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The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.

The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.

The purpose of this paper was to examine the factors that influence millennials’ intentions to adopt plug-in hybrid electric vehicles (PHEVs) in Gauteng, South Africa. This study was based on the theory of consumption values (TCV), using structured questionnaires and convenience sampling. Data was collected from 504 respondents in Gau-teng, South Africa. A multiple regression analysis was performed to test the proposed hypotheses. The findings revea-led a significant relationship between social, emotional and conditional values and millennials’ behavioural intentions to adopt PHEVs and demonstrated a non-significant relationship between functional and epistemic values and beha-vioural intentions. Online data collection was used owing to COVID-19 pandemic restrictions, which limited the researcher’s abilities to explain items in the questionnaire. Also, the study focused only on millennials residing in Gauteng, which does not accurately represent the behavioural intentions of the entire country. Nevertheless, this study offers useful information to environmentally friendly car manufacturers, car dealerships and the government, for developing strategies aimed at encouraging the adoption of PHEVs among South African millennial consumers.

2021AbstractSchool dropout is a serious problem worldwide, and contributes to a great deal of poverty and misery. People who have not finished school obviously suffer the consequences, but these extend to all of society since they become a burden due to lack of education and skills for the workplace. Much like poverty, school dropout is complex and multidimensional. Hence, early warning systems that predict which children are at risk of dropping out of school are of the utmost importance, and furthermore, the interventions to rescue these children must be bespoke, i.e., tailored to the specific situation of each child. Much work has been done using traditional methods such as attendance thresholds and logistic regression. However, school dropout prediction by means of applying machine learning is relatively new. In addition, an application that has worked in one country does not necessarily work in another, since the available data sets are different. Therefore, the following question arises: does machine learning enable a more accurate early warning of school dropout specifically in Chile? In this paper we answer this question, testing and comparing machine learning predictive models with a traditional logistic regression, using public databases from the Chilean Ministry of Education. In addition, we offer some practical recommendations for other researchers and policy makers who endeavour to implement practical working early warning systems for school dropout.

Governance mechanisms help manage, direct, and control people, resources, and the interests of those involved in a firm. In family firms, understanding the use of governance mechanisms is particularly important given their rela-tionship with the sustainability of the family and the business. Even though we know a great deal about family busi-ness governance in North America and Europe, we still know very little regarding the use of governance mechanisms in small and medium (SME) family firms in Latin America, nor do we know whether the use of governance mech-anisms impacts financial performance. To address these gaps, this paper presents the results of a survey completed by 2287 representatives of family business SMEs from 24 Latin American countries. Participants indicated the like-lihood of their using different governance mechanisms and responded to questions concerning their businesses. Our results indicate that the small and medium Latin American family firms in our study were not very likely to use for-mal business and family governance mechanisms, however, the use of formal business governance mechanisms was related to financial performance. The implications of these results for research and practice are discussed.

This paper aims to provide a better understanding of conditions that influence the gap between positive attitude and intention towards organic food products and actual behaviour regarding these products. Thus, we propose an extended version of the Theory of Planned Behaviour (TPB) to explain parts of this gap and we highlight the crucial role played by consumers’ involvement as a moderator. A structural equation modelling was performed, and the sta-tistical analysis of a sample of 1327 French consumers supports our organic food products buying behaviour model. The results showed that the difference between the means of actual behaviour was highly different between low- and high-involvement consumers. More specifically, high-involvement consumers express more positive attitudes towards buying organic food products, perceive higher subjective norms and behavioural control, they have higher behavioural intention, and buy organic food products more frequently. Additionally, the results indicated that, com-pared to low-involvement consumers, high-involvement consumers regard organic food products as more attractive, healthier, tastier, and with higher value. We proposed some marketing strategies to help managers to better promote the organic food products market and, in turn, increase their revenues. For example, marketers therefore have a vested interest in increasing consumer involvement, and, among other things, they can do so by educating them (i.e., high-lighting the benefits of consuming organic foods). Moreover, since high-involvement customers have positive atti-tude-intention and behaviour, they can be allies for marketers through their influence (social norms). Thus, we suggest the use of digital influencers to endorse organic food.

Even though, in the past, competition depended on the factors of production possessed, today it depends on the production of value-added goods, their export, and finally, branding of the country today. Since the late 1990s, the brand value of countries has been an important concept that has been studied. Current academic literature is deprived of weighting sub dimensions of country brand strength index and compare index values by years. Having an import-ant role in academic literature, Fetscherin (2010) identified five dimensions of the country brand strength index as export, tourism, foreign direct investment, migration and governance, but not giving any weighting to sub dimensions. In order to contribute to current country brand index literature, sub-dimensions of the index are weighted with the help of the analytical hierarchy process (AHP) method, comparing 2010 and 2015. Therefore, the innovation of this paper is its weighting method and the comparison of index values by years. The Country Brand Strength Index (CBSI) is calculated for G7 countries and Turkey using the survey based AHP method, consisting of 5 different indi-cators: exports, foreign direct investments, tourism, immigration, and governance. According to the results, it is deter-mined that “exports” has the most important weight among those indicators with Canada leading the group with the best index value in 2010 and 2015. The aim of this study, which was conducted with limited resources, is to shed light on studies to be carried out in the future in order to establish a strong country brand and increase country competi-tiveness in the international markets. In this respect, repetition of this research as regards to geographical and regional variations and performing qualitative and quantitative studies, incorporating different dimensions in the index such as culture, science and technology, will strengthen the academic literature in this field.

This paper examines the relationship between intercultural competencies and performance in “international locals”, defined as employees who, albeit not formally appointed to any international position, are regularly exposed to different types of intercultural interactions. From a sample of 258 employees of a multinational company, we selected a sub-group of 94 international locals, and examined the effect of ethnocentrism (ET), cultural intelligence (CQ) and emotional intelligence (EQ) on their performance, utilizing multiple regression analysis. The results show that the higher levels of CQ and/or EQ, the better the performance of international locals. Evidence also indicates that EQ interacts with CQ, triggering or enhancing the effect on performance. Finally, ET does not have any effect on the performance of international locals. The research implies that EQ and CQ are essential for performance in international business situations, and that international locals, who are continually exposed to regular intercultural interactions, need to focus on developing these two competencies. By assessing and helping international locals to develop higher EQ and CQ, organizations can ensure that they have enthusiastic and perseverant employees, who enjoy intercultural interactions, and can contribute to develop competitive advantages and capabilities.