DOI: 10.7910/dvn/vw7aax
title: Benchmarking (Normalized)

description: &lt;p&gt;This dataset provides processed and normalized/standardized indices for the management tool &lt;b&gt;'Benchmarking'&lt;/b&gt;. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption).&lt;/p&gt;

&lt;p&gt;The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Benchmarking dataset in the &lt;a href="https://dataverse.harvard.edu/dataverse/mgmt_raw_data" target="_blank" rel="noopener noreferrer"&gt;Management Tool Source Data (Raw Extracts)&lt;/a&gt; Dataverse.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;Data Files and Processing Methodologies:&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;b&gt;Google Trends File (Prefix: &lt;code&gt;GT_&lt;/code&gt;): Normalized Relative Search Interest (RSI)&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;i&gt;Input Data:&lt;/i&gt; Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query &lt;code&gt;"benchmarking" + "benchmarking management"&lt;/code&gt;.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Processing:&lt;/i&gt; None. Utilizes the original base-100 normalized Google Trends index.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Output Metric:&lt;/i&gt; Monthly Normalized RSI (Base 100).&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Frequency:&lt;/i&gt; Monthly.&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Google Books Ngram Viewer File (Prefix: &lt;code&gt;GB_&lt;/code&gt;): Normalized Relative Frequency&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;i&gt;Input Data:&lt;/i&gt; Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query &lt;code&gt;Benchmarking&lt;/code&gt;.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Processing:&lt;/i&gt;
 &lt;ol&gt;
 &lt;li&gt;Annual relative frequency series normalized (peak year = 100).&lt;/li&gt;
 &lt;/ol&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Output Metric:&lt;/i&gt; Annual Normalized Relative Frequency Index (Base 100).&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Frequency:&lt;/i&gt; Annual.&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Crossref.org File (Prefix: &lt;code&gt;CR_&lt;/code&gt;): Normalized Relative Publication Share Index&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;i&gt;Input Data:&lt;/i&gt; Absolute monthly publication counts matching Benchmarking-related keywords [&lt;code&gt;"benchmarking" AND (...)&lt;/code&gt; - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Processing:&lt;/i&gt;
 &lt;ol&gt;
 &lt;li&gt;Monthly relative share calculated (Benchmarking Count / Total Count).&lt;/li&gt;
 &lt;li&gt;Monthly relative share series normalized (peak month's share = 100).&lt;/li&gt;
 &lt;/ol&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Output Metric:&lt;/i&gt; Monthly Normalized Relative Publication Share Index (Base 100).&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Frequency:&lt;/i&gt; Monthly.&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Bain & Co. Survey - Usability File (Prefix: &lt;code&gt;BU_&lt;/code&gt;): Normalized Usability Index&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;i&gt;Input Data:&lt;/i&gt; Original usability percentages (%) from Bain surveys for specific years: Benchmarking (1993, 1996, 1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported in 2022 survey data.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Processing:&lt;/i&gt;
 &lt;ol&gt;
 &lt;li&gt;Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100).&lt;/li&gt;
 &lt;/ol&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Output Metric:&lt;/i&gt; Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak).&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Frequency:&lt;/i&gt; Biennial (Approx.).&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Bain & Co. Survey - Satisfaction File (Prefix: &lt;code&gt;BS_&lt;/code&gt;): Standardized Satisfaction Index&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;i&gt;Input Data:&lt;/i&gt; Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Benchmarking (1993-2017). Note: Not reported in 2022 survey data.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Processing:&lt;/i&gt;
 &lt;ol&gt;
 &lt;li&gt;Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609.&lt;/li&gt;
 &lt;li&gt;Index Scale Transformation: Index = 50 + (Z * 22).&lt;/li&gt;
 &lt;/ol&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Output Metric:&lt;/i&gt; Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]).&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;Frequency:&lt;/i&gt; Biennial (Approx.).&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;b&gt;File Naming Convention:&lt;/b&gt; Files generally follow the pattern: &lt;code&gt;PREFIX_Tool_Processed.csv&lt;/code&gt; or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_).&lt;/p&gt;

&lt;p&gt;Consult the parent Dataverse description (&lt;a href="https://dataverse.harvard.edu/dataverse/management-fads/mgmt_normalized_indices" target="_blank" rel="noopener noreferrer"&gt;Management Tool Comparative Indices&lt;/a&gt;) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Benchmarking dataset 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;. Comprehensive project documentation provides full details on all processing steps.&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 Añez’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 Añez (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. Añez 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 Añez (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.