PapersFlow Research Brief
Organizational and Employee Performance
Research Guide
What is Organizational and Employee Performance?
Organizational and Employee Performance refers to the evaluation and enhancement of collective and individual productivity within organizations, often through knowledge sharing, motivational factors, and structural equation modeling techniques as applied in business research.
Research on Organizational and Employee Performance includes 41,760 works focusing on machine learning, IoT, and digital transformation applications in areas like supply chain management and knowledge management. Key methods involve Partial Least Squares Structural Equation Modeling (PLS-SEM) for analyzing relationships in business data, as detailed in "Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R" (2021). Studies examine factors such as extrinsic motivators and organizational climate influencing knowledge sharing intentions, with "Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Forces, and Organizational Climate1" (2005) receiving 4204 citations.
Topic Hierarchy
Research Sub-Topics
Machine Learning for Organizational Performance Prediction
Researchers apply supervised learning algorithms like random forests and neural networks to predict firm performance metrics from financial, operational, and employee data. Studies focus on feature engineering, model interpretability, and validation using longitudinal datasets.
Employee Knowledge Sharing Intentions
This area examines psychological and organizational factors influencing knowledge sharing using theory of planned behavior and social exchange theory via SEM analysis. Research investigates extrinsic rewards, trust, and climate effects on sharing behaviors in teams.
Digital Transformation Impact on Organizational Performance
Studies quantify how IoT, AI, and cloud adoption affect firm productivity, agility, and customer satisfaction through difference-in-differences and PLS-SEM analyses. Researchers explore moderating roles of organizational culture and industry contexts.
PLS-SEM in Organizational Research
This sub-topic advances partial least squares structural equation modeling for analyzing complex latent variable relationships in non-normal data typical of surveys. Focus includes composite model assessment, heteroscedasticity handling, and integration with machine learning.
Supply Chain Management with IoT and Data Analytics
Researchers develop predictive analytics frameworks using IoT sensor data for demand forecasting, inventory optimization, and disruption detection. Studies evaluate blockchain integration and real-time visibility impacts on supply chain resilience.
Why It Matters
Organizational and Employee Performance research impacts supply chain management through IoT integration, as shown in "Internet of things and supply chain management: a literature review" (2017) which reviews IoT enablers improving SCM processes. Knowledge sharing drives performance, with Bock et al. (2005) demonstrating how extrinsic motivators and organizational climate predict intentions, cited 4204 times and applied in knowledge repositories. Lin (2007) found extrinsic rewards reduce intrinsic motivation for sharing, affecting 1324-cited studies on employee behaviors in firms implementing digital tools.
Reading Guide
Where to Start
"Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R" (2021) by Hair et al., as it provides a foundational primer on statistical methods central to analyzing performance relationships in business research.
Key Papers Explained
"Research methods for business: A skill building approach" (1993) establishes core research skills, extended by Hair et al.'s "Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R" (2021) for advanced modeling applied in Bock et al.'s "Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Forces, and Organizational Climate1" (2005), which tests motivators empirically. Lin's "Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions" (2007) builds on Bock et al. by dissecting motivation types, while Ben-Daya et al.'s "Internet of things and supply chain management: a literature review" (2017) links digital tools to performance outcomes.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Current frontiers emphasize PLS-SEM applications in IoT-driven supply chains and motivation models for knowledge management, as seen in top-cited works like Gad's "Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review" (2022) for optimization in organizational contexts, with no recent preprints available.
Papers at a Glance
Frequently Asked Questions
What methods test validity in organizational performance surveys?
Validity and reliability of research instruments in organizational performance studies are tested through procedures outlined in "Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research" (2016). This involves statistical checks like factor analysis and Cronbach's alpha on survey data. Taherdoost (2016) provides steps ensuring questionnaire accuracy in business contexts.
How does PLS-SEM apply to employee performance analysis?
"Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R" (2021) by Hair et al. offers a primer for modeling complex relationships in organizational data. It uses R software to assess paths between variables like motivation and performance. The method suits predictive modeling in knowledge management studies.
What factors influence knowledge sharing in organizations?
Bock et al. (2005) in "Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Forces, and Organizational Climate1" identify extrinsic motivators, social-psychological forces, and organizational climate as key predictors. Individuals hoard knowledge without supportive climates. The study integrates these to explain sharing behaviors.
Why do extrinsic rewards affect employee knowledge sharing?
Lin (2007) in "Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions" shows extrinsic motivation negatively moderates intrinsic effects on sharing intentions. Rewards can undermine voluntary sharing in organizations. Empirical tests confirm this in Journal of Information Science.
How does IoT impact organizational supply chain performance?
"Internet of things and supply chain management: a literature review" (2017) by Ben-Daya et al. covers IoT technologies enhancing SCM processes like inventory tracking. It reviews applications improving organizational efficiency. IoT enablers directly boost performance metrics.
What role does organizational climate play in performance?
"Breaking the Myths of Rewards" (2002) by Bock and Kim examines how climate influences knowledge sharing beyond rewards. Managers introducing knowledge paradigms must address climate factors. The study reveals determinants of individual behaviors.
Open Research Questions
- ? How do extrinsic rewards interact with intrinsic motivation to affect long-term employee knowledge sharing in digital transformation contexts?
- ? What specific IoT enablers most improve supply chain performance metrics in organizations?
- ? How can PLS-SEM models integrate organizational climate and governance factors for better performance prediction?
- ? Which social-psychological forces best overcome knowledge hoarding in modern workplaces?
- ? What validation techniques optimize survey instruments for measuring corporate social performance under institutional ownership?
Recent Trends
The field spans 41,760 works with sustained interest in PLS-SEM via Hair et al. (2021, 6537 citations) and IoT in supply chains from Ben-Daya et al. (2017, 1327 citations), alongside motivation studies like Lin (2007, 1324 citations).
Recent high-citation growth appears in Gad (2022, 1498 citations) applying particle swarm optimization to performance challenges.
No preprints or news in the last 12 months indicate stable reliance on established methods.
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