The importance of interaction between Operations Management (OM) and Human Behavior has been recently re-addressed. This paper introduced the Reasoned Action Theory suggested by Froehle and Roth (2004) to analyze Operational Capabilities exploring the suitability of this model in the context of OM. It also seeks to discuss the behavioral aspects of operational capabilities from the perspective of organizational routines. This theory was operationalized using Fishbein and Ajzen (F/A) behavioral model and a multi-case strategy was employed to analyze the Continuous Improvement (CI) capability. The results posit that the model explains partially the CI behavior in an operational context and some contingency variables might influence the general relations among the variables involved in the F/A model. Thus intention might not be the determinant variable of behavior in this context. Submitted 22.12.2011. Approved 16.08.2012 Evaluated in double blind review Scientific Editor: André Lucirton Costa ABSTRACTResumo A importância da interação entre as áreas de operações e gestão de pessoas tem sido recentemente ressaltada. Este trabalho adotou a teoria da Ação Racional sugerida por Froelhe and Roth (2004) para analisar as competências operacionais, objetivando explorar a adequação deste modelo no contexto de gestão de operações, e o aspecto comportamental das capacidades operacionais sob a perspectiva de rotinas organizacionais. A teoria foi operacionalizada utilizando o modelo comportamental de Fishbein e Ajzen (F/A) e analisada a competência de Melhoria Contínua. Metodologicamente, foi empregada estratégia multicasos com casos representativos. Os resultados indicam que este modelo explica parcialmente o comportamento da Melhoria Contínua e algumas variáveis contingenciais podem influenciar a relação geral entre as variáveis envolvidas no modelo F/A, assim, intenção pode não ser a variável determinante do comportamento nesse contexto. Palavras-chave Comportamento em operações, melhoria contínua, capability operacional, modelo comportamental de Fishbein e Ajzen, rotinas organizacionais. Froelhe and Roth (2004) Resumen La importancia de la interacción entre las áreas de gestión de operaciones y gestión de personas ha sido resaltada recientemente por diversos autore. Este trabajo adoptó la teoría de la Acción Racional sugerida por
Fragmentation of scientific knowledge in administration: a critical analysis Fragmentación del conocimiento científico en administración: un análisis crítico RESUMOEste estudo objetiva retomar a discussão da fragmentação da ciência, explorar como esse fenômeno manifesta-se em Administração e quais suas implicações para o avanço do conhecimento científico. Para tanto, utilizou-se a interface entre os campos de Operações e Recursos Humanos como caso ilustrativo. A metodologia adotada foi o estudo bibliométrico por meio de um mapeamento estruturado da produção científica dessas duas grandes áreas. Os resultados indicaram que ambas evoluíram de modo independente, com pouca sinergia entre os autores dos diferentes campos. Foram evidenciados fatores que ressaltam a fragmentação (visões epistemológicas, preferências metodológicas e distanciamento entre os autores). Finalmente, buscou-se provocar a reflexão quanto às oportunidades oferecidas pelo intercâmbio entre áreas para a evolução do conhecimento. PALAVRAS-CHAVE
IntroductionThe basis of support is understanding. In machine learning, understanding happens through assimilated knowledge and is centered on six pillars: big data, data volume, value, variety, velocity, and veracity. This study analyzes school attendance problems (SAP), which encompasses its legal statutes, school codes, students’ attendance behaviors, and interventions in a school environment. The support pillars include attention to the physical classroom, school climate, and personal underlying factors impeding engagement, from which socio-emotional factors are often the primary drivers.MethodsThis study asked the following research question: What can we learn about specific underlying factors of absenteeism using machine learning approaches? Data were retrieved from one school system available through the proprietary Building Dreams (BD) platform, owned by the Fight for Life Foundation (FFLF), whose mission is to support youth in underserved communities. The BD platform, licensed to K-12 schools, collects student-level data reported by educators on core values associated with in-class participation (a reported—negative or positive—behavior relative to the core values) based on Social–Emotional Learning (SEL) principles. We used a multi-phased approach leveraging several machine learning techniques (clustering, qualitative analysis, classification, and refinement of supervised and unsupervised learning). Unsupervised technique was employed to explore strong boundaries separating students using unlabeled data.ResultsFrom over 20,000 recorded behaviors, we were able to train a classifier with 90.2% accuracy and uncovered a major underlying factor directly affecting absenteeism: the importance of peer relationships. This is an important finding and provides data-driven support for the fundamental idea that peer relationships are a critical factor affecting absenteeism.DiscussionThe reported results provide a clear evidence that implementing socio-emotional learning components within a curriculum can improve absenteeism by targeting a root cause. Such knowledge can drive impactful policy and programming changes necessary for supporting the youth in communities overwhelmed with adversities.
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