library / lib0103d01ed1adc3d5
Predictive HR Analytics, Text Mining Organizational Network Analysis with Excel
Mong Shen Ng
In a sentence
A practical, do-it-yourself guide showing HR professionals how to run predictive analytics, text mining, sentiment analysis, and organizational network analysis entirely in Microsoft Excel to drive better business decisions.
This is the only book that teaches Predictive HR Analytics, Text Mining, and Organizational Network Analysis using tools you already own and know—Microsoft Excel and free add-ins—without months of learning R or buying expensive SPSS software. Through step-by-step print-screen instructions, it walks you from defining a business problem through the ARHAT framework, gathering and analyzing data with decision trees, correlation, multiple and logistic regression, mining unstructured text into word clouds and sentiment scores, and mapping employees' social networks into measurable centrality metrics. Packed with real-world case studies (Best Buy, Nielsen, Xerox, HP, Hilton, JetBlue) and dozens of HR metrics, it shows you how to predict attrition, performance, engagement's impact on sales, diversity's impact on EBIT, and workplace accidents—and crucially, how to translate those findings into an engaging data story that drives change.
The four lenses
- Science
- Statistics
- Systems
- Strategy
Tags
The model
A causal model in which HR design levers and contextual conditions (compensation, training, diversity practices, network position, demographics, sentiment) influence psychological and behavioral states (engagement, job satisfaction, flight risk) that in turn drive organizational outcome metrics (attrition, performance, sales, EBIT, safety incidents). The analytics process itself (ARHAT framework + storytelling) is the meta-lever that surfaces these relationships.
Employee Engagementpsychological state
The degree of emotional commitment, enthusiasm, and discretionary effort employees invest in their work and organization, measured through engagement surveys and net promoter scores.
Compensation Competitivenessdesign lever
How an employee's pay compares to the external market and internal salary ranges, expressed through market-ratio, compa-ratio, and pay-spread metrics; a key design lever affecting retention and performance.
Training & Development Investmentdesign lever
The provision, quality, and perceived effectiveness of learning and development opportunities offered to employees, including training satisfaction, knowledge gained, and on-the-job application.
Diversity & Inclusiondesign lever
The composition of the workforce across ethnicity, gender, and other traits (quantified via Simpson's Diversity Index) combined with inclusive practices that give employees equal access to opportunities.
Organizational Network Positionbehavioral pattern
An employee's structural position in the organizational social network, quantified through centrality metrics (degree, betweenness, closeness, eigenvector) and network reach, immersion, and diversity.
Employee Demographics & Contextcontextual condition
Background and situational characteristics of employees including age, tenure, marital status, gender, commute time, and triggering life events that condition retention and accident likelihood.
Personality Traitscontextual condition
Stable individual dispositions such as conscientiousness, extraversion, agreeableness, creativity, and grit measured through personality assessments and used to predict performance and retention.
Employee Sentimentpsychological state
The polarity (positive, negative, neutral) of opinions employees and candidates express in text such as Glassdoor reviews, survey comments, and social posts, scored through sentiment analysis.
Employee Flight Riskpsychological state
The estimated probability that an employee will voluntarily resign, derived from combinations of low pay relative to market, high performance, demographics, network position, and behavioral signs.
Employee Attrition & Absenteeismoutcome metric
The actual rate at which employees leave the organization (voluntary turnover) and the frequency of unplanned absence, key outcome metrics the book repeatedly predicts.
Employee & Sales Performanceoutcome metric
The output and effectiveness of employees including performance ratings, sales revenue, customer service quality, and productivity, treated as a central outcome predicted from multiple drivers.
Business Financial Outcomesoutcome metric
Organization-level financial and operational results such as EBIT, profit margin, revenue, market share, total shareholder return, and customer satisfaction influenced by HR states and practices.
Workplace Safety Incidentsoutcome metric
The frequency of workplace accidents, injuries, and recordable safety cases, an outcome metric predicted from engagement, demographics, tenure, and earnings pressure.
Analytics & Data Storytelling Processdesign lever
The structured ARHAT framework and data-storytelling practice (data, visuals, narrative) through which HR surfaces relationships and communicates them to stakeholders to drive change.
How they connect
- employee engagement → predicts employee performance
- employee engagement − predicts employee attrition
- employee engagement − predicts safety incidents
- employee engagement → predicts business financial outcomes
- compensation competitiveness − predicts flight risk
- compensation competitiveness → influences employee performance
- training development → predicts employee performance
- training development − predicts employee attrition
- diversity inclusion → predicts business financial outcomes
- diversity inclusion − predicts employee attrition
- network position → predicts employee performance
- network position − predicts flight risk
- employee demographics → moderates employee attrition
- employee demographics → predicts safety incidents
- personality traits → predicts employee performance
- personality traits − predicts flight risk
- employee sentiment − predicts employee attrition
- flight risk → predicts employee attrition
- employee attrition − predicts business financial outcomes
- employee performance → predicts business financial outcomes
- analytics storytelling process → moderates business financial outcomes
The process
The book's overall operating playbook is a structured, five-step methodology called the ARHAT Predictive HR Analytics Framework, designed to guide HR professionals from identifying a business problem to delivering actionable, data-driven recommendations. This playbook begins with foundational project management steps, including stakeholder analysis and project prioritization, to ensure business alignment and support. Once a project is defined, the practitioner follows the ARHAT framework: Ask critical business questions, Review existing literature to avoid reinventing the wheel, formulate a testable Hypothesis, Analyze data, and finally, Tell a compelling story with the findings. The 'Analyze Data' phase is the technical core of the playbook, where the book provides a suite of practical, step-by-step processes primarily using Microsoft Excel and free add-ins. These processes enable the practitioner to perform various types of analysis, including correlation to identify relationships, multiple and logistic regression to build predictive models, text mining to quantify unstructured feedback, sentiment analysis to gauge opinion, and Organizational Network Analysis (ONA) to map informal structures and influence. The final 'Tell the Story' phase leverages specific data visualization techniques, also taught within Excel, to translate complex analytical outputs into clear, engaging narratives that drive change. In essence, the playbook equips HR professionals to systematically tackle business challenges, test hypotheses with robust data analysis using accessible tools, and communicate insights effectively to influence strategic decisions. It provides a complete, end-to-end workflow for transforming HR from a support function into a strategic, predictive partner within the organization.
HR Analytics Project Lifecycle (ARHAT Framework)
To provide a structured, end-to-end framework for conducting a predictive HR analytics project, from initial problem definition to delivering actionable recommendations and measuring impact.
When to use: When initiating any new HR analytics project to address a business challenge like high attrition, low performance, or poor engagement.
Step 1Conduct stakeholder analysis.
Entry: A potential business problem has been identified.
Exit: Key stakeholders are identified, mapped, and an engagement strategy is defined for each.
In: Business problem · Out: Stakeholder map, Stakeholder engagement strategy
Step 2Prioritize the analytics project.
Entry: One or more potential analytics projects have been proposed.
Exit: A specific, high-priority project is selected for execution.
- Decide whether a project is a Quick Win, Major Project, Fill-In, or Thankless Task.
In: List of potential HR analytics projects · Out: Prioritized project selection
Step 3Ask questions to define the business problem.
Entry: A project has been prioritized.
Exit: A well-defined business question and project scope are agreed upon with the sponsor.
In: Prioritized project idea · Out: Defined business question, Project scope document
Step 4Review existing literature.
Entry: The business question is defined.
Exit: A summary of existing knowledge and approaches relevant to the problem is compiled.
In: Defined business question · Out: Literature review summary
Step 5Formulate a testable hypothesis.
Entry: Literature review is complete.
Exit: A testable hypothesis is formulated and approved.
In: Business question, Literature review summary · Out: Approved hypothesis
Step 6Gather and analyze data.
Entry: A testable hypothesis has been formulated.
Exit: The hypothesis has been tested and key insights are generated from the data.
- Choose the appropriate statistical analysis method (e.g., correlation, regression, ONA).
In: Approved hypothesis, Raw data from various sources · Out: Cleaned dataset, Analysis results, Key insights
Step 7Tell the story with data.
Entry: Data analysis is complete and insights have been generated.
Exit: Recommendations are presented to and understood by stakeholders, with a clear call to action.
In: Key insights, Analysis results · Out: Presentation deck, Actionable recommendations
Perform Multiple Regression Analysis in Excel
To build a predictive model that explains the relationship between multiple independent variables (inputs) and a single dependent variable (output), and to forecast future outcomes.
When to use: During the 'Analyze Data' step of an analytics project, when the hypothesis involves predicting a continuous variable based on multiple factors.
Step 1Enable the Analysis ToolPak add-in.
Entry: Microsoft Excel is open.
Exit: The 'Data Analysis' button is visible on the Data tab.
Out: Enabled Analysis ToolPak
Step 2Prepare the data.
Entry: A clean dataset is available.
Exit: Data is formatted correctly for regression analysis, with all variables being numerical.
In: Raw dataset · Out: Formatted dataset with dummy variables
Step 3Run the regression analysis.
Entry: Data is prepared and Analysis ToolPak is enabled.
Exit: Excel generates the regression summary output.
In: Formatted dataset · Out: Regression summary output table
Step 4Interpret the results.
Entry: Regression summary output is generated.
Exit: The model's validity is confirmed and the predictive formula is derived.
- Determine which variables are statistically significant predictors based on their P-values.
In: Regression summary output table · Out: Interpretation of model fit and significance, Predictive regression equation
Perform Logistic Regression Analysis in Excel
To predict the probability of a binary, categorical outcome (e.g., resign/stay, buy/don't buy) based on one or more independent variables.
When to use: During the 'Analyze Data' step when the hypothesis involves predicting a 'yes/no' or binary outcome.
Step 1Enable the Solver add-in.
Entry: Microsoft Excel is open.
Exit: The 'Solver' button is visible on the Data tab.
Out: Enabled Solver add-in
Step 2Prepare the data and coefficient cells.
Entry: A clean dataset is available.
Exit: Data is organized and initial coefficient cells are set up.
In: Raw dataset · Out: Formatted data table, Initialized coefficient cells
Step 3Calculate Logit, Probability P(X), and Log-Likelihood (LL).
Entry: Data and coefficients are set up.
Exit: Logit, P(X), and LL values are calculated for every row of data.
In: Formatted data table, Initialized coefficients · Out: Completed calculation columns
Step 4Use Solver to find optimal coefficients.
Entry: All calculation columns are complete.
Exit: Solver finds the optimal coefficient values that maximize the log-likelihood function.
In: Sum of Log-Likelihood, Initialized coefficient cells · Out: Optimized coefficient values
Step 5Use the model to make predictions.
Entry: Optimal coefficients have been determined by Solver.
Exit: A prediction of probability is made for a new set of inputs.
In: Optimized coefficients, New input data · Out: Predicted probability of the outcome
Perform Correlation Analysis in Excel
To measure the strength and direction of the linear relationship between two or more numerical variables.
When to use: During the 'Analyze Data' step to quickly identify which variables are related to an outcome of interest, often as a preliminary step before regression analysis.
Step 1Enable the Analysis ToolPak add-in.
Entry: Microsoft Excel is open.
Exit: The 'Data Analysis' button is visible on the Data tab.
Out: Enabled Analysis ToolPak
Step 2Prepare the data.
Entry: A clean dataset with numerical variables is available.
Exit: Data is formatted in a contiguous block for analysis.
In: Raw dataset · Out: Formatted data table
Step 3Run the correlation analysis.
Entry: Data is prepared and Analysis ToolPak is enabled.
Exit: Excel generates a correlation matrix.
In: Formatted data table · Out: Correlation matrix
Step 4Interpret the correlation matrix.
Entry: A correlation matrix has been generated.
Exit: Significant relationships between variables have been identified.
In: Correlation matrix · Out: List of significantly correlated variable pairs
Conduct Text Frequency Analysis
To count the occurrences of each word in a body of unstructured text to identify the most frequently mentioned topics or concepts.
When to use: As a first step in text mining to get a high-level understanding of what topics are most prominent in a collection of text.
Step 1Enable the Developer tab in Microsoft Word.
Entry: Microsoft Word is open with the text to be analyzed.
Exit: The Developer tab is visible in the Word ribbon.
Step 2Insert and run the Word Frequency VBA macro.
Entry: The text to be analyzed is in a Word document and the Developer tab is enabled.
Exit: A new Word document is generated containing a list of all unique words and their frequency counts.
In: Unstructured text, VBA macro code · Out: List of words and their frequencies
Step 3Format and sort the results.
Entry: The word frequency list has been generated.
Exit: A sorted table showing the most frequently used words is created.
In: List of words and their frequencies · Out: Sorted table of word frequencies
Visualize Text with a Word Cloud
To create a simple, impactful visual representation of text data where the size of each word indicates its frequency or importance.
When to use: In the 'Tell the Story' phase of an analytics project to present findings from text analysis in an easily digestible format.
Step 1Install the 'Pro Word Cloud' add-in in Microsoft Word.
Entry: Microsoft Word is open.
Exit: The 'Pro Word Cloud' panel appears on the right side of the Word document.
Out: Installed 'Pro Word Cloud' add-in
Step 2Generate the word cloud.
Entry: The text is available in Word and the add-in is installed.
Exit: A word cloud is generated in the add-in panel.
In: Unstructured text · Out: Word cloud visualization
Step 3Format and use the word cloud.
Entry: A word cloud has been generated.
Exit: A formatted word cloud is ready for use in a report or presentation.
In: Generated word cloud · Out: Formatted word cloud image
Perform Sentiment Analysis in Excel
To automatically categorize unstructured text (e.g., survey comments, reviews) as positive, negative, or neutral and assign a sentiment score.
When to use: During the 'Analyze Data' step to add a layer of qualitative insight to text data, moving beyond simple frequency counts.
Step 1Install the 'Azure Machine Learning' add-in in Excel.
Entry: Microsoft Excel is open.
Exit: The 'Azure Machine Learning' panel appears on the right side of the Excel sheet.
Out: Installed 'Azure Machine Learning' add-in
Step 2Prepare the data.
Entry: Unstructured text data is available.
Exit: Data is in a single column with the header 'tweet_text'.
In: List of text comments · Out: Formatted data column
Step 3Run the sentiment analysis.
Entry: Data is prepared and the add-in is open.
Exit: Two new columns, 'Sentiment' (Positive/Negative/Neutral) and 'Score' (0 to 1), are generated next to the input data.
In: Formatted data column · Out: Sentiment classification for each comment, Sentiment score for each comment
Step 4Analyze the results.
Entry: Sentiment and Score columns have been generated.
Exit: Insights are drawn from the sentiment analysis results.
In: Sentiment and Score columns · Out: Summary of overall sentiment
Visualize and Analyze an Organizational Network with NodeXL
To map and analyze the formal and informal relationships within an organization to identify key influencers, communication flows, and structural patterns.
When to use: During the 'Analyze Data' step to understand collaboration, influence, and information flow that is not visible on a formal organization chart.
Step 1Install and open the NodeXL Basic Excel Template.
Entry: Microsoft Excel is installed.
Exit: The NodeXL template is open, showing 'Edges' and 'Vertices' worksheets and a graph pane.
Out: Open NodeXL template
Step 2Enter the network data (edge list).
Entry: Relationship data has been collected (e.g., from a survey).
Exit: A complete edge list is entered into the 'Edges' worksheet.
In: List of relationships · Out: Completed edge list
Step 3Generate and format the network graph.
Entry: The edge list is complete.
Exit: A formatted network graph is displayed in the graph pane.
In: Completed edge list · Out: Network graph visualization
Step 4Calculate graph metrics.
Entry: A network graph has been generated.
Exit: Network metrics are calculated and populated in new columns in the 'Vertices' worksheet.
In: Network graph · Out: Calculated graph metrics (Degree, Betweenness, Closeness, etc.)
Step 5Interpret the graph metrics.
Entry: Graph metrics have been calculated.
Exit: Key influencers, brokers, and peripheral individuals in the network are identified.
In: Calculated graph metrics · Out: Analysis of the network structure and key players
Create a Chart for Data Visualization in Excel
To create a visual representation of data in Excel to identify trends, compare values, and communicate findings more effectively than a table of numbers.
When to use: During the 'Analyze Data' and 'Tell the Story' phases of an analytics project to explore data and present results.
Step 1Select the data for the chart.
Entry: Data is organized in an Excel worksheet.
Exit: The relevant data range is selected.
In: Formatted data table
Step 2Insert the chart.
Entry: Data has been selected.
Exit: A default chart is inserted into the worksheet.
- Choose the appropriate chart type based on the data and the message you want to convey.
In: Selected data range · Out: Excel chart
Step 3Customize and format the chart.
Entry: A chart has been inserted.
Exit: The chart is clear, easy to read, and effectively communicates the key message.
In: Default Excel chart · Out: Formatted, presentation-ready chart
A candidate measure
Predictive HR Analytics, Text Mining Organizational Network Analysis with Excel — derived measurement candidates
Employee Engagement
Engagement index %; eNPS score; Promoter/detractor percentages
self-report suitability: high
Compensation Competitiveness
Market-ratio; Compa-ratio; Pay spread (std dev of merit increase)
self-report suitability: low
Training & Development Investment
Reaction scores; Pre/post test gains; Training hours per FTE; ROI %
self-report suitability: high
Diversity & Inclusion
Simpson's Diversity Index; Female percent; Diversity percentage; Inclusion survey ratings
self-report suitability: medium
Organizational Network Position
Degree centrality; Betweenness centrality; Closeness centrality; Eigenvector centrality
self-report suitability: medium
Employee Demographics & Context
Age; Tenure; Marital status; Commute time (minutes); Triggering event flag
self-report suitability: high
Personality Traits
Conscientiousness score; Extraversion score; Agreeableness score; Grit/creativity score
self-report suitability: high
Employee Sentiment
Sentiment score (0-100%); Polarized word counts; Glassdoor star rating
self-report suitability: medium
Employee Flight Risk
Predicted resignation probability; Flight risk score by group
self-report suitability: low
Employee Attrition & Absenteeism
Voluntary turnover rate; Absenteeism rate; First-year resignation rate
self-report suitability: none
Employee & Sales Performance
Performance rating; Sales revenue; Customer service score; Output per FTE
self-report suitability: low
Business Financial Outcomes
EBIT; Profit margin; Revenue; Market share; TSR; Customer satisfaction index
self-report suitability: none
Workplace Safety Incidents
Accident count; Injury rate; Recordable case frequency
self-report suitability: none
Analytics & Data Storytelling Process
Recommendation adoption rate; Stakeholder buy-in; Project impact measures
self-report suitability: medium
The story
The reader An HR or people analytics professional who wants to deliver data-driven recommendations that improve business performance and establish credibility with executives.
External problem
They need to run predictive analytics, mine text, and analyze networks but lack expensive software, programming skills, and a structured method.
Internal problem
They feel intimidated by statistics and machine learning and fear their analytics won't be trusted or won't drive change.
Philosophical problem
HR shouldn't be relegated to a cost center reporting what happened; it deserves to influence strategy with fact-based prediction—just like Finance and Sales.
The plan
- Learn the basics of machine learning, statistics, and the analytics maturity model
- Apply the five-step ARHAT framework to a real, sponsor-backed business problem
- Install free Excel add-ins (Analysis ToolPak, Solver, NodeXL, Azure ML) following step-by-step instructions
- Run decision trees, correlation, regression, logistic regression, text mining, sentiment analysis, and ONA
- Translate findings into a data story with narrative and visuals to drive stakeholder action
Success
- You predict attrition, performance, and engagement impact with confidence
- You uncover actionable insights from text and social networks
- You tell compelling data stories that win executive approval and drive change
- You become a trusted, strategic, analytically driven HR leader
At stake
- Your analytics projects stall for lack of a strong sponsor or data access
- Your insights get ignored because you couldn't tell an engaging story
- HR remains a cost center, losing influence and missing opportunities to retain talent and improve results
Chapter by chapter
ch01Chapter 1
This chapter introduces the fundamentals of predictive HR analytics, emphasizing its significant impact on organizational effectiveness through practical tools like Excel.
ch02Chapter 2
This chapter delineates different types of machine learning — supervised, unsupervised, and reinforcement learning — and explores their practical applications, challenges, and analytical maturity, particularly within the realm of HR analytics.
ch03Chapter 3
This chapter articulates the critical nature of implementing a structured predictive analytics framework in HR to address varied organizational challenges effectively.
ch04Chapter 4
This chapter emphasizes the importance of clear, engaging visual communication in presentations, offering strategies to enhance viewer understanding and retention.
ch05Chapter 5
This chapter explores the principles and applications of statistical analysis, highlighting the importance of visualizing data relationships to make informed decisions.
ch06Chapter 6
This chapter delves into the practical application of regression analysis in Excel, providing detailed guidance on using the software for both multiple linear regression and logistic regression to derive insights from data.
ch07Chapter 7
This chapter explores the profound impact of sentiment analysis on employer branding, employee selection, and retention, arguing that understanding and analyzing employee sentiment can lead to significant improvements in workforce engagement and company performance.
- A one-star increase in Glassdoor ratings leads to a statistically significant increase in the likelihood of employee retention.
- Companies with poor employer brands often overpay salaries by 10% due to weaker attractiveness to potential hires.
- Engaged employees are significantly less likely to resign, with studies showing an 87% drop in resignation rates among highly engaged staff.
- Concrete steps in sentiment analysis can lead to actionable changes that improve employee satisfaction and retention metrics.
ch08Chapter 8
This chapter explores the Employee Net Promoter Score (eNPS) as a pivotal metric for measuring employee engagement and morale, alongside the transformative impact of flexible work arrangements on organizational productivity.
ch09Chapter 9
This chapter explores how Organizational Network Analysis (ONA) can be leveraged to improve employee retention, optimize team performance, and enhance decision-making processes within organizations.
- Organizational Network Analysis (ONA) provides deeper insights into employee connections than traditional metrics.
- Employees who are well-connected exhibit greater loyalty and are less likely to leave the organization.
- Strong, quality connections within an organization are more valuable than sheer quantity, as excessive networking can hinder performance.
- Understanding informal relational dynamics is critical for effective onboarding, retention, and performance improvement strategies.
ch10Chapter 10
This chapter provides a detailed guide on utilizing logistic regression and multiple regression analysis to predict employee behaviors, specifically their likelihood to resign and impact on sales, using Excel as the primary tool for calculation.
ch11Chapter 11
Chapter 11 details the analytical processes required to assess employee diversity through data and its correlation with performance, focusing on techniques such as the Simpson’s Diversity Index and regression analysis in Excel.
- Raw diversity metrics are insufficient; organizations must transition to quantitative diversity indices for a nuanced understanding.
- The Social Network Diversity Index, derived from the Simpson's Diversity Index, provides a more comprehensive view of diversity in workplace contexts.
- High R Square values from regression analyses indicate strong correlations between diversity indices and employee performance ratings.
- Statistical significance in findings (e.g., P-values under 0.05) is essential for relying on analytics in decision-making.
ch12Chapter 12
This chapter explores the application of predictive analytics in human resources, detailing how organizations can leverage data to predict employee turnover and enhance performance metrics.
ch13Chapter 13
This chapter examines the intricate relationships between personality traits, employee performance, and organizational profitability, emphasizing how strategic hiring and internal networking can mitigate turnover and bolster productivity.
ch14Chapter 14
The chapter examines the critical role of compensation and benefits in employee satisfaction and retention, illustrating how organizations can enhance performance and reduce turnover by strategically investing in their workforce.
- 80% of American workers prefer benefits over higher salaries, emphasizing the importance of a comprehensive benefits package in recruitment and retention.
- Walmart’s pay increase led to a significant recovery in sales and customer service metrics, demonstrating the tangible benefits of investing in employees.
- Research shows that a connection with team members is often more crucial to job satisfaction than direct supervision or salary.
- The strategic use of Market-Ratio and Compa-Ratio Analytics can help organizations maintain fairness in compensation and drive retention.
ch15Chapter 15
This chapter demonstrates how to perform multiple linear regression analysis in Excel to evaluate the impact of training programs on customer service ratings, offering a step-by-step guide for working professionals.
ch16Chapter 16
This chapter delves into a comprehensive guide on creating various types of charts in Excel, emphasizing their applications in data analysis, visualization, and effective presentation.
Questions this book answers
- How can HR professionals run predictive analytics without expensive software or programming?
- Which employees are most likely to leave, and what drives their flight risk?
- How does employee engagement impact business outcomes like sales, profit, and customer satisfaction?
- How can unstructured text (reviews, surveys, social posts) be mined and scored for sentiment?
- How does an employee's organizational network position predict their performance and retention?
Glossary
- Employee Engagement
- The emotional commitment and discretionary effort employees give to their work and organization.
- Compensation Competitiveness
- How an employee's pay positions against external market and internal salary ranges.
- Training & Development Investment
- The extent and effectiveness of learning opportunities provided to employees.
- Diversity & Inclusion
- Workforce compositional variety plus inclusive practices giving equal opportunity.
- Organizational Network Position
- An employee's structural importance within the organization's social network.
- Employee Demographics & Context
- Background and situational attributes conditioning employee behavior.
- Personality Traits
- Stable dispositions predicting work behavior and performance.
- Employee Sentiment
- The polarity of opinions expressed in employee and candidate text.
Related in the library
- People Analytics & Text Mining with R
- 12_ The Elements of Great Managing
- Armstrong's Handbook of Strategic Human Resource Management
- First, Break All the Rules_ What the World_s Greatest Managers Do Differently
- Fundamentals Hrm Bauer
- Goal Setting & Team Management with OKR - Objectives and Key Results_ Skills for Effective Office Leadership, Smart Business Focus, & Growth. How to Manage Projects, People & Employees. 2nd Edition
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