Kogan Page AI for HR Review

Kogan Page AI for HR Review

Table of Contents

Are we investing enough in the future of our workforce? In an era where technology drives much of our daily operations, tools like “Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce Paperback – Import, 3 January 2022” emerge as essential resources. This interesting tome endeavors to guide human resources professionals through the intricate world of artificial intelligence, promising to transform the landscape of our HR practices.

Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce     Paperback – Import, 3 January 2022

Learn more about the Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce     Paperback – Import, 3 January 2022 here.

The Promise of AI in HR

AI’s integration into HR has been nothing short of revolutionary. If we’ve ever attended a meeting about streamlining operations, we know there’s always a buzz around artificial intelligence. Kogan Page’s book sheds light on how this can benefit HR departments specifically. By harnessing AI, we’re not just automating mundane tasks, but also empowering our teams to make data-driven decisions that can lead to a more productive work environment.

Understanding the Basics

Before jumping into AI’s potential, it’s crucial to familiarize ourselves with the fundamentals. The book is a savory blend of AI jargon and practical applications. Kogan Page does an admirable job simplifying complex terms, ensuring we’re not drowning in data science speak. This section acts as a gentle introduction—think of it as dipping our toes into the AI pool. We get to grasp the basic components of AI, such as machine learning and natural language processing, and how these elements play into HR analytics.

A Table of AI Applications in HR

Instead of recounting all information in a dense paragraph, let’s break it down:

AI Component Application in HR Potential Benefit
Machine Learning Predictive hiring Identifies candidates who are likely to thrive based on past data
Natural Language Processing Resume screening and chatbots Streamlines recruitment by filtering high-potential candidates efficiently
Data Analytics Employee performance analysis Enhances employee engagement by identifying strengths and areas of improvement
Automated Systems Onboarding and training Reduces onboarding time, ensuring new hires hit the ground running

Our takeaway here is simple: each facet of AI addresses an existing HR challenge, amplifying efficiency and precision.

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Challenges and Considerations

For every shining promise AI offers, there lurks a potential pitfall. As we explore AI’s HR applications, we need to be aware of these challenges. Kogan Page highlights the ethical considerations of AI—questions of privacy, bias, and the potential loss of human touch. Are we merely becoming another cog in a data-driven machine? These ethical pitfalls can be perilous, and the book prompts us to question our readiness to embrace this digital revolution while keeping the human element intact.

Ethical Dilemmas

The text takes care to reflect on biases embedded within AI systems. If the algorithms analyze data biased towards a particular outcome, our decision-making will be flawed. We can’t overlook how AI can replicate and even amplify existing biases. The book wisely suggests methods to counteract this, such as continually feeding AI systems diverse data sets and subjecting algorithms to regular ethical audits.

Privacy Concerns

Privacy in AI is like discussing attendance at a family reunion: everyone wants to know the details, but no one wants their own affairs revealed. The balance between data collection and employee privacy is precarious. Kogan Page is candid about these challenges, advising us on establishing transparent protocols around data use. The key? Ensuring employees are informed and able to trust the systems in place.

Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce     Paperback – Import, 3 January 2022

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Integrating AI into Our Current Systems

The book doesn’t just stop at highlighting what AI can do; it walks us through the integration process. Implementing AI in HR isn’t simply about pressing a button; it’s an art. Like trying to fit a square peg into a round hole, we need to smooth out the edges of our existing systems to allow for seamless integration.

Steps to Implementation

Kogan Page recommends a structured approach. Here are the suggested steps:

  1. Assessment of Current Systems: Analyze our existing HR processes to identify areas where AI can add value.
  2. Pilot Programs: Start small by implementing AI in one part of our HR operations.
  3. Feedback Loop and Adjustments: Collect feedback from test runs and refine the system accordingly.
  4. Training and Development: Equip our HR team with the knowledge and skills to work alongside AI technologies.
  5. Full-scale Rollout: Once we’re confident in the system, go live and monitor closely for continuous improvement.

Each step requires deliberate planning and collaboration, ensuring no one’s left behind in this digital evolution.

Training and Development

We know the importance of upskilling, and this theme echoes throughout the book. As AI takes over more administrative tasks, human skills like emotional intelligence and strategic planning become paramount. Kogan Page emphasizes training our teams to complement AI tools, not compete with them. This isn’t about replacement; it’s about augmentation.

The Future Landscape

As we peruse through the book, it becomes evident that the potential of AI in HR is boundless. This isn’t just about today’s workforce; it’s about the landscape of tomorrow. We need not just anticipate change, but actively shape it.

Prospective Innovations

Kogan Page tantalizes us with glimpses of future possibilities. Imagine personalized professional development plans powered by AI, where each employee receives tailored recommendations based on their performance and interests. Or envision AI systems predicting potential workplace conflicts and suggesting proactive resolutions before tensions escalate. The future is painted with opportunities for proactive and precise personnel management.

Reimagining Workforce Strategies

AI is not a one-size-fits-all solution, but it does invite us to consider new strategies for workforce management. Whether it’s remote work planning, diversity initiatives, or global talent acquisition, AI provides the tools to rethink our previous approaches. The book nudges us toward a future where strategic decisions are informed by a blend of human insight and AI precision.

Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce     Paperback – Import, 3 January 2022

Final Reflections

As we turn the final pages of “Kogan Page Artificial Intelligence for HR,” it’s apparent this isn’t just a manual—it’s a call to action. It encourages us to not only embrace technology but to do so thoughtfully and ethically. There’s a responsibility to remain vigilant, ensuring our AI endeavors reflect our organization’s values and enhance the human experience.

In exploring the book, we’re reminded of our power to drive transformation and the importance of balance. Just as we don’t want AI overshadowing the human aspect of HR, we also don’t want to resist technological advancements out of fear. The secret lies in harmonizing the two. With Kogan Page’s guidance, the path forward is not just clearer but also a bit more welcoming.

Learn more about the Kogan Page Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce     Paperback – Import, 3 January 2022 here.

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University Student Essentials
University Student Essentials

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (= 0.87, < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (= 0.02, = .423). Additionally, CSE (= 0.02, = .423), PC (= 0.05, = .334), ATE (= 0.00, = .983), and PP (= 0.03, = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (= 0.22, < .001) and RES (= 0.21, < .001). The results also indicated that PC was significantly correlated with ATE (= -0.79, < .001), AXY (= 0.18, < .001), and RES (= 0.20, < .001). ATE was significantly correlated with AXY (= -0.19, < .001) and RES (= -0.19, < .001). AXY was significantly correlated with RES (= 0.85, < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, < .001), ATE (F(7,194) = 2.20, = .036), Years computers (F(1,194) = 5.71, = .018), and PC (F(12,194) = 2.00, = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.