HR Machine Learning on Social Media Data

Human resource departments gather data from various sources to help make effective decisions related to recruitment, selection, strategic planning, and employee management. Social media profiles (e.g., Facebook, Twitter, LinkedIn) are one recent source that many companies are turning to in order to gather unique information about current and prospective employees. The increasing abilities of artificial intelligence and machine learning may have an impact on the ease and validity of using social media information from applicants and employees to make employment decisions. In this research, we use previously published scholarly and trade sources to explain how HR personnel could use social media data in conjunction with machine learning to recruit, select, monitor, and communicate with potential or current employees. We further discuss associated ethical and legal risks and provide guidance for future research on this topic.https://digitalcommons.usu.edu/fsrs2020/1025/thumbnail.jp

Is Anybody Listening? How Leader Humility Promotes Employee Voice

Learning from employees on the front lines is critical for organizations’ ability to adapt, change, and learn. Leaders can play a crucial role in enabling and encouraging employees to express their ideas about how their organization can grow and improve. However, not all leaders seem to be successful at promoting employees’ desire to offer their ideas. We present a research study in this paper that demonstrates how leaders who are humble effectively promote voice. Furthermore, we show that when employees express voice, the contribution they make is recognized by their supervisors and they are more likely to be seen as good performers. We discuss the practical implications of this research and offer important ways leaders can promote and encourage the implementation of voice. Presentation Time: Wednesday, 11 a.m.-12 p.m. Zoom link: https://usu-edu.zoom.us/j/85282214929?pwd=Yld6WTgvSG01QmFTK3dTSHlDamZtUT0

GMO Products and the Food Industry: A Literature Review of Opinions and Behaviors by U.S. Restaurants

The health implications of genetically modified organisms (GMOs) have been the subject of considerable debate in the literature. One aspect of the debate is the apparent disconnect between what is reported in the scientific communities and what is reported in the general public. Specifically, it has been reported that while “Nearly 9 out of 10 scientists from the American Association for the Advancement of Science say GMOs are ‘generally safe’ to eat, more than half of the general public believe it is not a good idea.” This presents somewhat of a dilemma for companies, such as restaurants, who are involved in food distribution to the general public. That is, should restaurants continue to use GMO food products given assurances from the scientific community? Or, should they respond to consumer concerns by adopting at least some form of a non-GMO policy? The purpose of this review is to gain a conceptual understanding of what the food industry is saying and doing with regard to these questions. Preliminary results are explained and further research direction is given

Structuring the Social Media Assessment During the Hiring Process

Social media assessments (SMAs) are becoming a common practice during the hiring process, but little research existing that examines how to best structure SMAs. Drawing from Campion et al.’s (1997) components of interview structure and including additional components specific to the SMA procedure, a framework of eight components of SMA structure that should be considered when conducting SMAs in research and/or practice has been developed. Those components include (1) job-related specificity, (2) procedural consistency, (3) measurements used, (4) documentation, (5) assessor training, (6) separate rater(s) than decision maker(s), (7) informed consent, and (8) notification of results. We define each of these components, and outline how they can be measured in terms of three levels of structure: low, medium, and high. It is often assumed that SMAs are not consistently applied in practice and that there is little structure inherent in the process, but no research to date has tested this assumption. A content analysis was performed which examined nine academic studies and rated them in terms of the eight components on the low, medium, and high scale. Results indicated thatSMAs used in hiring are generally low in structure, which indicates a great deal of opportunity for improving the validity and reliability SMA by adding more structure to the procedure

Creating Silence: How Managerial Narcissism Decreases Employee Voice

Narcissism in organizations is becoming increasingly prevalent, as evidenced by the growing number of CEOs that seek acclaim and dominance, often at the expense of others (Chatterjee, 2017). Narcissism is defined as “individuals for whom enhancing the positivity of the self (specifically, to achieve status and esteem) is overwhelmingly important” (Campbell, 2004), and most often wields a detrimental influence on organizations. In the management literature, efficacy is defined as “a person’s estimate that a given behavior will lead to certain outcomes” (Bandura, 1977), and employees who hold a stronger sense of personal self-efficacy will display more active efforts to improve their organization (Bandura, 1977). Using a sample of 257 full-time employees, we explore the relationship between narcissism and efficacy in terms of promotive voice, which is focused on proactively pointing out opportunities for improvement (Liang, 2012). Additionally, we explore how this relationship is altered by voice setting, seeing as how the social setting in which voice occurs can affect managerial reactions to it (Isaakyan, 2018). Results confirm our hypothesis that employee voice behavior will be negatively correlated to managerial narcissism, and this relationship is mediated by efficacy, and moderated by voice setting

Anomaly Detection Methods to Improve Supply Chain Data Quality and Operations

Supply chain operations drive the planning, manufacture, and distribution of billions of semiconductors a year, spanning thousands of products across many supply chain configurations. The customizations span from wafer technology to die stacking and chip feature enablement. Data quality drives efficiency in these processes and anomalies in data can be very disruptive, and at times, consequential. Developing preventative measures that automate the detection of anomalies before they reach downstream execution systems would result in significant efficiency gain for the organization. The purpose of this research is to identify an effective, actionable, and computationally efficient approach to highlight anomalies in a sparse and highly variable supply chain data structure. This research highlights the application of ensemble unsupervised learning algorithms for anomaly detection on supply chain demand data. The outlier detection algorithms explored include Angle-Based Outlier Detection, Isolation Forest, Local Outlier Factor and K-Nearest Neighbors. The application of an ensemble technique on unconstrained forecast signal, which is traditionally a consistent demand line, demonstrated a dramatic decrease in false positives. The application of the ensemble technique to the sales-order netted demand forecast, a signal that is irregular in structure, the algorithm identifies true anomalous observations relative to historical observations across time. The research team concluded that assessing an outlier is not limited to the most recent forecast’s observations but must be considered in the context of historical demand patterns across time

Real-world experience of IL-17Ai drug survival in a large cohort of axial spondyloarthritis and psoriatic arthritis

Axial spondyloarthritis; Drug survival; Psoriatic arthritisEspondiloartritis axial; Supervivencia de los medicamentos; Artritis psoriásicaEspondiloartritis axial; Supervivència dels medicaments; Artritis psoriàsicaObjective The aim was to assess the use and drug survival of IL-17Ai in a real-world cohort of axial SpA (axSpA) and PsA patients. Methods Patients ever commenced on an IL-17Ai (secukinumab or ixekizumab) for axSpA or PsA at the Leeds Specialist Spondyloarthritis Service were identified. Demographics, IL-17Ai treatment length and reason for cessation were collected. Drug survival data were plotted as a Kaplan–Meier curve, with log rank test of median survival compared between axSpA and PsA. Cox regression analysis was performed to investigate the relationship between diagnosis and length of drug survival. Results In total, 228 patients (91 axSpA and 137 PsA) were exposed to IL-17Ai. Drug survival for all patients at 12 months was 69% (95% Confidence Interval (CI) 63, 75%) and at 24 months 60% (95% CI 54, 67%). In axSpA and PsA, drug survival at 12 months was 63% (CI 54, 74%) and 73% (CI 66, 81%), respectively, and at 24 months it was 53% (CI 44, 65%) and 65% (CI 57, 75%), respectively. Median survival did not differ significantly between both diseases (log rank test 0.65). There was no association between diagnosis and survival (hazard ratio 0.92, 95% CI 0.63, 1.33), including when adjusting for age, previous biologic DMARD usage and sex (hazard ratio 0.89, 95% CI 0.61, 1.13). Conclusion This is the first study, to our knowledge, to analyse and compare real-world IL-17Ai drug survival in patients with axSpA and PsA from a single centre. We demonstrate that there is no difference in IL-17Ai survival rates and no relationship between diagnosis and drug survival. These results contribute to the body of real-world evidence confirming the role of IL-17Ai in the management of axSpA and PsA

Effect of the subjective intensity of fatigue and interoception on perceptual regulation and performance during sustained physical activity

BACKGROUND: The subjective experience of fatigue impairs an individual’s ability to sustain physical endurance performance. However, precise understanding of the specific role perceived fatigue plays in the central regulation of performance remains unclear. Here, we examined whether the subjective intensity of a perceived state of fatigue, pre-induced through prior upper body activity, differentially impacted performance and altered perceived effort and affect experienced during a sustained, isometric contraction in lower body. We also explored whether (cardiac) interoception predicted the intensity of experienced perceptual and affective responses and moderated the relationships between constructs during physical activity. METHODS: Using a repeated-measures study design, thirty male participants completed three experimental conditions, with the intensity of a pre-induced state of fatigue manipulated to evoke moderate (MOD), severe (SEV) and minimal (control; CON) intensity of perceptions prior to performance of the sustained contraction. RESULTS: Performance of the sustained contraction was significantly impaired under a perceived state of fatigue, with reductions of 10% and 14% observed in the MOD and SEV conditions, respectively. Performance impairment was accompanied by greater perceived effort and more negative affective valence reported during the contraction. However, effects were limited to comparisons to CON, with no difference evident between the two experimental trials (i.e. MOD vs. SEV). Individuals’ awareness of their accuracy in judging resting heartbeats was shown to predict the subjective intensity of fatigue experienced during the endurance task. However, interoception did not moderate the relationships evident between fatigue and both perceived effort and affective valence. CONCLUSIONS: A perceived state of fatigue limits endurance performance, influencing both how effortful activity is perceived to be and the affective experience of activity. Though awareness of interoceptive representations of bodily states may be important to the subjective experience of fatigue, interoception does not modulate the relationships between perceived fatigue and other perceptual (i.e. effort) and affective constructs

Promotion of germination using hydroxamic acid inhibitors of 9-cis-epoxycarotenoid dioxygenase

Abscisic acid (ABA) inhibits seed germination and the regulation of ABA biosynthesis has a role in maintenance of seed dormancy. The key rate-limiting step in ABA biosynthesis is catalysed by 9-cis-epoxycarotenoid dioxygenase (NCED). Two hydroxamic acid inhibitors of carotenoid cleavage dioxygenase (CCD), D4 and D7, previously found to inhibit CCD and NCED in vitro, are shown to have the novel property of decreasing mean germination time of tomato (Solanum lycopersicum L.) seeds constitutively overexpressing LeNCED1. Post-germination, D4 exhibited no negative effects on tomato seedling growth in terms of height, dry weight and fresh weight. Tobacco (Nicotiana tabacum L.) seeds containing a tetracycline-inducible LeNCED1 transgene were used to show that germination could be negatively and positively controlled through the chemical induction of gene expression and the chemical inhibition of the NCED protein: application of tetracycline increased mean germination time and delayed hypocotyl emergence in a similar manner to that observed when exogenous ABA was applied and this was reversed by D4 when NCED expression was induced at intermediate levels. D4 also improved germination in lettuce (Lactuca sativa L.) seeds under thermoinhibitory temperatures and in tomato seeds imbibed in high osmolarity solutions of polyethylene glycol. D4 reduced ABA and dihydrophaseic acid accumulation in tomato seeds overexpressing LeNCED1 and reduced ABA accumulation in wild type tomato seeds imbibed on polyethylene glycol. The evidence supports a mode of action of D4 through NCED inhibition, and this molecule provides a lead compound for the design of NCED inhibitors with greater specificity and potency