Veronica Vitali, Claudia Bazzani, Annamaria Gimigliano, Marco Cristani, Diego Begalli and Gloria Menegaz
This study proposes a literature review and, based on the findings, the authors develop a conceptual framework, attempting to explain how technology may influence visitor behavior…
Abstract
Purpose
This study proposes a literature review and, based on the findings, the authors develop a conceptual framework, attempting to explain how technology may influence visitor behavior and eventually trade show performance.
Design/methodology/approach
The present research explores the role of visitors in the trade show context. The analysis specifically focuses on the variables that influence visitors’ participation at business-to-business trade shows and how their satisfaction and perception can be related to exhibition performance. The authors also take into consideration technological trends that prior to COVID-19 pandemics were slowly emerging in the trade show industry.
Findings
The findings highlight a continuity between pre-, at and postexhibition phases. Visitors’ behavior represents a signal of how a trade show is perceived as postexhibition purchases and next visit emerge as signals of an exhibition evaluation in relation to visitors’ perception. Besides being urgent tools for the continuity of the sector due to the pandemics, emerging technological trends can be key elements in understanding visitors’ behavior and in boosting their interest and loyalty toward trade shows.
Originality/value
The paper proposes a conceptual model including top notch and innovative technological trends to improve the understandment of visitors’ behavior. Both practitioners in companies and academics might find the study useful, given the digital uplift generated by the pandemics.
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Alberto Bayo-Moriones and Alejandro Bello-Pindado
The purpose of this paper is to analyse the impact on manufacturing performance of human resource management (HRM) practices across two job levels within manufacturing firms in…
Abstract
Purpose
The purpose of this paper is to analyse the impact on manufacturing performance of human resource management (HRM) practices across two job levels within manufacturing firms in Argentina and Uruguay: that of line managers and frontline workers. HRM practices are categorised into three bundles defined by the AMO theoretical framework: ability, motivation and opportunity.
Design/methodology/approach
The article uses data from a survey to 301 manufacturing plants in Uruguay and Argentina. Given the characteristics of the dependent variable, linear regression models have been estimated in order to test the hypotheses.
Findings
The results show that the ability and opportunity bundles for line managers are positively associated with manufacturing performance. However, only the motivation bundle affects manufacturing performance for frontline workers.
Research limitations/implications
The main limitations are the use of cross-sectional data, the focus on two specific countries and the analysis of two employee categories that are not completely homogenous. The paper extends the contingency perspective in HRM by examining the relevance of job level as a contingent factor in the HRM-performance relationship in the manufacturing industry.
Practical implications
The results suggest that manufacturing companies should target HR investments more towards line managers than to frontline employees. More specifically, they should concentrate efforts on the ability and opportunity bundles.
Originality/value
The article contributes to the very limited empirical evidence on the impact of HRM differentiation on firm performance by analysing sub-dimensions in a context not previously analysed.
Objetivo
El objetivo del trabajo es analizar el impacto sobre los resultados de manufactura de la aplicación de prácticas de Dirección de Recursos Humanos en dos niveles de empleados dentro de las empresas industriales argentinas y uruguayas: los supervisores y los operarios de producción. Las prácticas de DRH son clasificadas en tres paquetes de acuerdo con el marco definido por el modelo AMO: capacidad, motivación y oportunidad.
Diseño/metodología/enfoque
El artículo utiliza datos procedentes de una encuesta realizada a 301 plantas industriales en Uruguay y Argentina. Dadas las características de la variable dependiente, se estiman modelos de regresión lineal para contrastar las hipótesis.
Hallazgos
Nuestros resultados muestran que los paquetes de prácticas orientados a la capacidad y la oportunidad para los supervisores están asociados positivamente con los resultados de manufactura. Sin embargo, solo el paquete de prácticas orientado a la motivación afecta a los resultados de manufactura para los operarios de producción.
Limitaciones/implicaciones de investigación
Las principales limitaciones son el uso de datos transversales, el enfoque en dos países concretos y el análisis de dos ocupaciones que no son completamente homogéneas. Este trabajo extiende la perspectiva contingente analizando la importancia del nivel jerárquico del puesto como un factor de contingencia en la relación DRH-resultados en la industria manufacturera.
Implicaciones prácticas
Los resultados sugieren que las empresas industriales deberían dirigir sus inversiones en DRH más hacia los supervisores que hacia los operarios. Más concretamente, deberían concentrar sus esfuerzos en los paquetes de capacidad y oportunidad.
Originalidad/valor
El artículo contribuye a la escasa evidencia empírica sobre el impacto de la diferenciación de la DRH sobre los resultados de manufactura analizando subdimensiones en un contexto no estudiado con anterioridad.
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Deepjyoti Kalita and Dipen Deka
Systematic organization of domain knowledge has many advantages in archiving, sharing and retrieval of information. Ontologies provide a cushion for such practices in the semantic…
Abstract
Purpose
Systematic organization of domain knowledge has many advantages in archiving, sharing and retrieval of information. Ontologies provide a cushion for such practices in the semantic Web environment. This study aims to develop an ontology that can preserve the knowledge base of traditional dance practices.
Design/methodology/approach
It is hypothesized that an ontology-based approach for the chosen domain might boost collaborative research prospects in the domain. A systematic methodology was developed for modeling the ontology based on the analytico-synthetic rule of library classification. Protégé 5.2 was used as an editor for the ontology using the Web ontology language combined with description logic axioms. Ontology was later implemented in a local GraphDB repository to run queries over it.
Findings
The developed ontology on traditional dances (OTD) was tested using the dances of the Rabha tribes of North East India. Rabha tribes are from an indigenous mongoloid community and have a robust presence in Southeast Asian countries, such as Myanmar, Thailand, Bangladesh, Bhutan and Nepal. The result from HermiT reasoner found the presence of no logical inconsistency in the ontology, while the OOPS! pitfall checker tool reported no major internal inconsistency. The induced knowledge base of traditional dances of the Rabha’s in the developed OTD was further validated based on some competency questions.
Research limitations/implications
In the growing trend of globalization, preservation of the cultural knowledge base of human societies is an important issue. Traditional dances reflect a strong base of the cultural heritage of human societies as they are closely related to the lifestyle, habitat, religious practices and festivals of a specific community.
Originality/value
The current study is exclusively designed, keeping in mind the variables of traditional dance domain based on a survey of the user- and domain-specific needs. The ontology finds probable uses in traditional knowledge information systems, lifestyle-based e-commerce sites and e-learning platforms.
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Sheryl Brahnam, Loris Nanni, Shannon McMurtrey, Alessandra Lumini, Rick Brattin, Melinda Slack and Tonya Barrier
Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex…
Abstract
Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.