, linear regression, principal component analysis (PCA), and visualization of PC1 vs. PC2 loading plots, alongside trend visualizations that elucidate complex patterns.
Complementing this, the doctoral dissertation employs a qualitative approach to investigate underlying causal mechanisms and social processes, examining how contextual factors, power structures, social networks, and legitimation processes shape perceptions of a tool’s value and utility. The integration of quantitative (reports/data) and qualitative (dissertation) methodologies offers a holistic and dialectical understanding of management innovation dynamics, positioning the project as a novel contribution to the study of organizational paradoxes and innovation diffusion.
<strong>Transparency and Reproducibility</strong><br>The raw dataset is made publicly available to ensure transparency, enable reproducibility of the analyses presented in the technical reports, and foster further quantitative research by scholars, students, consultants, and practitioners interested in management tool trends, innovation diffusion, and strategic management. The technical reports provide descriptive and exploratory analyses, while the doctoral dissertation delves into qualitative factors driving tool adoption or abandonment, collectively laying a robust foundation for future research.
<strong>Authorship and Interdisciplinary Contributions</strong><br>The project is led by:

<strong>Diomar Anez (Principal Investigator):</strong> Doctoral candidate in Management Sciences, with training in Philosophy, Communication, Business Valuation, Financial Planning, and Economics, and over 25 years of experience in organizational consulting. Anez designed the conceptual framework, selected the management tools, developed the theoretical foundation, and provided interpretive insights within the context of organizational dynamics and management theory.
<strong>Dimar Anez (Data Analysis and Programming):</strong> With studies in Computer and Electronic Engineering, and expertise in data science, machine learning, and big data. Dimar led automated data extraction, preprocessing, cleaning, statistical modeling (including time-series analysis, linear regression, PCA, and forecasting techniques), and visualization, ensuring analytical rigor and scalability using Python and advanced software tools.