Search results

In an era of increasing financial vulnerability, people are not saving enough to either fund their future pension benefits or having precautionary savings. The authors propose that pension knowledge makes people increase their probability of having voluntary pension and banking savings.

The authors use the social protection survey in Chile, a unique set of panel data for affiliates in 2006 and 2009. First, the authors use clustering algorithms to find naturally occurring groupings in the level of pension knowledge. Second, the authors run a probit regression model for explaining the probability of having a voluntary pension and banking savings, using as determinants the level of pension knowledge and several control variables that are usually explored in the literature.

The authors find two clusters of pension knowledge in the Chilean pension system. In addition, the authors find that there is a positive correlation between high pension knowledge and good financial decision-making, as these people have voluntary retirement and banking savings.

As people who spend time planning accumulate more wealth, it is important to develop public policies that promote the advantages to know better about the benefits of having voluntary savings for the long-term horizon. Conscientious people are also more likely to have voluntary savings.

Policy programs to increase to be responsible can have positive effects on society's welfare.

Up to the authors’ knowledge, this is the first study that connects clustering algorithms and pension knowledge.

En una era de creciente vulnerabilidad financiera, las personas no están ahorrando lo suficiente para financiar sus futuras pensiones ni para tener ahorros precautorios. Proponemos que el conocimiento de las pensiones hace que las personas aumenten su probabilidad de tener ahorros voluntarios para pensiones y bancarios.

Usamos la Encuesta de Protección Social en Chile, una base única de datos de panel para afiliados en 2006 y 2009. En primer lugar, usamos algoritmos de clustering para encontrar agrupaciones naturales en cuanto a nivel de conocimiento sobre pensiones. En segundo lugar, usamos un modelo de regresión probit para explicar la probabilidad de tener ahorros voluntarios para pensión y bancarios, utilizando como determinantes el nivel de conocimiento sobre pensiones y variables de control.

Encontramos dos grupos de conocimiento sobre pensiones en el sistema de pensiones chileno. Además, encontramos que existe una correlación positiva entre un alto conocimiento de las pensiones y tener ahorros voluntarios.

A medida que las personas dedican tiempo a planificar acumulan más riqueza, por lo que es importante desarrollar políticas públicas que promuevan las ventajas para conocer mejor los beneficios de contar con ahorros voluntarios para un horizonte de largo plazo.

Los programas de políticas para aumentar la responsabilidad pueden tener efectos positivos en el bienestar de la sociedad.

Este es el primer estudio que conecta algoritmos de agrupación en clústeres para el conocimiento de las pensiones y sus implicaciones en la toma de decisiones financieras.

This study aims to present the climate change effect on soil moisture regimes in Mexico in a global 1.5°C warming scenario.

The soil moisture regimes were determined using the Newhall simulation model with the database of mean monthly precipitation and temperature at a scale of 1: 250,000 for the current scenario and with the climate change scenarios associated with a mean global temperature increase of 1.5°C, considering two Representative Concentration Pathways, 4.5 and 8.5 W/m² and three general models of atmospheric circulation, namely, GFDL, HADGEM and MPI. The different vegetation types of the country were related to the soil moisture regimes for current conditions and for climate change.

According to the HADGEM and MPI models, almost the entire country is predicted to undergo a considerable increase in soil moisture deficit, and part of the areas of each moisture regime will shift to the next drier regime. The GFDL model also predicts this trend but at smaller proportions.

The changes in soil moisture at the regional scale that reveal the impacts of climate change and indicate where these changes will occur are important elements of the knowledge concerning the vulnerability of soils to climate change. New cartography is available in Mexico.

The purpose of this study is to present the evolution of academic research in venture capital (VC) research between 1990 and 2014.

The study analyzes the most influential journals in VC research by analyzing papers, which were published on the Web of Science database.

Results show a steady increasing rate of VC research during the past 25 years. The paper reports the 40 academic journals that permanently publish articles about VC research.

The main contribution of this work is to develop a general overview of the leading journals in VC research, which leads to the development of a future research agenda for bibliometric analysis, such as the review of the most productive and influential authors, universities and countries in VC research.

This paper aims to propose that the literature on customer engagement has emphasized the benefits of customer engagement to the firm and, to a large extent, ignored the customers’ perspective. By drawing upon co-creation and other literature, this paper attempts to alleviate this gap by proposing a strategic framework that aligns both the customer and firm perspectives in successfully creating engagement that generates value for both the customer and the bottom line.

A strategic framework is proposed that includes the necessary firm resources, data, process, timeline and goals for engagement, and captures customers’ motives, situational factors and preferred engagement styles.

The authors argue that sustainability of data-driven customer engagement requires a dynamic and iterative value generation process involving customers recognizing the value of engagement behaviours and firm’s ability to capture and passing value back to customers.

This paper proposes a dynamic strategic value-creation framework that comprehensively captures both the customer and firm perspectives to data-driven customer engagement.

The purpose of this paper is to examine the profile of entrepreneurs who failed in previous businesses, but despite their initial failure, decide to start or maintain another business. Based on the literature, re-entrepreneurs present some unique characteristics that allow them to overcome and learn from their previous ventures. Moreover, following the institutional dimensions literature, the paper shows that these dimensions should also affect the prevalence and type of re-entrepreneurial profiles.

Traditional ANOVA and logistic regression analysis were used as a way to test several hypotheses. Moreover, decision trees were implemented to provide further insights with respect to the characteristics of the relationships under study. Data from the Global Entrepreneurship Monitor Survey (Adult Population Survey 2016 for Chile) is the main source of information.

The results of this paper show that for Chilean entrepreneurs, there is a different profile between those who fail and abandon and those who decide to start a new business after failing. These differences show that post-failure entrepreneurs behave like angel investors, have better business skills and are more experienced. Additionally, they are more likely to share ownership of their new business.

The paper contributes to the literature with empirical results that validate that failure determines special characteristics in entrepreneurs through a methodology that is relatively new in the community. Also, the paper discusses how institutions (institutional theory) could play a role in this phenomenon. This can help decision makers and the academic community better understand the nature of this type of entrepreneur and thus provide insights for more focussed public policies and better-informed business decisions.

This study aims to evaluate the role of entrepreneurial ecosystems conditions (formal, informal and social capital) on different types of entrepreneurial re-entry at a global scale.

Given this phenomenon’s nature, this study builds a panel of data of 54 economies covering different (advanced and emerging) countries across the globe during the period 2004–2017 by mixing multiples sources of information (e.g. Global Entrepreneurship Monitor, the World Economic Forum, the World Bank and the International Monetary Fund). The statistical analysis consisted of the fixed-effect dynamic generalized method of moments (GMM) estimation for panel data.

Three empirical insights emerge from the study. First, the entrepreneurial ecosystem’s formal conditions are mainly configured to support high-growth entrepreneurship ignoring re-entrepreneurs. Consequently, the formal conditions’ contribution is very limited in emerging economies. Second, the analysis of informal conditions revealed social media’s critical contribution for legitimizing entrepreneurship and supporting those entrepreneurs who want to re-enter the domestic or international market after a business failure. Third, social networks built during previous business angels or entrepreneurial experiences or with other entrepreneurs also play a crucial role for re-entrepreneurs to overcome the weaknesses in the entrepreneurial ecosystems’ conditions.

The study contributes to two ongoing academic debates among entrepreneurship scholars. The first is related to how the entrepreneurial ecosystem supports entrepreneurial activity in different economic contexts. The second is related to the study of the contextual determinants of entrepreneurial re-entry after a business failure.

Although the financial markets are regulated by robust systems and rules that control their efficiency and try to protect investors from various manipulation schemes, markets still suffer from frequent attempts to mislead or misinform investors in order to generate illegal profits. The impetus to effectively and systematically address such schemes presents many challenges to academia, industry and relevant authorities. This paper aims to discuss these issues.

The paper describes a case study on fraud detection using data mining techniques that help analysts to identify possible instances of touting based on spam e‐mails. Different data mining techniques such as decision trees, neural networks and linear regression are shown to offer great potential for this emerging domain. The application of these techniques is demonstrated using data from the Pink Sheets market.

Results strongly suggest the cumulative effect of “stock touting” spam e‐mails is key to understanding the patterns of manipulations associated with touting e‐mail campaigns, and that data mining techniques can be used to facilitate fraud investigations of spam e‐mails.

The approach proposed and the paper's findings could be used retroactively to help the relevant authorities and organisations identify abnormal behaviours in the stock market. It could also be used proactively to warn analysts and stockbrokers of possible cases of market abuse.

This research studies the relationships between the cumulative volume of spam touts and a number of financial indicators using different supervised classification techniques. The paper aims to contribute to a better understanding of the market manipulation problem and provide part of a unified framework for the design and analysis of market manipulation systems.

This research presents an ontological model, to communicate the impact of dynamic preconditions for peri-urban communities. As such, this paper approaches perturbation communities as social-complex-adaptive-systems.

Previous assessment of dynamic preconditions have typically been based on top-down approaches. Through the lens of social-complex-adaptive and systemic design approaches (requiring a range of different disciplines), this work focuses on providing a broader view towards periurban research. The methodological approach involved academic literature, fieldwork observations, in-depth discussions with community, government, experts and research groups, focusing on a region called “Xochimilco” on the outskirts of Mexico City, a unique pre-Hispanic, Aztec ecosystem. This evolving man made agricultural/ecological structure of island plots, still provides environmental services to Mexico City. This region provides the basis of the research and subsequent ontological model. Ontology, in this instance, refers to the nature of being within a range of constraining dynamic forces relating to resilient behaviors of the current Xochimilco perturbation ecosystem.

Xochimilco can be considered as a longitudinal phenomenon that contributed to the understanding of observable resilient and precondition elements between the past and present of a living complex-adaptive-system.

The research has provided a better understanding of community resilience through preconditions, contributing towards preparation of environmental change and future urbanization. To this end, the research focused on visualizing key dynamics elements for communities attempting to absorb new urban conditions (being continuously pushed into it).

The outcomes of this research have provided specific systemic, bottom up approaches with ontological modeling to assist with visualizing and understanding intangible dynamic conditions that impact high complex areas of perturbation regions.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

It has become evident that students have diverse preferred learning styles and effective instructors must design and deliver courses to meet the needs of those students. This study investigates the four physiological learning styles of visual, aural, read‐write and kinesthetic as they apply to online education. Findings suggest that online students are more likely to have stronger visual and read‐write learning styles. Further, read‐write learners and students that were strong across all four learning styles were likely to evaluate course effectiveness lower than other students while aural/readwrite learners and students that were not strong on any learning style were more likely to evaluate course effectiveness higher than other students.