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The purpose of this paper is to demonstrate that common method variance, specifically single-source bias, threatens the validity of a university-created student assessment of instructor instrument, suggesting that decisions made from these assessments are inherently flawed or skewed. Single-source bias leads to generalizations about assessments that might influence the ability of raters to separate multiple behaviors of an instructor.

Exploratory factor analysis, nested confirmatory factor analysis and within-and-between analysis are used to assess a university-developed, proprietary student assessment of instructor instrument to determine whether a hypothesized factor structure is identifiable. The instrument was developed over a three-year period by a university-mandated committee.

Findings suggest that common method variance, specifically single-source bias, resulted in the inability to identify hypothesized constructs statistically. Additional information is needed to identify valid instruments and an effective collection method for assessment.

Institutions are not guaranteed valid or useful instruments even if they invest significant time and resources to produce one. Without accurate instrumentation, there is insufficient information to assess constructs for teaching excellence. More valid measurement criteria can result from using multiple methods, altering collection times and educating students to distinguish multiple traits and behaviors of individual instructors more accurately.

This paper documents the three-year development of a university-wide student assessment of instructor instrument and carries development through to examining the psychometric properties and appropriateness of using this instrument to evaluate instructors.

Purpose – The purpose of this research is to design and build a wild animal pest repellent device with combination of passive infrared (PIR) sensor and ultrasonic signal based on microcontroller as system controller. The PIR sensor is used to detect the presence of wild animal objects and ultrasonic signals to interfere with the hearing.

Design/Methodology/Approach – The design of the system is built based on microcontroller as the system controller. The system as a whole includes hardware and software. The design of hardware consists of the system design on the transmitter side and the system design on the receiver side, while the software in the of system are algorithms using C language programming.

Findings – The resulting repellent device can detect animals approaching up to a distance of 5 m and may interfere with its hearing with a 40 kHz ultrasonic frequency up to a distance of 20 m. The system also uses remote monitoring devices using 433 MHz radio frequency up to a distance of 60 m.

Research Limitations/Implications – Each animal has different hearing frequencies, as well as some wild animals, but the hearing frequencies of wild animals are generally at ultrasonic frequencies. The frequency of animal hearing may vary from audio frequency to ultrasonic frequency, so ultrasonic wave emission testing with varying frequencies is required.

Practical Implications – This research combines systems on transmitters and receivers, with real-time monitoring of wild animal positions, and it can be possible to monitor the position of more detailed animals by installing more types of sensors as well as increasing the number of sensors.

Originality/Value – This paper may provide additional insight into the hearing frequencies of animals and may also serve as comparable papers for similar studies.

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.

Background: Most children in low-income countries complete their elementary education with low competency in essential reading, writing, and arithmetic skills. Besides, about 250 million students are not learning the basics, most of whom have spent at least four years in school, and this failure is coined the global learning crisis. This study aimed to examine educational quality management practices perceived by secondary school teachers.

Methods: The study employed a multilevel mixed-method design. Employing a simple random sampling technique, the researcher selected 251 teachers from 10 secondary schools in the research regions. He collected data through a researcher-designed questionnaire, school standards, and student achievement records from November 2018 to March 2019. He analyzed data from a questionnaire using frequency, percentage, mean, Pearson correlation, and exploratory factor analysis. The document review concerning quality management was analyzed using content analysis to triangulate the quantitative findings.

Results: At the school level, the study revealed the impracticality of laboratories. Besides, incompetent and unmotivated teachers and students ran the education business from the input side. At the same level, principals' management practices on staff development and encouraging parents to support their schools were low. The principals' management practices in the teaching-learning process were also undesirable at the classroom level. Overall, the study revealed incredibly insufficient input, process, and output management in the study context.

Conclusion: Hence, the study concluded that it is difficult to achieve the very objectives of producing creative, critical, and problem-solving individuals through this type of educational provision and its management. Due to this, it is not easy to achieve quality education for all goals.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

Since public sector organizations provide services to citizens but struggle with poor perceptions of their functioning, it is valuable to examine how their online responses to complaints on social media could impact their reputation. Yet, surprisingly little is known about effects of public organizations' webcare. Therefore, this study assesses the impact of the webcare's tone, response strategy and user's involvement on participants’ continuance intention and perceptions of reputation.

Two experimental studies (Study 1: N = 424; Study 2: N = 203) with an interval of one week were carried out to assess the effects of singular and repeated exposure to webcare by a Dutch public transport organization on the participants' continuance intention and perceived organizational reputation. Study 1 examined the effects of the webcare's tone (corporate vs conversational human voice (CHV)) and response strategy (accommodative vs defensive); Study 2 contained tone of voice and user's involvement (observer vs complainer). The effects of repeated exposure to the webcare's tone were also examined.

The results indicate that perceptions of CHV in webcare contribute to webcare as reputation management tool, since it leads to immediate higher reputation scores that also remain stable after repeated exposure. Furthermore, people's continuance intention increased after repeated exposure to webcare responses that were perceived as CHV, thus a natural and engaging communication style, indicating this is an effective strategy for customer care as well. No substantial impact was found for response strategy and user's involvement in the complaint handling.

The novelty of this study is that the authors assess the effects of the webcare's tone combined with response strategy and user's involvement in a public sector context with a sector-specific conceptualization of reputation and continuance intention measured after singular and repeated exposure to webcare.

This paper aims to study the impact of human resource heterogeneity on firms’ cash-holding policies.

The authors construct a proxy for human resource heterogeneity using the dissimilarity in employees’ skill structure between the firm and its peers in the same industry.

The authors report evidence that firms with heterogeneous human resources hold more cash than other firms. This effect is more pronounced in labor-intensive firms and firms more susceptible to hold-up by employees, i.e. firms located in regions with more labor disputes and firms surrounded by more external employment opportunities. In addition, the authors demonstrate that high cash holdings triggered by human resource heterogeneity reduce the scale and efficiency of firms’ capital investment.

This study highlights the role of human resource heterogeneity in determining firms’ cash policies. This paper adds to the understanding of labor adjustment costs within the firm and provides insights into firms’ cash-holding decisions.